Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f1bcc961-6e23-4af0-80a5-f86f72072b33",
   "metadata": {},
   "source": [
    "# Heart attack prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "974c4e5f-37ed-4079-9621-77d0ebc463b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba1859bb-d05f-43c5-9a37-888aef40ddcf",
   "metadata": {},
   "source": [
    "## Goal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13088755-97f4-44b6-9f19-019b3265c605",
   "metadata": {},
   "source": [
    "#### Current Cost"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d22ad543-e469-42f9-8cdc-5b04235f5dd8",
   "metadata": {},
   "source": [
    "The goal of this project is to reduce the costs associated to heart attacks in the national healthcare system by a 20%. The current cost per person is:\n",
    "\n",
    "$$ Current cost = (1-HA) * (CostNHA) + HA * (CostHA) = 4709.3€ $$\n",
    "\n",
    "$$ Current cost = 0.905799 * 0 + 0.094186 * 50000 = 4709.3€ $$\n",
    "\n",
    "HA = Heart attack probability = 0.094186\n",
    "\n",
    "CostHA = Cost of one person having a heart attack = 50000\n",
    "\n",
    "CostNHA = Cost of one person not having a heart attack = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c40907c9-928c-438d-86f9-388bece018f9",
   "metadata": {},
   "source": [
    "#### After plan cost"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d647fb76-d5e4-4735-a513-dc54e2bdb95d",
   "metadata": {},
   "source": [
    "The cost per person after applying this program has several branches:\n",
    "* People that won't have a HA but were diagnosed to have one and accept to follow the plan -> Cost: 1.000€\n",
    "$$ C1 = FPP * 1.000€ $$\n",
    "\n",
    "* People that will have a heart attack but were diagnosed not to have one -> Cost: 50.000€\n",
    "$$ C2 = FNP * 50.000€ $$\n",
    "\n",
    "* People that will have a heart attack, are diagnosed to have one and decide not to take the plan -> Cost: 50.000€\n",
    "$$ C3 = TPP * (1- P(takesplan)) * 50.000€ $$\n",
    "\n",
    "* People that will have a heart attack, are diagnosed to have one and decide to take the plan, and they don't adhere to it -> Cost: 51.000€\n",
    "$$ C4 = TPP * P(takesplan) * (1-P(adheresplan)) * 51.000€ $$\n",
    "\n",
    "* People that will have a heart attack, are diagnosed to have one and decide to take the plan, they adhere to it, and it doesn't work -> Cost: 51.000€\n",
    "$$ C5 = TPP * P(takesplan) * P(adheresplan) * (1-P(planworks)) * 51.000€ $$\n",
    "\n",
    "* People that will have a heart attack, are diagnosed to have one and decide to take the plan, they adhere to it, and it works -> Cost: 1.000€\n",
    "$$ C6 = TPP * P(takesplan) * P(adheresplan) * P(planworks) * 1.000€ $$\n",
    "\n",
    "FPP = False Positive Percentage = TN / Total Cases\n",
    "\n",
    "FNP = False Negative Percentage = FN / Total Cases\n",
    "\n",
    "TPP = True Positive Percentage = TP / Total Cases\n",
    "\n",
    "P(takesplan) = Probability that someone takes the plan when is offered to him/her = 0.85\n",
    "\n",
    "P(adheresplan) = Probability that someone adheres to the plan = Unknown\n",
    "\n",
    "P(planworks) = Probability that the plan works = 0.75"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "083d6480-a429-4017-8296-b52586b0add3",
   "metadata": {},
   "source": [
    "#### Formula\n",
    "We need to do a cost analysis to check the minimum percentage of adherence that we need in order to reduce the cost by 20%, that means, we need to get the value of P(adheresplan) so that the cost is equal than 80% of the current cost.\n",
    "\n",
    "$$ Currentcost * 0.8 = Newcost $$\n",
    "$$ Currentcost * 0.8 - (c1 + c2 + c3) = c4 + c5 + c6 $$\n",
    "\n",
    "$$ (Currentcost * 0.8 - (c1 + c2 + c3)) / (TPP*P(takesplan)) = $$\n",
    "$$ =((1-P(adheresplan))*51000) + (P(adheresplan)*(1-(P(planworks))*51000) + (P(adheresplan)*P(planworks)*1000) = $$\n",
    "$$ = 51000-51000*P(adheresplan) + P(adheresplan)*(1-P(planworks))*51000) + (P(adheresplan)*P(planworks)*1000) $$\n",
    "\n",
    "$$ ((Currentcost * 0.8 - (c1 + c2 + c3)) / (TPP*P(takesplan)) - 51000)/ P(adheresplan) = $$\n",
    "$$ = -51000 + (1-P(planworks))*51000) + P(planworks)*1000) $$\n",
    "\n",
    "$$ P(adheresplan) = $$\n",
    "$$ = ((Currentcost * 0.8 - (c1 + c2 + c3)) / (TPP*P(takesplan)) - 51000)/ (-51000 + (1-P(planworks))*51000) + P(planworks)*1000)) = $$\n",
    "$$ = ((Currentcost * 0.8 - (c1 + c2 + c3)) / (TPP*P(takesplan)) - 51000)/ (-P(planworks))*51000) + P(planworks)*1000))$$\n",
    "$$ = ((Currentcost * 0.8 - (c1 + c2 + c3)) / (TPP*P(takesplan)) - 51000)/ (-P(planworks))*50000)))$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1ef98e9f-4140-4fe3-a769-c513163f2927",
   "metadata": {},
   "outputs": [],
   "source": [
    "def minimum_adheresplan_prob(total_cases, heart_attacks, fpp, fnp, tpp, p_takesplan=0.85, p_planworks=0.75):\n",
    "    ha = heart_attacks/total_cases\n",
    "    nha = 1-ha\n",
    "    \n",
    "    current_cost = ha*50000 + nha*0\n",
    "    target_new_cost = 0.8 * current_cost\n",
    "    \n",
    "    #print(f\"We need a to reduce the cost per person to {target_new_cost}€\\n\")\n",
    "    \n",
    "    c1 = fpp * 1000\n",
    "    c2 = fnp * 50000\n",
    "    c3 = tpp * (1-p_takesplan) * 50000\n",
    "    \n",
    "    p_adheresplan = (((target_new_cost - (c1+c2+c3)) / (tpp*p_takesplan)) - 51000) / (-p_planworks*50000)\n",
    "    \n",
    "    #print(f\"The minimum adherence rate plan to reduce the cost by 20% is {p_adheresplan}\\n\")\n",
    "    \n",
    "    return p_adheresplan #if p_adheresplan > 0 and p_adheresplan < 1 else None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6a3f0aa-3c00-4cb9-9e2a-5a663c270e2d",
   "metadata": {},
   "source": [
    "## Load the dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "feb33a13-b8c2-49a5-aed7-5f3e239c4cc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"heart_disease.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "880cff58",
   "metadata": {},
   "source": [
    "### Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "391493d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 253680 entries, 0 to 253679\n",
      "Data columns (total 20 columns):\n",
      " #   Column             Non-Null Count   Dtype  \n",
      "---  ------             --------------   -----  \n",
      " 0   HeartAttack        253676 non-null  float64\n",
      " 1   HighBP             253679 non-null  float64\n",
      " 2   HighChol           253679 non-null  float64\n",
      " 3   CholCheck          253679 non-null  float64\n",
      " 4   BMI                253679 non-null  float64\n",
      " 5   Smoker             253679 non-null  float64\n",
      " 6   Stroke             253679 non-null  float64\n",
      " 7   Diabetes           253680 non-null  float64\n",
      " 8   PhysActivity       253680 non-null  float64\n",
      " 9   Fruits             253679 non-null  float64\n",
      " 10  Veggies            253679 non-null  float64\n",
      " 11  HvyAlcoholConsump  253679 non-null  float64\n",
      " 12  GenHlth            253680 non-null  float64\n",
      " 13  MentHlth           253679 non-null  float64\n",
      " 14  PhysHlth           253680 non-null  float64\n",
      " 15  DiffWalk           253680 non-null  float64\n",
      " 16  Sex                253680 non-null  float64\n",
      " 17  Age                253670 non-null  float64\n",
      " 18  Education          253668 non-null  float64\n",
      " 19  Income             253680 non-null  float64\n",
      "dtypes: float64(20)\n",
      "memory usage: 38.7 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6482834c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    229783\n",
       "1.0     23893\n",
       "Name: HeartAttack, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.HeartAttack.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7a63ce58",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"HeartAttack\", data=df, palette=\"bwr\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c74c9643",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QYGdaWlo0adIkud1u7dmzR6tWrdLSpUu1fPnybvhkAABAXxTek28+ZcoUTZky5bzbgsGgVq5cqYULF+rhhx+WJL3++uuKj4/Xm2++qdmzZ8vv92vt2rV64403NHHiREnS+vXrlZCQoK1btyotLU0HDhxQeXm5ampqNGrUKEnSmjVrlJKSooMHDyoxMVEVFRXav3+/Ghoa5Ha7JUnLli3TzJkztXjxYkVHR2vDhg365JNPtG7dOjmdTiUlJenDDz/U8uXLlZ+fL4fDcQU+MQAA0Jv12nusDh06pKamJqWmptrrnE6nxo0bpx07dkiSamtr1d7eHpJxu91KSkqyMzt37pRlWXapkqTRo0fLsqyQTFJSkl2qJCktLU2BQEC1tbV2Zty4cXI6nSGZI0eO6I9//OMFzyMQCKilpSXkBQAArk69tlg1NTVJkuLj40PWx8fH29uampoUERGhmJiYi2bi4uI6HT8uLi4kc+77xMTEKCIi4qKZs8tnM+dTXFxs39tlWZYSEhIufuIAAKDP6rXF6qxzL7EFg8HPvOx2buZ8eROZszeuX2w8CxYskN/vt18NDQ0XHTsAAOi7em2xcrlckjrPBjU3N9szRS6XS21tbfL5fBfNHD16tNPxjx07FpI59318Pp/a29svmmlubpbUeVbtbzmdTkVHR4e8AADA1anXFqshQ4bI5XKpsrLSXtfW1qaqqiqNGTNGkpScnKx+/fqFZBobG1VXV2dnUlJS5Pf7tXv3bjuza9cu+f3+kExdXZ0aGxvtTEVFhZxOp5KTk+3M9u3bQx7BUFFRIbfbrcGDB5v/AAAAQJ/To8Xq1KlT8nq98nq9kv56w7rX61V9fb0cDofy8vJUVFSksrIy1dXVaebMmbruuuuUmZkpSbIsS1lZWSooKNC2bdu0b98+PfHEExoxYoT9K8Fhw4Zp8uTJys7OVk1NjWpqapSdna309HQlJiZKklJTUzV8+HB5PB7t27dP27ZtU2FhobKzs+0ZpszMTDmdTs2cOVN1dXUqKytTUVERvwgEAAC2Hn3cwt69e3X//ffby/n5+ZKkGTNmaN26dZo3b55aW1uVk5Mjn8+nUaNGqaKiQv3797f3WbFihcLDwzV9+nS1trZqwoQJWrduncLCwuzMhg0blJuba/96MCMjI+TZWWFhYdq8ebNycnI0duxYRUZGKjMzU0uXLrUzlmWpsrJSc+bM0ciRIxUTE6P8/Hx7zAAAAI4gjw6/olpaWmRZlvx+f7fdbzV57WvdclygryvP+mZPD+GyPfj1xT09BKBX2lK2sFuPf6l/v3vtPVYAAAB9DcUKAADAEIoVAACAIRQrAAAAQyhWAAAAhlCsAAAADKFYAQAAGEKxAgAAMIRiBQAAYAjFCgAAwBCKFQAAgCEUKwAAAEMoVgAAAIZQrAAAAAyhWAEAABhCsQIAADCEYgUAAGAIxQoAAMAQihUAAIAhFCsAAABDKFYAAACGUKwAAAAMoVgBAAAYQrECAAAwhGIFAABgCMUKAADAEIoVAACAIRQrAAAAQyhWAAAAhlCsAAAADKFYAQAAGEKxAgAAMIRiBQAAYAjFCgAAwBCKFQAAgCEUKwAAAEMoVgAAAIZQrAAAAAyhWAEAABjSpWL1wAMP6MSJE53Wt7S06IEHHrjcMdk+/fRT/eu//quGDBmiyMhI3XrrrXruued05swZOxMMBrVo0SK53W5FRkZq/Pjx+uCDD0KOEwgENHfuXMXGxioqKkoZGRk6fPhwSMbn88nj8ciyLFmWJY/H0+kc6+vrNW3aNEVFRSk2Nla5ublqa2szdr4AAKBv61Kxeuedd85bKD755BO9++67lz2os5YsWaIf/ehHKikp0YEDB/T888/rhRde0KpVq+zM888/r+XLl6ukpER79uyRy+XSpEmTdPLkSTuTl5ensrIylZaWqrq6WqdOnVJ6ero6OjrsTGZmprxer8rLy1VeXi6v1yuPx2Nv7+jo0NSpU3X69GlVV1ertLRUGzduVEFBgbHzBQAAfVv45wn/9re/tf/z/v371dTUZC93dHSovLxcN910k7HB7dy5U3//93+vqVOnSpIGDx6s//7v/9bevXsl/XW2auXKlVq4cKEefvhhSdLrr7+u+Ph4vfnmm5o9e7b8fr/Wrl2rN954QxMnTpQkrV+/XgkJCdq6davS0tJ04MABlZeXq6amRqNGjZIkrVmzRikpKTp48KASExNVUVGh/fv3q6GhQW63W5K0bNkyzZw5U4sXL1Z0dPR5zyEQCCgQCNjLLS0txj4fAADQu3yuGas777xTd911lxwOhx544AHdeeed9is5OVk//OEP9f3vf9/Y4L72ta9p27Zt+vDDDyVJ7733nqqrq/Xggw9Kkg4dOqSmpialpqba+zidTo0bN047duyQJNXW1qq9vT0k43a7lZSUZGd27twpy7LsUiVJo0ePlmVZIZmkpCS7VElSWlqaAoGAamtrL3gOxcXF9uVFy7KUkJBwuR8LAADopT7XjNWhQ4cUDAZ16623avfu3Ro0aJC9LSIiQnFxcQoLCzM2uPnz58vv9+srX/mKwsLC1NHRocWLF+uf/umfJMmeMYuPjw/ZLz4+Xh999JGdiYiIUExMTKfM2f2bmpoUFxfX6f3j4uJCMue+T0xMjCIiIkJm7s61YMEC5efn28stLS2UKwAArlKfq1jdcsstkhRy83h3+vGPf6z169frzTff1G233Sav16u8vDy53W7NmDHDzjkcjpD9gsFgp3XnOjdzvnxXMudyOp1yOp0XHQsAALg6fK5i9bc+/PBDvfPOO2pubu5UtExdDnzqqaf09NNP67HHHpMkjRgxQh999JGKi4s1Y8YMuVwuSX+dTbrxxhvt/Zqbm+3ZJZfLpba2Nvl8vpBZq+bmZo0ZM8bOHD16tNP7Hzt2LOQ4u3btCtnu8/nU3t7eaSYLAAB8MXXpV4Fr1qzR8OHD9f3vf18/+clPVFZWZr82bdpkbHAff/yxrrkmdIhhYWF2kRsyZIhcLpcqKyvt7W1tbaqqqrJLU3Jysvr16xeSaWxsVF1dnZ1JSUmR3+/X7t277cyuXbvk9/tDMnV1dWpsbLQzFRUVcjqdSk5ONnbOAACg7+rSjNUPf/hDLV68WPPnzzc9nhDTpk3T4sWLdfPNN+u2227Tvn37tHz5cn3rW9+S9NdLc3l5eSoqKtLQoUM1dOhQFRUV6brrrlNmZqYkybIsZWVlqaCgQAMHDtSAAQNUWFioESNG2L8SHDZsmCZPnqzs7Gy99NJLkqRZs2YpPT1diYmJkqTU1FQNHz5cHo9HL7zwgo4fP67CwkJlZ2df8BeBAADgi6VLxcrn8+mRRx4xPZZOVq1apWeeeUY5OTlqbm6W2+3W7NmzQy41zps3T62trcrJyZHP59OoUaNUUVGh/v3725kVK1YoPDxc06dPV2trqyZMmKB169aF3Gi/YcMG5ebm2r8ezMjIUElJib09LCxMmzdvVk5OjsaOHavIyEhlZmZq6dKl3f45AACAvsERDAaDn3enrKws3XPPPXryySe7Y0xXtZaWFlmWJb/f320zXZPXvtYtxwX6uvKsb/b0EC7bg19f3NNDAHqlLWULu/X4l/r3u0szVl/+8pf1zDPPqKamRiNGjFC/fv1Ctufm5nblsAAAAH1al4rVyy+/rOuvv15VVVWqqqoK2eZwOChWAADgC6lLxerQoUOmxwEAANDndelxCwAAAOisSzNWZx93cCGvvvpqlwYDAADQl3X5cQt/q729XXV1dTpx4oQeeOABIwMDAADoa7pUrMrKyjqtO3PmjHJycnTrrbde9qAAAAD6ImP3WF1zzTX67ne/qxUrVpg6JAAAQJ9i9Ob1P/zhD/r0009NHhIAAKDP6NKlwPz8/JDlYDCoxsZGbd68WTNmzDAyMAAAgL6mS8Vq3759IcvXXHONBg0apGXLln3mLwYBAACuVl0qVm+//bbpcQAAAPR5XSpWZx07dkwHDx6Uw+HQ3/3d32nQoEGmxgUAANDndOnm9dOnT+tb3/qWbrzxRt13332699575Xa7lZWVpY8//tj0GAEAAPqELhWr/Px8VVVV6X/+53904sQJnThxQj/72c9UVVWlgoIC02MEAADoE7p0KXDjxo36yU9+ovHjx9vrHnzwQUVGRmr69OlavXq1qfEBAAD0GV2asfr4448VHx/faX1cXByXAgEAwBdWl4pVSkqKfvCDH+iTTz6x17W2turZZ59VSkqKscEBAAD0JV26FLhy5UpNmTJFX/rSl3THHXfI4XDI6/XK6XSqoqLC9BgBAAD6hC4VqxEjRuj3v/+91q9fr9/97ncKBoN67LHH9PjjjysyMtL0GAEAAPqELhWr4uJixcfHKzs7O2T9q6++qmPHjmn+/PlGBgcAANCXdOkeq5deeklf+cpXOq2/7bbb9KMf/eiyBwUAANAXdalYNTU16cYbb+y0ftCgQWpsbLzsQQEAAPRFXSpWCQkJ+vWvf91p/a9//Wu53e7LHhQAAEBf1KV7rL797W8rLy9P7e3teuCBByRJ27Zt07x583jyOgAA+MLqUrGaN2+ejh8/rpycHLW1tUmSrr32Ws2fP18LFiwwOkAAAIC+okvFyuFwaMmSJXrmmWd04MABRUZGaujQoXI6nabHBwAA0Gd0qViddf311+uee+4xNRYAAIA+rUs3rwMAAKAzihUAAIAhFCsAAABDKFYAAACGUKwAAAAMoVgBAAAYQrECAAAwhGIFAABgCMUKAADAEIoVAACAIb2+WP3pT3/SE088oYEDB+q6667TnXfeqdraWnt7MBjUokWL5Ha7FRkZqfHjx+uDDz4IOUYgENDcuXMVGxurqKgoZWRk6PDhwyEZn88nj8cjy7JkWZY8Ho9OnDgRkqmvr9e0adMUFRWl2NhY5ebm2v8INQAAQK8uVj6fT2PHjlW/fv30i1/8Qvv379eyZct0ww032Jnnn39ey5cvV0lJifbs2SOXy6VJkybp5MmTdiYvL09lZWUqLS1VdXW1Tp06pfT0dHV0dNiZzMxMeb1elZeXq7y8XF6vVx6Px97e0dGhqVOn6vTp06qurlZpaak2btyogoKCK/JZAACA3u+y/hHm7rZkyRIlJCTotddes9cNHjzY/s/BYFArV67UwoUL9fDDD0uSXn/9dcXHx+vNN9/U7Nmz5ff7tXbtWr3xxhuaOHGiJGn9+vVKSEjQ1q1blZaWpgMHDqi8vFw1NTUaNWqUJGnNmjVKSUnRwYMHlZiYqIqKCu3fv18NDQ1yu92SpGXLlmnmzJlavHixoqOjr9CnAgAAeqtePWP11ltvaeTIkXrkkUcUFxenu+66S2vWrLG3Hzp0SE1NTUpNTbXXOZ1OjRs3Tjt27JAk1dbWqr29PSTjdruVlJRkZ3bu3CnLsuxSJUmjR4+WZVkhmaSkJLtUSVJaWpoCgUDIpclzBQIBtbS0hLwAAMDVqVcXq//7v//T6tWrNXToUP3yl7/Uk08+qdzcXP3Xf/2XJKmpqUmSFB8fH7JffHy8va2pqUkRERGKiYm5aCYuLq7T+8fFxYVkzn2fmJgYRURE2JnzKS4utu/bsixLCQkJn+cjAAAAfUivLlZnzpzR3XffraKiIt11112aPXu2srOztXr16pCcw+EIWQ4Gg53WnevczPnyXcmca8GCBfL7/faroaHhouMCAAB9V68uVjfeeKOGDx8esm7YsGGqr6+XJLlcLknqNGPU3Nxszy65XC61tbXJ5/NdNHP06NFO73/s2LGQzLnv4/P51N7e3mkm6285nU5FR0eHvAAAwNWpVxersWPH6uDBgyHrPvzwQ91yyy2SpCFDhsjlcqmystLe3tbWpqqqKo0ZM0aSlJycrH79+oVkGhsbVVdXZ2dSUlLk9/u1e/duO7Nr1y75/f6QTF1dnRobG+1MRUWFnE6nkpOTDZ85AADoi3r1rwK/+93vasyYMSoqKtL06dO1e/duvfzyy3r55Zcl/fXSXF5enoqKijR06FANHTpURUVFuu6665SZmSlJsixLWVlZKigo0MCBAzVgwAAVFhZqxIgR9q8Ehw0bpsmTJys7O1svvfSSJGnWrFlKT09XYmKiJCk1NVXDhw+Xx+PRCy+8oOPHj6uwsFDZ2dnMQgEAAEm9vFjdc889Kisr04IFC/Tcc89pyJAhWrlypR5//HE7M2/ePLW2tionJ0c+n0+jRo1SRUWF+vfvb2dWrFih8PBwTZ8+Xa2trZowYYLWrVunsLAwO7Nhwwbl5ubavx7MyMhQSUmJvT0sLEybN29WTk6Oxo4dq8jISGVmZmrp0qVX4JMAAAB9gSMYDAZ7ehBfJC0tLbIsS36/v9tmuiavfe2zQ8AXUHnWN3t6CJftwa8v7ukhAL3SlrKF3Xr8S/373avvsQIAAOhLKFYAAACGUKwAAAAMoVgBAAAYQrECAAAwhGIFAABgCMUKAADAEIoVAACAIRQrAAAAQyhWAAAAhlCsAAAADKFYAQAAGEKxAgAAMIRiBQAAYAjFCgAAwBCKFQAAgCEUKwAAAEMoVgAAAIZQrAAAAAyhWAEAABhCsQIAADCEYgUAAGAIxQoAAMAQihUAAIAhFCsAAABDKFYAAACGUKwAAAAMoVgBAAAYQrECAAAwhGIFAABgCMUKAADAEIoVAACAIRQrAAAAQyhWAAAAhlCsAAAADKFYAQAAGEKxAgAAMIRiBQAAYAjFCgAAwJA+VayKi4vlcDiUl5dnrwsGg1q0aJHcbrciIyM1fvx4ffDBByH7BQIBzZ07V7GxsYqKilJGRoYOHz4ckvH5fPJ4PLIsS5ZlyePx6MSJEyGZ+vp6TZs2TVFRUYqNjVVubq7a2tq663QBAEAf02eK1Z49e/Tyyy/r9ttvD1n//PPPa/ny5SopKdGePXvkcrk0adIknTx50s7k5eWprKxMpaWlqq6u1qlTp5Senq6Ojg47k5mZKa/Xq/LycpWXl8vr9crj8djbOzo6NHXqVJ0+fVrV1dUqLS3Vxo0bVVBQ0P0nDwAA+oQ+UaxOnTqlxx9/XGvWrFFMTIy9PhgMauXKlVq4cKEefvhhJSUl6fXXX9fHH3+sN998U5Lk9/u1du1aLVu2TBMnTtRdd92l9evX6/3339fWrVslSQcOHFB5ebleeeUVpaSkKCUlRWvWrNHPf/5zHTx4UJJUUVGh/fv3a/369brrrrs0ceJELVu2TGvWrFFLS8uV/1AAAECv0yeK1Zw5czR16lRNnDgxZP2hQ4fU1NSk1NRUe53T6dS4ceO0Y8cOSVJtba3a29tDMm63W0lJSXZm586dsixLo0aNsjOjR4+WZVkhmaSkJLndbjuTlpamQCCg2traC449EAiopaUl5AUAAK5O4T09gM9SWlqq3/zmN9qzZ0+nbU1NTZKk+Pj4kPXx8fH66KOP7ExERETITNfZzNn9m5qaFBcX1+n4cXFxIZlz3ycmJkYRERF25nyKi4v17LPPftZpAgCAq0CvnrFqaGjQv/zLv2j9+vW69tprL5hzOBwhy8FgsNO6c52bOV++K5lzLViwQH6/3341NDRcdFwAAKDv6tXFqra2Vs3NzUpOTlZ4eLjCw8NVVVWl//iP/1B4eLg9g3TujFFzc7O9zeVyqa2tTT6f76KZo0ePdnr/Y8eOhWTOfR+fz6f29vZOM1l/y+l0Kjo6OuQFAACuTr26WE2YMEHvv/++vF6v/Ro5cqQef/xxeb1e3XrrrXK5XKqsrLT3aWtrU1VVlcaMGSNJSk5OVr9+/UIyjY2NqqurszMpKSny+/3avXu3ndm1a5f8fn9Ipq6uTo2NjXamoqJCTqdTycnJ3fo5AACAvqFX32PVv39/JSUlhayLiorSwIED7fV5eXkqKirS0KFDNXToUBUVFem6665TZmamJMmyLGVlZamgoEADBw7UgAEDVFhYqBEjRtg3ww8bNkyTJ09Wdna2XnrpJUnSrFmzlJ6ersTERElSamqqhg8fLo/HoxdeeEHHjx9XYWGhsrOzmYUCAACSenmxuhTz5s1Ta2urcnJy5PP5NGrUKFVUVKh///52ZsWKFQoPD9f06dPV2tqqCRMmaN26dQoLC7MzGzZsUG5urv3rwYyMDJWUlNjbw8LCtHnzZuXk5Gjs2LGKjIxUZmamli5deuVOFgAA9GqOYDAY7OlBfJG0tLTIsiz5/f5um+mavPa1bjku0NeVZ32zp4dw2R78+uKeHgLQK20pW9itx7/Uv9+9+h4rAACAvoRiBQAAYAjFCgAAwBCKFQAAgCEUKwAAAEMoVgAAAIZQrAAAAAyhWAEAABhCsQIAADCEYgUAAGAIxQoAAMAQihUAAIAhFCsAAABDKFYAAACGUKwAAAAMoVgBAAAYQrECAAAwhGIFAABgCMUKAADAEIoVAACAIRQrAAAAQyhWAAAAhlCsAAAADKFYAQAAGEKxAgAAMIRiBQAAYAjFCgAAwBCKFQAAgCEUKwAAAEMoVgAAAIZQrAAAAAyhWAEAABhCsQIAADCEYgUAAGAIxQoAAMAQihUAAIAhFCsAAABDKFYAAACGUKwAAAAM6dXFqri4WPfcc4/69++vuLg4PfTQQzp48GBIJhgMatGiRXK73YqMjNT48eP1wQcfhGQCgYDmzp2r2NhYRUVFKSMjQ4cPHw7J+Hw+eTweWZYly7Lk8Xh04sSJkEx9fb2mTZumqKgoxcbGKjc3V21tbd1y7gAAoO/p1cWqqqpKc+bMUU1NjSorK/Xpp58qNTVVp0+ftjPPP/+8li9frpKSEu3Zs0cul0uTJk3SyZMn7UxeXp7KyspUWlqq6upqnTp1Sunp6ero6LAzmZmZ8nq9Ki8vV3l5ubxerzwej729o6NDU6dO1enTp1VdXa3S0lJt3LhRBQUFV+bDAAAAvZ4jGAwGe3oQl+rYsWOKi4tTVVWV7rvvPgWDQbndbuXl5Wn+/PmS/jo7FR8fryVLlmj27Nny+/0aNGiQ3njjDT366KOSpCNHjighIUFbtmxRWlqaDhw4oOHDh6umpkajRo2SJNXU1CglJUW/+93vlJiYqF/84hdKT09XQ0OD3G63JKm0tFQzZ85Uc3OzoqOjzzvmQCCgQCBgL7e0tCghIUF+v/+C+1yuyWtf65bjAn1dedY3e3oIl+3Bry/u6SEAvdKWsoXdevyWlhZZlvWZf7979YzVufx+vyRpwIABkqRDhw6pqalJqampdsbpdGrcuHHasWOHJKm2tlbt7e0hGbfbraSkJDuzc+dOWZZllypJGj16tCzLCskkJSXZpUqS0tLSFAgEVFtbe8ExFxcX25cXLctSQkLC5X4MAACgl+ozxSoYDCo/P19f+9rXlJSUJElqamqSJMXHx4dk4+Pj7W1NTU2KiIhQTEzMRTNxcXGd3jMuLi4kc+77xMTEKCIiws6cz4IFC+T3++1XQ0PD5zltAADQh4T39AAu1Xe+8x399re/VXV1dadtDocjZDkYDHZad65zM+fLdyVzLqfTKafTedGxAACAq0OfmLGaO3eu3nrrLb399tv60pe+ZK93uVyS1GnGqLm52Z5dcrlcamtrk8/nu2jm6NGjnd732LFjIZlz38fn86m9vb3TTBYAAPhi6tXFKhgM6jvf+Y5++tOf6le/+pWGDBkSsn3IkCFyuVyqrKy017W1tamqqkpjxoyRJCUnJ6tfv34hmcbGRtXV1dmZlJQU+f1+7d69287s2rVLfr8/JFNXV6fGxkY7U1FRIafTqeTkZPMnDwAA+pxefSlwzpw5evPNN/Wzn/1M/fv3t2eMLMtSZGSkHA6H8vLyVFRUpKFDh2ro0KEqKirSddddp8zMTDublZWlgoICDRw4UAMGDFBhYaFGjBihiRMnSpKGDRumyZMnKzs7Wy+99JIkadasWUpPT1diYqIkKTU1VcOHD5fH49ELL7yg48ePq7CwUNnZ2d326z4AANC39OpitXr1aknS+PHjQ9a/9tprmjlzpiRp3rx5am1tVU5Ojnw+n0aNGqWKigr179/fzq9YsULh4eGaPn26WltbNWHCBK1bt05hYWF2ZsOGDcrNzbV/PZiRkaGSkhJ7e1hYmDZv3qycnByNHTtWkZGRyszM1NKlS7vp7AEAQF/Tp55jdTW41OdgXA6eYwWcH8+xAq5ePMcKAADgKkOxAgAAMIRiBQAAYAjFCgAAwBCKFQAAgCEUKwAAAEMoVgAAAIZQrAAAAAyhWAEAABhCsQIAADCEYgUAAGAIxQoAAMAQihUAAIAhFCsAAABDKFYAAACGUKwAAAAMoVgBAAAYQrECAAAwhGIFAABgCMUKAADAEIoVAACAIRQrAAAAQyhWAAAAhlCsAAAADKFYAQAAGEKxAgAAMIRiBQAAYAjFCgAAwBCKFQAAgCEUKwAAAEMoVgAAAIZQrAAAAAyhWAEAABhCsQIAADCEYgUAAGAIxQoAAMAQihUAAIAhFCsAAABDKFZd8OKLL2rIkCG69tprlZycrHfffbenhwQAAHoBitXn9OMf/1h5eXlauHCh9u3bp3vvvVdTpkxRfX19Tw8NAAD0MIrV57R8+XJlZWXp29/+toYNG6aVK1cqISFBq1ev7umhAQCAHhbe0wPoS9ra2lRbW6unn346ZH1qaqp27Nhx3n0CgYACgYC97Pf7JUktLS3dNs5PW1u77dhAX9ad37srpb39k54eAtArdff3++zxg8HgRXMUq8/hz3/+szo6OhQfHx+yPj4+Xk1NTefdp7i4WM8++2yn9QkJCd0yRgAXZs2d09NDANBNLOuHV+R9Tp48KcuyLridYtUFDocjZDkYDHZad9aCBQuUn59vL585c0bHjx/XwIEDL7gPrh4tLS1KSEhQQ0ODoqOje3o4AAzi+/3FEgwGdfLkSbnd7ovmKFafQ2xsrMLCwjrNTjU3N3eaxTrL6XTK6XSGrLvhhhu6a4jopaKjo/kfXuAqxff7i+NiM1VncfP65xAREaHk5GRVVlaGrK+srNSYMWN6aFQAAKC3YMbqc8rPz5fH49HIkSOVkpKil19+WfX19XryySd7emgAAKCHUaw+p0cffVR/+ctf9Nxzz6mxsVFJSUnasmWLbrnllp4eGnohp9OpH/zgB50uBwPo+/h+43wcwc/63SAAAAAuCfdYAQAAGEKxAgAAMIRiBQAAYAjFCgAAwBCKFXAZXnzxRQ0ZMkTXXnutkpOT9e677140X1VVpeTkZF177bW69dZb9aMf/egKjRTA57F9+3ZNmzZNbrdbDodDmzZt+sx9+H5DolgBXfbjH/9YeXl5Wrhwofbt26d7771XU6ZMUX19/Xnzhw4d0oMPPqh7771X+/bt0/e+9z3l5uZq48aNV3jkAD7L6dOndccdd6ikpOSS8ny/cRaPWwC6aNSoUbr77ru1evVqe92wYcP00EMPqbi4uFN+/vz5euutt3TgwAF73ZNPPqn33ntPO3fuvCJjBvD5ORwOlZWV6aGHHrpghu83zmLGCuiCtrY21dbWKjU1NWR9amqqduzYcd59du7c2SmflpamvXv3qr29vdvGCqD78f3GWRQroAv+/Oc/q6Ojo9M/vh0fH9/pH+k+q6mp6bz5Tz/9VH/+85+7bawAuh/fb5xFsQIug8PhCFkOBoOd1n1W/nzrAfQ9fL8hUayALomNjVVYWFin2anm5uZO/6/1LJfLdd58eHi4Bg4c2G1jBdD9+H7jLIoV0AURERFKTk5WZWVlyPrKykqNGTPmvPukpKR0yldUVGjkyJHq169ft40VQPfj+42zKFZAF+Xn5+uVV17Rq6++qgMHDui73/2u6uvr9eSTT0qSFixYoG984xt2/sknn9RHH32k/Px8HThwQK+++qrWrl2rwsLCnjoFABdw6tQpeb1eeb1eSX99nILX67Ufp8L3GxcUBNBl//mf/xm85ZZbghEREcG77747WFVVZW+bMWNGcNy4cSH5d955J3jXXXcFIyIigoMHDw6uXr36Co8YwKV4++23g5I6vWbMmBEMBvl+48J4jhUAAIAhXAoEAAAwhGIFAABgCMUKAADAEIoVAACAIRQrAAAAQyhWAAAAhlCsAAAADKFYAQAAGEKxAgAAMIRiBQCfobm5WbNnz9bNN98sp9Mpl8ultLQ07dy5s6eHBqCXCe/pAQBAb/cP//APam9v1+uvv65bb71VR48e1bZt23T8+PGeHhqAXoYZKwC4iBMnTqi6ulpLlizR/fffr1tuuUVf/epXtWDBAk2dOlWS5Pf7NWvWLMXFxSk6OloPPPCA3nvvPUnSsWPH5HK5VFRUZB9z165dioiIUEVFRY+cE4DuQ7ECgIu4/vrrdf3112vTpk0KBAKdtgeDQU2dOlVNTU3asmWLamtrdffdd2vChAk6fvy4Bg0apFdffVWLFi3S3r17derUKT3xxBPKyclRampqD5wRgO7kCAaDwZ4eBAD0Zhs3blR2drZaW1t19913a9y4cXrsscd0++2361e/+pW+/vWvq7m5WU6n097ny1/+subNm6dZs2ZJkubMmaOtW7fqnnvu0Xvvvac9e/bo2muv7alTAtBNKFYAcAk++eQTvfvuu9q5c6fKy8u1e/duvfLKKzp27JiefvppRUZGhuRbW1tVWFioJUuW2MtJSUlqaGjQ3r17dfvtt/fEaQDoZhQrAOiCb3/726qsrFROTo5WrVqld955p1PmhhtuUGxsrCTpgw8+0MiRI9Xe3q6ysjJNmzbtCo8YwJXArwIBoAuGDx+uTZs26e6771ZTU5PCw8M1ePDg82bb2tr0+OOP69FHH9VXvvIVZWVl6f3331d8fPyVHTSAbseMFQBcxF/+8hc98sgj+ta3vqXbb79d/fv31969ezV37lxNnTpVr7zyiu677z6dPHlSS5YsUWJioo4cOaItW7booYce0siRI/XUU0/pJz/5id577z1df/31uv/++9W/f3/9/Oc/7+nTA2AYxQoALiIQCGjRokWqqKjQH/7wB7W3tyshIUGPPPKIvve97ykyMlInT57UwoULtXHjRvvxCvfdd5+Ki4v1hz/8QZMmTdLbb7+tr33ta5Kk+vp63X777SouLtY///M/9/AZAjCJYgUAAGAIz7ECAAAwhGIFAABgCMUKAADAEIoVAACAIRQrAAAAQyhWAAAAhlCsAAAADKFYAQAAGEKxAgAAMIRiBQAAYAjFCgAAwJD/B3aUJhQhXL00AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.0    141974\n",
       "1.0    111706\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.countplot(x='Sex', data=df, palette=\"mako_r\")\n",
    "plt.show()\n",
    "df.Sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9e4a291e",
   "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>HighBP</th>\n",
       "      <th>HighChol</th>\n",
       "      <th>CholCheck</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Smoker</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>PhysActivity</th>\n",
       "      <th>Fruits</th>\n",
       "      <th>Veggies</th>\n",
       "      <th>HvyAlcoholConsump</th>\n",
       "      <th>GenHlth</th>\n",
       "      <th>MentHlth</th>\n",
       "      <th>PhysHlth</th>\n",
       "      <th>DiffWalk</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HeartAttack</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.395592</td>\n",
       "      <td>0.395331</td>\n",
       "      <td>0.959966</td>\n",
       "      <td>28.270060</td>\n",
       "      <td>0.424833</td>\n",
       "      <td>0.027657</td>\n",
       "      <td>0.256342</td>\n",
       "      <td>0.768630</td>\n",
       "      <td>0.637326</td>\n",
       "      <td>0.816356</td>\n",
       "      <td>0.058347</td>\n",
       "      <td>2.422359</td>\n",
       "      <td>3.030094</td>\n",
       "      <td>3.731329</td>\n",
       "      <td>0.142565</td>\n",
       "      <td>0.426559</td>\n",
       "      <td>7.813716</td>\n",
       "      <td>5.082116</td>\n",
       "      <td>6.148035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.750345</td>\n",
       "      <td>0.701168</td>\n",
       "      <td>0.988658</td>\n",
       "      <td>29.466622</td>\n",
       "      <td>0.619470</td>\n",
       "      <td>0.164776</td>\n",
       "      <td>0.687231</td>\n",
       "      <td>0.640355</td>\n",
       "      <td>0.604696</td>\n",
       "      <td>0.763906</td>\n",
       "      <td>0.035492</td>\n",
       "      <td>3.367555</td>\n",
       "      <td>4.670322</td>\n",
       "      <td>9.154439</td>\n",
       "      <td>0.414975</td>\n",
       "      <td>0.572887</td>\n",
       "      <td>10.131210</td>\n",
       "      <td>4.745971</td>\n",
       "      <td>5.148161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               HighBP  HighChol  CholCheck        BMI    Smoker    Stroke  \\\n",
       "HeartAttack                                                                 \n",
       "0.0          0.395592  0.395331   0.959966  28.270060  0.424833  0.027657   \n",
       "1.0          0.750345  0.701168   0.988658  29.466622  0.619470  0.164776   \n",
       "\n",
       "             Diabetes  PhysActivity    Fruits   Veggies  HvyAlcoholConsump  \\\n",
       "HeartAttack                                                                  \n",
       "0.0          0.256342      0.768630  0.637326  0.816356           0.058347   \n",
       "1.0          0.687231      0.640355  0.604696  0.763906           0.035492   \n",
       "\n",
       "              GenHlth  MentHlth  PhysHlth  DiffWalk       Sex        Age  \\\n",
       "HeartAttack                                                                \n",
       "0.0          2.422359  3.030094  3.731329  0.142565  0.426559   7.813716   \n",
       "1.0          3.367555  4.670322  9.154439  0.414975  0.572887  10.131210   \n",
       "\n",
       "             Education    Income  \n",
       "HeartAttack                       \n",
       "0.0           5.082116  6.148035  \n",
       "1.0           4.745971  5.148161  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('HeartAttack').mean()\n",
    "# not sure how this adds value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4bb4ad4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.Age,df.HeartAttack).plot(kind=\"bar\",figsize=(20,6))\n",
    "plt.title('Heart Attack Frequency for Age groups')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7bc64812",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.Sex,df.HeartAttack).plot(kind=\"bar\",figsize=(15,6),color=['#1CA53B','#AA1111' ])\n",
    "plt.title('Heart Disease Frequency for Sex')\n",
    "plt.xlabel('Sex')\n",
    "plt.xticks(rotation=0)\n",
    "plt.legend([\"No heart attack\", \"Had a heart attack\"])\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fdf27bb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.GenHlth,df.HeartAttack).plot(kind=\"bar\",figsize=(15,6),color=['#1CA53B','#AA1111' ])\n",
    "plt.title('Heart Disease Frequency for General Health Score')\n",
    "plt.xlabel('General Health Score')\n",
    "plt.xticks(rotation=0)\n",
    "plt.legend([\"No heart attack\", \"Had a heart attack\"])\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3b85b2e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.Diabetes,df.HeartAttack).plot(kind=\"bar\",figsize=(15,6),color=['#1CA53B','#AA1111' ])\n",
    "plt.title('Heart Disease Frequency for Diabetes')\n",
    "plt.xlabel('Diabetes')\n",
    "plt.xticks(rotation=0)\n",
    "plt.legend([\"No heart attack\", \"Had a heart attack\"])\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6c8e380c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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swO/LL7+0zaTMSVZAEh8fn23dtcuyrkXWSzOyXBvIZR17xIgRevrpp3M8bo0aNa7bU06ynk938uRJlS9fPtfaQYMG6bnnntPu3bs1Y8YMVa9e3Rbk5lXW9YmLi8u27vTp09mC1dsxHkqVKqUrV67o7NmzdoFWTj8zV1fXbD8fKfvPqEyZMsrIyFB8fPx1g/j169fr9OnT2rhxo232nSSdO3cun2dyValSpa57PSXd9FuYAQC403A7LQAAuKGgoCAZhqFTp06pQYMG2T61a9eW9L8A4tqgL6+zmApypszatWv10UcfqVGjRtluE74eDw8PPfXUU3rzzTeVlpamffv2Sbp6HWJiYlSlSpUcr0NWiGexWOTo6Gh3C9+lS5e0ePHi6x7TwcFBDRs2tL2lc/fu3bZjnj17VhkZGTkeM69h1s1q1aqVHB0ddfjw4RyPmzXzq0aNGipbtqw+++wzu9uWjx8/rs2bN9vtM2v21M8//2y3/O9vM83aZ7Vq1bR3797rHjuv4WVgYKAcHBw0a9asG9Z27NhRFSpU0JAhQ/T999+rf//++Q7XAgIC5Obmpk8++cRu+cmTJ7V+/foCe/tsbp544glJ0qeffmq3fMmSJdlqK1WqpISEBNtsSOnqyza+++47u7qsW8hzu555+fsgL2O+efPm2r9/v22MZFm0aJEsFovtfAEAuNswEw8AANxQ48aN9eKLL6pHjx7auXOnHn/8cXl4eCguLk5RUVGqXbu2XnrpJd1///2qUqWKhg8fLsMwVLJkSf33v//NdsvgjWSFgpMmTdJTTz0lBwcHPfjgg9e9pVS6eivv1q1bJV2d6RUbG6vVq1dr2bJlqlmzppYtW5brMfv06SM3Nzc1btxYZcuWVXx8vMLDw2W1Wm23lI4dO1Zr165Vo0aNNGjQINWoUUOXL1/WsWPH9O2332r27NkqV66c2rRpo4iICAUHB+vFF1/U2bNnNXXq1GxhxuzZs7V+/Xq1adNGFSpU0OXLl/Xxxx9Lklq0aCFJ6tKliz799FO1bt1ar7zyih555BE5OTnp5MmT2rBhg9q3b6+OHTvm6frejEqVKmns2LF68803deTIET355JMqUaKE/vjjD23fvl0eHh4aM2aMihQponHjxql3797q2LGj+vTpo3Pnzmn06NHZbrX08fFRixYtFB4erhIlSqhixYpat26dvvrqq2zHnzNnjp566im1atVKoaGhuu+++/TXX3/pwIED2r17t+2ZdXk5nzfeeEPjxo3TpUuX9Pzzz8tqtWr//v36888/NWbMGFutg4ODBgwYoNdff10eHh7ZnmeXF8WLF9fIkSP1xhtvqFu3bnr++ed19uxZjRkzRq6urnrrrbfyve8sMTExOb7FtkqVKipTpowCAwP1+OOPa9iwYUpJSVGDBg30008/5Rgqd+7cWaNGjVKXLl302muv6fLly3rvvfeUkZFhV/fYY48pJCRE48eP1x9//KGgoCC5uLhoz549cnd318CBA9WoUSOVKFFC/fr101tvvSUnJyd9+umn2rt3b7bj5mXMDx48WIsWLVKbNm00duxYVaxYUd98841mzpypl156SdWrV8/vpQQAwNwK860aAACgcOT0Fs2/a9OmTY5vqPz444+Nhg0bGh4eHoabm5tRpUoVo1u3bsbOnTttNfv37zdatmxpeHp6GiVKlDCee+45IzY2NttbMLPednnmzJlsx0lNTTV69+5tlClTxrBYLIakXN8MmvVWz6yPm5ubUaFCBaNt27bGxx9/bPem0SzXvp124cKFxhNPPGF4e3sbzs7Ohq+vr9GpUyfj559/ttvuzJkzxqBBgww/Pz/DycnJKFmypFG/fn3jzTffNC5cuGB3rWrUqGG4uLgYlStXNsLDw4158+bZncuWLVuMjh07GhUrVjRcXFyMUqVKGU2bNjW+/vpru2Omp6cbU6dONerUqWO4uroaRYsWNe6//36jb9++xqFDh657XW50nQ3jxr8LK1euNJ544gmjWLFihouLi1GxYkXj2WefNb7//nu7uo8++sioVq2a4ezsbFSvXt34+OOPc3zTaVxcnPHss88aJUuWNKxWq9G1a1dj586d2d5OaxiGsXfvXqNTp06Gl5eX4eTkZPj4+Bj/+te/jNmzZ9+w/+u9CXfRokXGww8/bLuO9erVy3Zcw7j6dl1JRr9+/XK8LjnJ7Vp+9NFHxoMPPmg4OzsbVqvVaN++fba3Gd/oLcvXO971PnPnzrXVnjt3zujZs6dRvHhxw93d3WjZsqXx66+/ZhuXhmEY3377rVG3bl3Dzc3NqFy5sjFjxoxsb6c1jKtvJn7nnXcMf39/23kFBAQY//3vf201mzdvNgICAgx3d3ejTJkyRu/evY3du3dn+3nnNuavfTutYRjG8ePHjeDgYKNUqVKGk5OTUaNGDWPKlCm2txYbxv/eTjtlypRs1y6n8wYAwOwshnETr2sDAAAA8ig0NFQbN27M18stCtv777+vQYMGKSYmRg888EBhtwMAAMDttAAAAECWPXv26OjRoxo7dqzat29PgAcAAEyDEA8AAAD4fx07dlR8fLwee+wxzZ49u7DbAQAAsOF2WgAAAAAAAMDkihR2AwAAAAAAAAByR4gHAAAAAAAAmBwhHgAAAAAAAGByvNjiH5aZmanTp0/L09NTFoulsNsBAAAAAABAITEMQ+fPn5evr6+KFMl9rh0h3j/s9OnTKl++fGG3AQAAAAAAAJM4ceKEypUrl2sNId4/zNPTU9LVH06xYsUKuRsAAAAAAAAUluTkZJUvX96WF+WGEO8flnULbbFixQjxAAAAAAAAcFOPXOPFFgAAAAAAAIDJEeIBAAAAAAAAJkeIBwAAAAAAAJgcz8QzGcMwdOXKFWVkZBR2K0C+OTg4yNHR8abu6QcAAAAAADdGiGciaWlpiouL08WLFwu7FeCWubu7q2zZsnJ2di7sVgAAAAAAuOMR4plEZmamjh49KgcHB/n6+srZ2ZlZTLgjGYahtLQ0nTlzRkePHlW1atVUpAh37gMAAAAAcCsI8UwiLS1NmZmZKl++vNzd3Qu7HeCWuLm5ycnJScePH1daWppcXV0LuyUAAAAAAO5oTI8xGWYs4W7B7zIAAAAAAAWHf2UDAAAAAAAAJkeIBwAAAAAAAJgcz8S7A5Rf0uQfPd6J4Kh/9Hi5GT16tFauXKno6OjCbuWOcezYMfn5+WnPnj2qW7duYbcDAAAAAAAKADPxcMtCQ0NlsVj09ttv2y1fuXLlHf+G3UqVKmn69Om3vJ8FCxaoePHit23/AAAAAADg7kaIhwLh6uqqSZMmKTExsbBbKRBpaWmF3QIAAAAAAIANIR4KRIsWLeTj46Pw8PBc65YvX64HHnhALi4uqlSpkqZNm3ZT+1+8eLEqVaokq9WqLl266Pz587Z1hmFo8uTJqly5stzc3FSnTh19+eWXtvUZGRnq1auX/Pz85Obmpho1aujdd9+1239oaKg6dOig8PBw+fr6qnr16mrWrJmOHz+uwYMHy2Kx5DqrMCIiQrVr15aHh4fKly+v/v3768KFC5KkjRs3qkePHkpKSrLtZ/To0dfd/9mzZ/X888+rXLlycnd3V+3atfXZZ5/ZHS8zM1OTJk1S1apV5eLiogoVKmjChAk59paZmak+ffqoevXqOn78+E1dbwAAAAAAYC6EeCgQDg4Omjhxot5//32dPHkyx5pdu3apU6dO6tKli3755ReNHj1aI0eO1IIFC3Ld9+HDh7Vy5UqtWrVKq1at0qZNm+xu3f33v/+t+fPna9asWdq3b58GDx6srl27atOmTZKuhljlypXTsmXLtH//fo0aNUpvvPGGli1bZnecdevW6cCBA1q7dq1WrVqlr776SuXKldPYsWMVFxenuLi46/ZYpEgRvffee4qJidHChQu1fv16DRs2TJLUqFEjTZ8+XcWKFbPtZ+jQodfd/+XLl1W/fn2tWrVKMTExevHFFxUSEqJt27bZjjdixAhNmjRJI0eO1P79+7VkyRJ5e3tn6ystLU2dOnXSzp07FRUVpYoVK+Z6rQEAAAAAgDnxYgsUmI4dO6pu3bp66623NG/evGzrIyIi1Lx5c40cOVKSVL16de3fv19TpkxRaGjodfebmZmpBQsWyNPTU5IUEhKidevWacKECUpJSVFERITWr1+vgIAASVLlypUVFRWlOXPmqGnTpnJyctKYMWNs+/Pz89PmzZu1bNkyderUybbcw8NDH330kZydnW3LHBwc5OnpKR8fn1zPPSwszG7/48aN00svvaSZM2fK2dlZVqtVFosl235y2v99992noUOH2r4PHDhQkZGR+uKLL9SwYUOdP39e7777rmbMmKHu3btLkqpUqaImTexfgHLhwgW1adNGly5d0saNG2W1WnM9BwAAAAAAYF6FOhPvhx9+UNu2beXr6yuLxaKVK1farTcMQ6NHj5avr6/c3NzUrFkz7du3z64mNTVVAwcOVOnSpeXh4aF27dplmwmWmJiokJAQWa1WWa1WhYSE6Ny5c3Y1sbGxatu2rTw8PFS6dGkNGjQo23PRfvnlFzVt2lRubm667777NHbsWBmGUWDX424wadIkLVy4UPv378+27sCBA2rcuLHdssaNG+vQoUPKyMi47j4rVapkC/AkqWzZskpISJAk7d+/X5cvX1bLli1VtGhR22fRokU6fPiwbZvZs2erQYMGKlOmjIoWLaq5c+cqNjbW7ji1a9e2C/DyYsOGDWrZsqXuu+8+eXp6qlu3bjp79qxSUlLyvK+MjAxNmDBBDz74oEqVKqWiRYtqzZo1tn4PHDig1NRUNW/ePNf9PP/887pw4YLWrFlDgAcAAAAAwB2uUEO8lJQU1alTRzNmzMhx/eTJkxUREaEZM2Zox44d8vHxUcuWLe2ehxYWFqYVK1Zo6dKlioqK0oULFxQUFGQXCgUHBys6OlqRkZGKjIxUdHS0QkJCbOszMjLUpk0bpaSkKCoqSkuXLtX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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.Education,df.HeartAttack).plot(kind=\"bar\",figsize=(15,6),color=['#1CA53B','#AA1111' ])\n",
    "plt.title('Heart Disease Frequency for Education')\n",
    "plt.xlabel('Education')\n",
    "plt.xticks(rotation=0)\n",
    "plt.legend([\"No heart attack\", \"Had a heart attack\"])\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7feef259",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.Income,df.HeartAttack).plot(kind=\"bar\",figsize=(15,6),color=['#1CA53B','#AA1111' ])\n",
    "plt.title('Heart Disease Frequency for Income')\n",
    "plt.xlabel('Income group')\n",
    "plt.xticks(rotation=0)\n",
    "plt.legend([\"No heart attack\", \"Had a heart attack\"])\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73a49990-9e6f-4422-9b67-e847b4c5c48e",
   "metadata": {},
   "source": [
    "## Data Cleaning & Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "541c50d0-53b4-4245-9ea5-058f9818c7cc",
   "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>HeartAttack</th>\n",
       "      <th>HighBP</th>\n",
       "      <th>HighChol</th>\n",
       "      <th>CholCheck</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Smoker</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>PhysActivity</th>\n",
       "      <th>Fruits</th>\n",
       "      <th>Veggies</th>\n",
       "      <th>HvyAlcoholConsump</th>\n",
       "      <th>GenHlth</th>\n",
       "      <th>MentHlth</th>\n",
       "      <th>PhysHlth</th>\n",
       "      <th>DiffWalk</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   HeartAttack  HighBP  HighChol  CholCheck   BMI  Smoker  Stroke  Diabetes  \\\n",
       "0          0.0     1.0       1.0        1.0  40.0     1.0     0.0       0.0   \n",
       "1          0.0     0.0       0.0        0.0  25.0     1.0     0.0       0.0   \n",
       "2          0.0     1.0       1.0        1.0  28.0     0.0     0.0       0.0   \n",
       "3          0.0     1.0       0.0        1.0  27.0     0.0     0.0       0.0   \n",
       "4          0.0     1.0       1.0        1.0  24.0     0.0     0.0       0.0   \n",
       "\n",
       "   PhysActivity  Fruits  Veggies  HvyAlcoholConsump  GenHlth  MentHlth  \\\n",
       "0           0.0     0.0      1.0                0.0      5.0      18.0   \n",
       "1           1.0     0.0      0.0                0.0      3.0       0.0   \n",
       "2           0.0     1.0      0.0                0.0      5.0      30.0   \n",
       "3           1.0     1.0      1.0                0.0      2.0       0.0   \n",
       "4           1.0     1.0      1.0                0.0      2.0       3.0   \n",
       "\n",
       "   PhysHlth  DiffWalk  Sex   Age  Education  Income  \n",
       "0      15.0       1.0  0.0   9.0        4.0     3.0  \n",
       "1       0.0       0.0  0.0   7.0        6.0     1.0  \n",
       "2      30.0       1.0  0.0   9.0        4.0     8.0  \n",
       "3       0.0       0.0  0.0  11.0        3.0     6.0  \n",
       "4       0.0       0.0  0.0  11.0        5.0     4.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0875a19b-757a-47a1-99a4-0dbc64b25571",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9057986439608956"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.HeartAttack.value_counts()/len(df))[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "264901d3",
   "metadata": {},
   "source": [
    "### Problems found in the data and actions taken\n",
    "- **Problem:** some null values in the data. **Action:** we droped those rows as werent that many\n",
    "- **Problem:** two age values were negative **Action:** doped as they were only two\n",
    "- **Problem:** diabetes was suposed to be binary but it has values 0, 1, and 2. **Action:** it was splited into two columns diabetes_1 and diabetes_2 inicating diabetes type 1 and type 2 respectively and both binary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cbf3f9fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def isDiabetes1(row):\n",
    "    if row['Diabetes']==1:\n",
    "        return 1.0\n",
    "    return 0.0\n",
    "def isDiabetes2(row):\n",
    "    if row['Diabetes']==2:\n",
    "        return 1.0\n",
    "    return 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7f45c505",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 253653 entries, 0 to 253679\n",
      "Data columns (total 19 columns):\n",
      " #   Column             Non-Null Count   Dtype  \n",
      "---  ------             --------------   -----  \n",
      " 0   HeartAttack        253653 non-null  float64\n",
      " 1   HighBP             253653 non-null  float64\n",
      " 2   HighChol           253653 non-null  float64\n",
      " 3   CholCheck          253653 non-null  float64\n",
      " 4   BMI                253653 non-null  float64\n",
      " 5   Smoker             253653 non-null  float64\n",
      " 6   Stroke             253653 non-null  float64\n",
      " 7   PhysActivity       253653 non-null  float64\n",
      " 8   Fruits             253653 non-null  float64\n",
      " 9   Veggies            253653 non-null  float64\n",
      " 10  HvyAlcoholConsump  253653 non-null  float64\n",
      " 11  GenHlth            253653 non-null  float64\n",
      " 12  DiffWalk           253653 non-null  float64\n",
      " 13  Sex                253653 non-null  float64\n",
      " 14  Age                253653 non-null  float64\n",
      " 15  Education          253653 non-null  float64\n",
      " 16  Income             253653 non-null  float64\n",
      " 17  diabetes_1         253653 non-null  float64\n",
      " 18  diabetes_2         253653 non-null  float64\n",
      "dtypes: float64(19)\n",
      "memory usage: 38.7 MB\n"
     ]
    }
   ],
   "source": [
    "# The amount of missing values is not considerable enough to do a more precise cleaning\n",
    "df = df.dropna()\n",
    "# Removing negative values from age \n",
    "df = df[df.Age > 0]\n",
    "# Adding diabetes_1 and diabetes_2\n",
    "df[\"diabetes_1\"] = df.apply(lambda row: isDiabetes1(row), axis=1)\n",
    "df[\"diabetes_2\"] = df.apply(lambda row: isDiabetes2(row), axis=1)\n",
    "df = df.drop('Diabetes',axis=1)\n",
    "df = df.drop('MentHlth', axis=1)\n",
    "df = df.drop('PhysHlth', axis=1)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "9184911a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.HeartAttack\n",
    "x = df.drop('HeartAttack', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "273538c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "176cb9cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracies = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f113f128",
   "metadata": {},
   "source": [
    "#### Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "589a6eee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy 90.49%\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(solver='lbfgs', max_iter=1000)\n",
    "\n",
    "lr.fit(x_train, y_train)\n",
    "acc = lr.score(x_test, y_test)*100\n",
    "\n",
    "accuracies['Logistic Regression'] = acc\n",
    "print(\"Test Accuracy {:.2f}%\".format(acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a05714c6",
   "metadata": {},
   "source": [
    "#### KNN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "803f4cd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum KNN Score is 90.30%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors = 2)  # n_neighbors means k\n",
    "knn.fit(x_train, y_train)\n",
    "prediction = knn.predict(x_test)\n",
    "\n",
    "scoreList = []\n",
    "for i in range(1,20):\n",
    "    knn2 = KNeighborsClassifier(n_neighbors = i)  # n_neighbors means k\n",
    "    knn2.fit(x_train, y_train)\n",
    "    scoreList.append(knn2.score(x_test, y_test))\n",
    "\n",
    "plt.plot(range(1,20), scoreList)\n",
    "plt.xticks(np.arange(1,20,1))\n",
    "plt.xlabel(\"K value\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.show()\n",
    "\n",
    "acc = max(scoreList)*100\n",
    "accuracies['KNN'] = acc\n",
    "print(\"Maximum KNN Score is {:.2f}%\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "0dca077a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Naive Bayes: 81.61%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "nb = GaussianNB()\n",
    "nb.fit(x_train, y_train)\n",
    "\n",
    "acc = nb.score(x_test,y_test)*100\n",
    "accuracies['Naive Bayes'] = acc\n",
    "print(\"Accuracy of Naive Bayes: {:.2f}%\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "3191402f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Test Accuracy 85.23%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtc = DecisionTreeClassifier()\n",
    "dtc.fit(x_train, y_train)\n",
    "\n",
    "acc = dtc.score(x_test, y_test)*100\n",
    "accuracies['Decision Tree'] = acc\n",
    "print(\"Decision Tree Test Accuracy {:.2f}%\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "061ee92b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Algorithm Accuracy Score : 89.51%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(n_estimators = 1000, random_state = 1)\n",
    "rf.fit(x_train, y_train)\n",
    "\n",
    "acc = rf.score(x_test,y_test)*100\n",
    "accuracies['Random Forest'] = acc\n",
    "print(\"Random Forest Algorithm Accuracy Score : {:.2f}%\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "16d5f981",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = [\"purple\", \"green\", \"orange\", \"magenta\",\"#CFC60E\",\"#0FBBAE\"]\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.figure(figsize=(16,5))\n",
    "plt.yticks(np.arange(0,100,10))\n",
    "plt.ylabel(\"Accuracy %\")\n",
    "plt.xlabel(\"Algorithms\")\n",
    "sns.barplot(x=list(accuracies.keys()), y=list(accuracies.values()), palette=colors)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e15ca01b",
   "metadata": {},
   "source": [
    "### Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "fc4c3e23",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_head_lr = lr.predict(x_test)\n",
    "knn3 = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn3.fit(x_train, y_train)\n",
    "y_head_knn = knn3.predict(x_test)\n",
    "y_head_nb = nb.predict(x_test)\n",
    "y_head_dtc = dtc.predict(x_test)\n",
    "y_head_rf = rf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "22fa041d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm_lr = confusion_matrix(y_test,y_head_lr)\n",
    "cm_knn = confusion_matrix(y_test,y_head_knn)\n",
    "cm_nb = confusion_matrix(y_test,y_head_nb)\n",
    "cm_dtc = confusion_matrix(y_test,y_head_dtc)\n",
    "cm_rf = confusion_matrix(y_test,y_head_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "45ed924f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2400x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(24,12))\n",
    "\n",
    "plt.suptitle(\"Confusion Matrixes\",fontsize=24)\n",
    "plt.subplots_adjust(wspace = 0.4, hspace= 0.4)\n",
    "\n",
    "plt.subplot(2,3,1)\n",
    "plt.title(\"Logistic Regression Confusion Matrix\")\n",
    "sns.heatmap(cm_lr,annot=True,cmap=\"Blues\",fmt=\"d\",cbar=False, annot_kws={\"size\": 24})\n",
    "\n",
    "plt.subplot(2,3,2)\n",
    "plt.title(\"K Nearest Neighbors Confusion Matrix\")\n",
    "sns.heatmap(cm_knn,annot=True,cmap=\"Blues\",fmt=\"d\",cbar=False, annot_kws={\"size\": 24})\n",
    "\n",
    "plt.subplot(2,3,4)\n",
    "plt.title(\"Naive Bayes Confusion Matrix\")\n",
    "sns.heatmap(cm_nb,annot=True,cmap=\"Blues\",fmt=\"d\",cbar=False, annot_kws={\"size\": 24})\n",
    "\n",
    "plt.subplot(2,3,5)\n",
    "plt.title(\"Decision Tree Classifier Confusion Matrix\")\n",
    "sns.heatmap(cm_dtc,annot=True,cmap=\"Blues\",fmt=\"d\",cbar=False, annot_kws={\"size\": 24})\n",
    "\n",
    "plt.subplot(2,3,6)\n",
    "plt.title(\"Random Forest Confusion Matrix\")\n",
    "sns.heatmap(cm_rf,annot=True,cmap=\"Blues\",fmt=\"d\",cbar=False, annot_kws={\"size\": 24})\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9397be34-82d9-4551-a508-e7fb1da1e6e3",
   "metadata": {},
   "source": [
    "#### Checking the results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b62e7d00-967a-43a5-82e4-127b69247ae8",
   "metadata": {},
   "source": [
    "As we can see, most models are getting a good accuracy at the cost of only getting right the mayority class, which is the non-heart attacked people. Let's see what are the minimum percentage of people adhering to the plan for the different models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "cb542255-57fc-4091-bb2c-f8a506cdccf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "len_x_test = len(x_test)\n",
    "confusion_matrices = {\n",
    "    \"logistic_regresstion\": cm_lr, \n",
    "     \"knn\": cm_knn, \n",
    "     \"naive bates\": cm_nb, \n",
    "     \"decision tree\": cm_dtc, \n",
    "     \"random forest\": cm_rf\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "f8868aec-d412-429b-bccb-35aeed5380f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The minimum adherence to the plan of the algorithm logistic_regresstion is 1.7728252558885194\n",
      "\n",
      "The minimum adherence to the plan of the algorithm knn is 1.6962077922077918\n",
      "\n",
      "The minimum adherence to the plan of the algorithm naive bates is 1.4152349157597206\n",
      "\n",
      "The minimum adherence to the plan of the algorithm decision tree is 1.5325206922498116\n",
      "\n",
      "The minimum adherence to the plan of the algorithm random forest is 1.7487597169045135\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for algorithm, conf_mat in confusion_matrices.items():\n",
    "    min_ad_plan = minimum_adheresplan_prob(\n",
    "            len_x_test, conf_mat[1][0] + conf_mat[1][1], \n",
    "            conf_mat[0][1]/len_x_test, \n",
    "            conf_mat[1][0]/len_x_test, \n",
    "            5*conf_mat[1][1]/len_x_test)\n",
    "    print(f\"The minimum adherence to the plan of the algorithm {algorithm} is {min_ad_plan}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89a81c97-56ab-44fb-b28a-156859414765",
   "metadata": {},
   "source": [
    "As we can see, only with 2 models is even possible to obtain a 20% cost reduction, with a 95% and 55% adherence rate, respectively. Since this is not a great result, we will try to balance the dataset in order to obtain a better model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd1674de-07eb-4a9e-b115-104fbb9dec6b",
   "metadata": {},
   "source": [
    "### Trying to balance the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "04c364fd-7906-4e8c-a59b-3f3f950024a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install imblearn \n",
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "68964b56",
   "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>HeartAttack</th>\n",
       "      <th>HighBP</th>\n",
       "      <th>HighChol</th>\n",
       "      <th>CholCheck</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Smoker</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>PhysActivity</th>\n",
       "      <th>Fruits</th>\n",
       "      <th>Veggies</th>\n",
       "      <th>HvyAlcoholConsump</th>\n",
       "      <th>GenHlth</th>\n",
       "      <th>DiffWalk</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Income</th>\n",
       "      <th>diabetes_1</th>\n",
       "      <th>diabetes_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>253678</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        HeartAttack  HighBP  HighChol  CholCheck   BMI  Smoker  Stroke  \\\n",
       "253675          0.0     1.0       1.0        1.0  45.0     0.0     0.0   \n",
       "253676          0.0     1.0       1.0        1.0  18.0     0.0     0.0   \n",
       "253677          0.0     0.0       0.0        1.0  28.0     0.0     0.0   \n",
       "253678          0.0     1.0       0.0        1.0  23.0     0.0     0.0   \n",
       "253679          1.0     1.0       1.0        1.0  25.0     0.0     0.0   \n",
       "\n",
       "        PhysActivity  Fruits  Veggies  HvyAlcoholConsump  GenHlth  DiffWalk  \\\n",
       "253675           0.0     1.0      1.0                0.0      3.0       0.0   \n",
       "253676           0.0     0.0      0.0                0.0      4.0       1.0   \n",
       "253677           1.0     1.0      0.0                0.0      1.0       0.0   \n",
       "253678           0.0     1.0      1.0                0.0      3.0       0.0   \n",
       "253679           1.0     1.0      0.0                0.0      2.0       0.0   \n",
       "\n",
       "        Sex   Age  Education  Income  diabetes_1  diabetes_2  \n",
       "253675  1.0   5.0        6.0     7.0         0.0         0.0  \n",
       "253676  0.0  11.0        2.0     4.0         0.0         1.0  \n",
       "253677  0.0   2.0        5.0     2.0         0.0         0.0  \n",
       "253678  1.0   7.0        5.0     1.0         0.0         0.0  \n",
       "253679  0.0   9.0        6.0     2.0         0.0         1.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv(\"heart_disease.csv\")\n",
    "\n",
    "# The amount of missing values is not considerable enough to do a more precise cleaning\n",
    "df2 = df2.dropna()\n",
    "# Removing negative values from age \n",
    "df2 = df2[df2.Age > 0]\n",
    "# Adding diabetes_1 and diabetes_2\n",
    "df2[\"diabetes_1\"] = df2.apply(lambda row: isDiabetes1(row), axis=1)\n",
    "df2[\"diabetes_2\"] = df2.apply(lambda row: isDiabetes2(row), axis=1)\n",
    "df2 = df2.drop('Diabetes',axis=1)\n",
    "df2 = df2.drop('MentHlth', axis=1)\n",
    "df2 = df2.drop('PhysHlth', axis=1)\n",
    "df2.tail()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2ec4dc2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HighBP               202922\n",
      "HighChol             202922\n",
      "CholCheck            202922\n",
      "BMI                  202922\n",
      "Smoker               202922\n",
      "Stroke               202922\n",
      "PhysActivity         202922\n",
      "Fruits               202922\n",
      "Veggies              202922\n",
      "HvyAlcoholConsump    202922\n",
      "GenHlth              202922\n",
      "DiffWalk             202922\n",
      "Sex                  202922\n",
      "Age                  202922\n",
      "Education            202922\n",
      "Income               202922\n",
      "diabetes_1           202922\n",
      "diabetes_2           202922\n",
      "dtype: int64\n",
      "HighBP               367914\n",
      "HighChol             367914\n",
      "CholCheck            367914\n",
      "BMI                  367914\n",
      "Smoker               367914\n",
      "Stroke               367914\n",
      "PhysActivity         367914\n",
      "Fruits               367914\n",
      "Veggies              367914\n",
      "HvyAlcoholConsump    367914\n",
      "GenHlth              367914\n",
      "DiffWalk             367914\n",
      "Sex                  367914\n",
      "Age                  367914\n",
      "Education            367914\n",
      "Income               367914\n",
      "diabetes_1           367914\n",
      "diabetes_2           367914\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "y = df2.HeartAttack\n",
    "x = df2.drop('HeartAttack', axis=1)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.2,random_state=0)\n",
    "print(x_train.count())\n",
    "sm = SMOTE(sampling_strategy='minority', random_state=7)\n",
    "oversampled_trainX, oversampled_trainY = sm.fit_resample(x_train, y_train)\n",
    "print(oversampled_trainX.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c86f5664",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracies = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "580c8c91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy 75.25%\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(solver='lbfgs', max_iter=1000)\n",
    "\n",
    "lr.fit(oversampled_trainX, oversampled_trainY)\n",
    "acc = lr.score(x_test, y_test)*100\n",
    "\n",
    "accuracies['Logistic Regression'] = acc\n",
    "print(\"Test Accuracy {:.2f}%\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "111b7472",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum KNN Score is 82.52%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors = 2)  # n_neighbors means k\n",
    "knn.fit(oversampled_trainX, oversampled_trainY)\n",
    "prediction = knn.predict(x_test)\n",
    "\n",
    "scoreList = []\n",
    "for i in range(1,20):\n",
    "    knn2 = KNeighborsClassifier(n_neighbors = i)  # n_neighbors means k\n",
    "    knn2.fit(oversampled_trainX, oversampled_trainY)\n",
    "    scoreList.append(knn2.score(x_test, y_test))\n",
    "\n",
    "plt.plot(range(1,20), scoreList)\n",
    "plt.xticks(np.arange(1,20,1))\n",
    "plt.xlabel(\"K value\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.show()\n",
    "\n",
    "acc = max(scoreList)*100\n",
    "accuracies['KNN'] = acc\n",
    "print(\"Maximum KNN Score is {:.2f}%\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "806cd3aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Naive Bayes: 70.85%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "nb = GaussianNB()\n",
    "nb.fit(oversampled_trainX, oversampled_trainY)\n",
    "\n",
    "acc = nb.score(x_test,y_test)*100\n",
    "accuracies['Naive Bayes'] = acc\n",
    "print(\"Accuracy of Naive Bayes: {:.2f}%\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6901967a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Test Accuracy 85.03%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtc = DecisionTreeClassifier()\n",
    "dtc.fit(oversampled_trainX, oversampled_trainY)\n",
    "\n",
    "acc = dtc.score(x_test, y_test)*100\n",
    "accuracies['Decision Tree'] = acc\n",
    "print(\"Decision Tree Test Accuracy {:.2f}%\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e2221b4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Algorithm Accuracy Score : 89.15%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(n_estimators = 1000, random_state = 1)\n",
    "rf.fit(oversampled_trainX, oversampled_trainY)\n",
    "\n",
    "acc = rf.score(x_test,y_test)*100\n",
    "accuracies['Random Forest'] = acc\n",
    "print(\"Random Forest Algorithm Accuracy Score : {:.2f}%\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7664ec9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DDz+s5557zu5oy4cffvi66oc9t2uvAlewYMEC1ahRQ7GxsTKZTGrYsKEOHz6sCRMmqH///jpz5oyWLVumCRMm6N5775V08bSEVq1aXXHOQ4cOyWKxKDQ0VOXLl1fNmjUVGBgo6eJpVmazWZUqVbL98liUadOm6eGHH7Y7wu9a12M6efKkgoODZbVadfbsWUkXf0lt2LChJGnmzJnq2rWr7foT9erV08iRI9WnTx+98cYbysrK0oYNG5SSkqKAgABJ0tixY9WpU6dC++rSpYt69uxpe/zqq6/qhRdeULdu3SRJderU0csvv6x33nlHL7300lWfk0OHDsnT01Nt27aVl5eXatWqJX9//yL/xn379unLL7/UwoUL1aJFC0kXQ5m2bdtqzZo1euihhyRdPLU7Li5Od955pySpd+/eJXrNTsBIX331lb744gvNnj3b9rrzV02bNtVDDz2kiRMnFvol93Imk0mvvPKK+vXrp2effdbWEwD+z/3336+77rpL77//vt58881Cy6tXr66+ffvaHvfp00dr167VqlWrFBQUVGh9X19f+fn5acWKFerfv78kacWKFQoICFD9+vUlSR9++KGGDRtme4+tU6eOfv75Zy1atMj2XlqU//73vwoODpYknTlzRj4+PkpMTLS7Ft2LL75o+3ft2rX1yy+/KD093XYphp49e+qNN97Q8OHD5e7urp07d2rHjh1KSEiQJC1cuFBNmzbV4MGDbfO8+eabatOmjfbu3aszZ84oLy9P999/v2rVqiVJtjM1gNKqQYMGtusWfv3119q1a5c2btxoO2rotdde05o1a/Tvf/9bvXr1cuiz+NU+5/7Vhg0blJmZqS+++EJ/+9vfJElvv/22OnfurK1bt9q2s1qtio+Pt12e4ZFHHtHGjRsLhRzFVbduXb366qu2x++9995V+7x69epKTk7WnDlzbK85derU0XfffadFixbp73//+3XVARTHpfe6/Px8nT9/XpLs7k9Qvnx5u96sU6eOMjIytGrVKrtArEqVKoqNjZXZbFbDhg3Vpk0bbdy4UU888YT27t2rr776SosXL7a9l48bN85ue0f69c0335SXl5caNWqkli1bau/evUpKSlK5cuXUoEEDJSUladOmTWrevPkV/+5du3bZ+k2SGjZsqJSUFK1YsULnz5/X+PHjVbFiRUlSbGysoqOjNWTIEFksFklSxYoVNXbsWNvrWkpKikwmk8aNGyeTySTpYpYREhKiTZs2qVmzZjp58qTatWtn+45wKTu4NN+loy1hHMLGW8SePXsUHBxsaz5Juvvuu3XmzBn99ttvOnHihC5cuGD3gaFy5cq2LwtFefDBBzVnzhx17NhRrVq1Ups2bdSuXTu7X2OuZceOHXanTRZHpUqVtHTpUuXl5Wnz5s2aOXOm3bUttm/frl9//VUrVqywjVmtVhUUFCgrK0t79+6Vm5ubmjZtaltet27dIi/E+9dfdrdv365t27bZjmSUZHtzOHv27FWfk9DQUNWsWdO2rFWrVrr//vuLvLbNnj175ObmZvfl7vbbb1f9+vW1Z88e25inp6ddqFKtWjVOI0WZ4efnpz/++EPvv/++AgICrngduIEDB+rhhx/WunXrrnrN11atWqlFixZ67733NHHixJIqGyjThgwZomeeecZ2euHl8vPzNX36dKWnp+vIkSPKzc1Vbm7uVa/B1rVrVy1ZskT9+/eX1WpVWlqannnmGUkXbxZx6NAhjRw5Uq+//rptm7y8PFWuXPmqdbZs2VJvvPGGpIuXFVmwYIGioqL0ySef2IK/VatWac6cOdq/f78tGLz8daRjx44aM2aMVq9erc6dOyslJUUtW7ZU7dq1JV18T//mm2/svvBcsn//foWFhenee+9V165dFRYWprCwMD3wwAM35cL9wPWyWq22z/vbt2/XmTNn1LJlS7t1zp07Zzsi2ZHP4o589t+zZ49q1KhhCy4kqVGjRrrtttv0yy+/2L5z1KpVy65vb/SzbFGf3a/W5ydPntT58+cLvSZeuHBBd91113XXARTHpfe6s2fPKiUlRXv37tXTTz9tt87ChQv1ySefKDs7W+fPn9eFCxcK/SDQqFEjmc1m22MfHx/t2rVL0v99r7y8Nxo2bKjbbrvN9vh6+9VischsNtv9EGixWK7Zw/Xr17c7w/JSaLhnzx75+fnZgkZJatGihQoKCrR3715b2Ojr62vbRrrY5/v377cdpHPJ+fPnbe/n3bt3V9++fW2nez/00EOqVq3aVevEjSFsvEUUdcrPpTGTyWT372ttd8nf/vY3rVq1SuvXr9fGjRsVFxenmTNnat68eSpfvnyx6vLw8Cjun2BTrlw51a1bV9LFF8qjR49q0KBBtmtAFRQUKDw8XH369Cmy5ivdbKKov/XyF7pLc8fExBR5FGSFChWu+px4eXlp6dKl2rRpk9atW6f3339fU6ZMUUpKit2L/ZVquTR++f9Gf/1wd/n/lkBpV716dSUkJCgiIkLPP/+8ZsyYUWTgeOedd+rxxx/XxIkT7a5rWpQhQ4aoV69edkdnAfg/ISEhCgsL07vvvqvu3bvbLZs1a5Zmz56tESNGyM/PT56ennrzzTeveoO0rl27auLEidq+fbvOnTun3377TZ07d5Z08T1TksaMGVPoyMhr3S3X09PT9l4vXTzK+Z577tHixYs1aNAgff/99xo8eLBiYmIUFhamypUra+XKlUpOTrZt4+7urkcffVSpqam6//77lZaWphEjRtiWFxQUqF27dhoyZEih/fv4+MhsNis5OVlbtmzR+vXrNW/ePE2aNEmLFy9WnTp1rlo/4Cx79uyxBeoFBQXy8fHRvHnzCq13KfB35LO4I5/9//qZ9fLxyxUVVN7IZ9m//jhyrT7fvXu3JCkxMVHVq1e3W355mAGUhMvf60aNGqU+ffpoypQpttP809PTFR8fr9dee03BwcGqVKmSZs6cWejyJsX5TlhUP15yvf1qMpmKHLv0/n8l5cuXt3uPv1Ydl+a9pKg+b9q0qSZMmFBouzvuuEPSxSMdL52x8dlnn2ny5MlKTk6+6hGYuDFcs/EW0ahRI2VkZNi9YGzZskWVKlVS9erVVadOHZUvX97uuminTp3Sr7/+etV5PTw81KFDB40aNUpz585VRkaG7VeU8uXLX/OFxtfXVxs3bryBv0x69tlntXPnTq1evVqS5O/vr927d6tu3bqF/nN3d1f9+vWVl5enn376yTbHr7/+qhMnTlxzX/7+/tq7d2+Rc1/64nS15+TSEY6vvvqqli9froMHD+rrr78utJ9GjRopLy/P7o3kjz/+0L59++wO+QbKupo1a2r+/Pn6/fff1bdv3yteFL5///7at2+fVq5cedX5AgMDdf/993NkI3AVr7zyiv7zn/9oy5YtduPfffedOnTooEcffVRNmjRRnTp17G5MVpQaNWooJCREK1as0IoVK3TvvffajjywWCyqXr26Dhw4UOg909GwzmQyyWQy2U4z27Jli2rWrKl+/fopICBA9erVU3Z2dqHtHn/8cW3YsEELFixQXl6e3Y+FTZs21e7du1WrVq1C9V36sdFkMunuu+/WgAEDtGzZMpUvX15r1qxxqHbgZtm4caN27dpl+/9506ZNdfToUZnN5kL/H7/0BdzRz+JX+5x7uUaNGunQoUM6dOiQbeznn3/WyZMnb+pn2Wv1ecOGDeXu7q7s7OxCyy8/ygu4GV566SXNmjVLhw8flnTxfTk4OFi9e/eWv7+/6tata3ed5OJo0KCB8vLy9OOPP9rGfvnlF7vvvqWlXxs1aqSdO3fa3Yhty5YtKleunOrVq3fF7Zo2bapff/1V3t7ehfr48jMp/P399c9//lMff/yxfH19bfd+KE5uAccRNrqYkydPaseOHXb/ZWdn66mnntJvv/2mMWPGaM+ePVqzZo0SEhIUGRmpcuXKycvLS4899pjefvttff3119q9e7dGjBhh+3BflNTUVH3yySfatWuXDhw4oE8//VQeHh6qWbOmpIuHWW/evFmHDx+2u+Pb5V566SWtXLlS77//vvbs2aPMzEyHLwrt5eWlxx9/XO+//76sVquioqL0/fffKy4uTjt27NC+ffv0xRdf2C4s27BhQ4WGhio2NlZbt27VTz/9pNdff10eHh5X/cVHuhh4fPrpp0pISNDu3bu1Z88epaena9KkSdd8Tv7zn/9o7ty52rFjhw4ePKhly5apoKCgyFPV69Wrpw4dOuj111/Xt99+q507d2ro0KGqXr26OnTo4NDzA5R2NWrU0Lx58/Tnn3/queee08mTJwutY7FY9OyzzxZ5dMZfDRo0SF9//fUVj2IGbnV+fn7q2rWr5s+fbzd+5513asOGDdqyZYv27Nmj2NhYuztRXknXrl21cuVKrVq1So888ojdspiYGE2fPl1z5szR3r17lZmZqSVLltgdgViU3Nxc5eTkKCcnR3v27NGYMWN05swZtWvXzlbroUOHtHLlSu3fv19z584tMgRs2LChgoKCNGHCBHXu3NnuKK6nnnpKx48f1+DBg7V161YdOHBA69at0/Dhw5Wfn68ffvhB06ZN07Zt25Sdna3PP/9cv//+uxo0aHDN5wQoaZd65PDhw9q+fbumTZumF198Ue3atdNjjz0m6eJd6Js3b67+/ftr7dq1ysrK0pYtWzRp0iTbndod+Sx+rc/+lwsNDZWfn5+GDBmi7du3a+vWrXr11Vf197//3XbN9JvhWn3u5eWl5557TvHx8Vq6dKn279+vn376SR999JFDN7ICjNCyZUs1atRIiYmJki6+1/34449au3at9u7dq8mTJ9t6t7gaNGigVq1aadSoUfrhhx/0448/atSoUXbvh6WlX7t27Sp3d3cNGzZMu3bt0tdff60xY8bo0Ucftf2QeaXtbr/9dvXr10/ffvutDhw4oE2bNmns2LH67bffdODAAU2cOFEZGRk6ePCg1q1bp3379tnez2vVqqWsrCz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      "text/plain": [
       "<Figure size 1600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = [\"purple\", \"green\", \"orange\", \"magenta\",\"#CFC60E\",\"#0FBBAE\"]\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.figure(figsize=(16,5))\n",
    "plt.yticks(np.arange(0,100,10))\n",
    "plt.ylabel(\"Accuracy %\")\n",
    "plt.xlabel(\"Algorithms\")\n",
    "sns.barplot(x=list(accuracies.keys()), y=list(accuracies.values()), palette=colors)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8c7ae0ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_head_lr = lr.predict(x_test)\n",
    "knn3 = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn3.fit(oversampled_trainX, oversampled_trainY)\n",
    "y_head_knn = knn3.predict(x_test)\n",
    "y_head_nb = nb.predict(x_test)\n",
    "y_head_dtc = dtc.predict(x_test)\n",
    "y_head_rf = rf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "464a71f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm_lr = confusion_matrix(y_test,y_head_lr)\n",
    "cm_knn = confusion_matrix(y_test,y_head_knn)\n",
    "cm_nb = confusion_matrix(y_test,y_head_nb)\n",
    "cm_dtc = confusion_matrix(y_test,y_head_dtc)\n",
    "cm_rf = confusion_matrix(y_test,y_head_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8de2e78e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2400x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(24,12))\n",
    "\n",
    "plt.suptitle(\"Confusion Matrixes\",fontsize=24)\n",
    "plt.subplots_adjust(wspace = 0.4, hspace= 0.4)\n",
    "\n",
    "plt.subplot(2,3,1)\n",
    "plt.title(\"Logistic Regression Confusion Matrix\")\n",
    "sns.heatmap(cm_lr,annot=True,cmap=\"Blues\",fmt=\"d\",cbar=False, annot_kws={\"size\": 20})\n",
    "\n",
    "plt.subplot(2,3,2)\n",
    "plt.title(\"K Nearest Neighbors Confusion Matrix\")\n",
    "sns.heatmap(cm_knn,annot=True,cmap=\"Blues\",fmt=\"d\",cbar=False, annot_kws={\"size\": 20})\n",
    "\n",
    "plt.subplot(2,3,4)\n",
    "plt.title(\"Naive Bayes Confusion Matrix\")\n",
    "sns.heatmap(cm_nb,annot=True,cmap=\"Blues\",fmt=\"d\",cbar=False, annot_kws={\"size\": 20})\n",
    "\n",
    "plt.subplot(2,3,5)\n",
    "plt.title(\"Decision Tree Classifier Confusion Matrix\")\n",
    "sns.heatmap(cm_dtc,annot=True,cmap=\"Blues\",fmt=\"d\",cbar=False, annot_kws={\"size\": 20})\n",
    "\n",
    "plt.subplot(2,3,6)\n",
    "plt.title(\"Random Forest Confusion Matrix\")\n",
    "sns.heatmap(cm_rf,annot=True,cmap=\"Blues\",fmt=\"d\",cbar=False, annot_kws={\"size\": 20})\n",
    "\n",
    "plt.show()\n",
    "plt.savefig('saved_figure.png')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "133b3d28",
   "metadata": {},
   "source": [
    "### Balancing with undersampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "06c88d34",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "281a84be",
   "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>HeartAttack</th>\n",
       "      <th>HighBP</th>\n",
       "      <th>HighChol</th>\n",
       "      <th>CholCheck</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Smoker</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>PhysActivity</th>\n",
       "      <th>Fruits</th>\n",
       "      <th>Veggies</th>\n",
       "      <th>HvyAlcoholConsump</th>\n",
       "      <th>GenHlth</th>\n",
       "      <th>DiffWalk</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Income</th>\n",
       "      <th>diabetes_1</th>\n",
       "      <th>diabetes_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>253675</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253676</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253677</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253678</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253679</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        HeartAttack  HighBP  HighChol  CholCheck   BMI  Smoker  Stroke  \\\n",
       "253675          0.0     1.0       1.0        1.0  45.0     0.0     0.0   \n",
       "253676          0.0     1.0       1.0        1.0  18.0     0.0     0.0   \n",
       "253677          0.0     0.0       0.0        1.0  28.0     0.0     0.0   \n",
       "253678          0.0     1.0       0.0        1.0  23.0     0.0     0.0   \n",
       "253679          1.0     1.0       1.0        1.0  25.0     0.0     0.0   \n",
       "\n",
       "        PhysActivity  Fruits  Veggies  HvyAlcoholConsump  GenHlth  DiffWalk  \\\n",
       "253675           0.0     1.0      1.0                0.0      3.0       0.0   \n",
       "253676           0.0     0.0      0.0                0.0      4.0       1.0   \n",
       "253677           1.0     1.0      0.0                0.0      1.0       0.0   \n",
       "253678           0.0     1.0      1.0                0.0      3.0       0.0   \n",
       "253679           1.0     1.0      0.0                0.0      2.0       0.0   \n",
       "\n",
       "        Sex   Age  Education  Income  diabetes_1  diabetes_2  \n",
       "253675  1.0   5.0        6.0     7.0         0.0         0.0  \n",
       "253676  0.0  11.0        2.0     4.0         0.0         1.0  \n",
       "253677  0.0   2.0        5.0     2.0         0.0         0.0  \n",
       "253678  1.0   7.0        5.0     1.0         0.0         0.0  \n",
       "253679  0.0   9.0        6.0     2.0         0.0         1.0  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv(\"heart_disease.csv\")\n",
    "\n",
    "# The amount of missing values is not considerable enough to do a more precise cleaning\n",
    "df2 = df2.dropna()\n",
    "# Removing negative values from age \n",
    "df2 = df2[df2.Age > 0]\n",
    "# Adding diabetes_1 and diabetes_2\n",
    "df2[\"diabetes_1\"] = df2.apply(lambda row: isDiabetes1(row), axis=1)\n",
    "df2[\"diabetes_2\"] = df2.apply(lambda row: isDiabetes2(row), axis=1)\n",
    "df2 = df2.drop('Diabetes',axis=1)\n",
    "df2 = df2.drop('MentHlth', axis=1)\n",
    "df2 = df2.drop('PhysHlth', axis=1)\n",
    "df2.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "b36d8d21",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df2.HeartAttack\n",
    "x = df2.drop('HeartAttack', axis=1)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "89b36484",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0.0, 18965), (1.0, 18965)]\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "rus = RandomUnderSampler(random_state=123)\n",
    "x_train_rus, y_train_rus= rus.fit_resample(x_train, y_train)\n",
    "print(sorted(Counter(y_train_rus).items()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "90ee038a",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracies = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "f523bc48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy 75.08%\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(solver='lbfgs', max_iter=1000)\n",
    "\n",
    "lr.fit(x_train_rus, y_train_rus)\n",
    "acc = lr.score(x_test, y_test)*100\n",
    "\n",
    "accuracies['Logistic Regression'] = acc\n",
    "print(\"Test Accuracy {:.2f}%\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "cac5c257",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum KNN Score is 85.20%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors = 2)  # n_neighbors means k\n",
    "knn.fit(x_train_rus, y_train_rus)\n",
    "prediction = knn.predict(x_test)\n",
    "\n",
    "scoreList = []\n",
    "for i in range(1,20):\n",
    "    knn2 = KNeighborsClassifier(n_neighbors = i)  # n_neighbors means k\n",
    "    knn2.fit(x_train_rus, y_train_rus)\n",
    "    scoreList.append(knn2.score(x_test, y_test))\n",
    "\n",
    "plt.plot(range(1,20), scoreList)\n",
    "plt.xticks(np.arange(1,20,1))\n",
    "plt.xlabel(\"K value\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.show()\n",
    "\n",
    "acc = max(scoreList)*100\n",
    "accuracies['KNN'] = acc\n",
    "print(\"Maximum KNN Score is {:.2f}%\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "25bb57bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Naive Bayes: 74.52%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "nb = GaussianNB()\n",
    "nb.fit(x_train_rus, y_train_rus)\n",
    "\n",
    "acc = nb.score(x_test,y_test)*100\n",
    "accuracies['Naive Bayes'] = acc\n",
    "print(\"Accuracy of Naive Bayes: {:.2f}%\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "35a17553",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Test Accuracy 68.12%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtc = DecisionTreeClassifier()\n",
    "dtc.fit(x_train_rus, y_train_rus)\n",
    "\n",
    "acc = dtc.score(x_test, y_test)*100\n",
    "accuracies['Decision Tree'] = acc\n",
    "print(\"Decision Tree Test Accuracy {:.2f}%\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "32e838c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Algorithm Accuracy Score : 72.04%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(n_estimators = 1000, random_state = 1)\n",
    "rf.fit(x_train_rus, y_train_rus)\n",
    "\n",
    "acc = rf.score(x_test,y_test)*100\n",
    "accuracies['Random Forest'] = acc\n",
    "print(\"Random Forest Algorithm Accuracy Score : {:.2f}%\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "4c48656e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = [\"purple\", \"green\", \"orange\", \"magenta\",\"#CFC60E\",\"#0FBBAE\"]\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.figure(figsize=(16,5))\n",
    "plt.yticks(np.arange(0,100,10))\n",
    "plt.ylabel(\"Accuracy %\")\n",
    "plt.xlabel(\"Algorithms\")\n",
    "sns.barplot(x=list(accuracies.keys()), y=list(accuracies.values()), palette=colors)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "9bff7f20",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_head_lr = lr.predict(x_test)\n",
    "knn3 = KNeighborsClassifier(n_neighbors = 2)\n",
    "knn3.fit(x_train_rus, y_train_rus)\n",
    "y_head_knn = knn3.predict(x_test)\n",
    "y_head_nb = nb.predict(x_test)\n",
    "y_head_dtc = dtc.predict(x_test)\n",
    "y_head_rf = rf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "5c267dab",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm_lr = confusion_matrix(y_test,y_head_lr)\n",
    "cm_knn = confusion_matrix(y_test,y_head_knn)\n",
    "cm_nb = confusion_matrix(y_test,y_head_nb)\n",
    "cm_dtc = confusion_matrix(y_test,y_head_dtc)\n",
    "cm_rf = confusion_matrix(y_test,y_head_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "88197ec5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(24,12))\n",
    "\n",
    "plt.suptitle(\"Confusion Matrixes\",fontsize=24)\n",
    "plt.subplots_adjust(wspace = 0.4, hspace= 0.4)\n",
    "\n",
    "plt.subplot(2,3,1)\n",
    "plt.title(\"Logistic Regression Confusion Matrix\")\n",
    "sns.heatmap(cm_lr,annot=True,cmap=\"Blues\",fmt=\"d\",cbar=False, annot_kws={\"size\": 24})\n",
    "\n",
    "plt.subplot(2,3,2)\n",
    "plt.title(\"K Nearest Neighbors Confusion Matrix\")\n",
    "sns.heatmap(cm_knn,annot=True,cmap=\"Blues\",fmt=\"d\",cbar=False, annot_kws={\"size\": 24})\n",
    "\n",
    "plt.subplot(2,3,4)\n",
    "plt.title(\"Naive Bayes Confusion Matrix\")\n",
    "sns.heatmap(cm_nb,annot=True,cmap=\"Blues\",fmt=\"d\",cbar=False, annot_kws={\"size\": 24})\n",
    "\n",
    "plt.subplot(2,3,5)\n",
    "plt.title(\"Decision Tree Classifier Confusion Matrix\")\n",
    "sns.heatmap(cm_dtc,annot=True,cmap=\"Blues\",fmt=\"d\",cbar=False, annot_kws={\"size\": 24})\n",
    "\n",
    "plt.subplot(2,3,6)\n",
    "plt.title(\"Random Forest Confusion Matrix\")\n",
    "sns.heatmap(cm_rf,annot=True,cmap=\"Blues\",fmt=\"d\",cbar=False, annot_kws={\"size\": 24})\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "9fa1fec2",
   "metadata": {},
   "outputs": [],
   "source": [
    "len_x_test = len(x_test)\n",
    "confusion_matrices = {\n",
    "    \"logistic_regresstion\": cm_lr, \n",
    "     \"knn\": cm_knn, \n",
    "     \"naive bates\": cm_nb, \n",
    "     \"decision tree\": cm_dtc, \n",
    "     \"random forest\": cm_rf\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "f617fde5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The minimum adherence to the plan of the algorithm logistic_regresstion is 1.379488617800363\n",
      "\n",
      "The minimum adherence to the plan of the algorithm knn is 1.4997498882289069\n",
      "\n",
      "The minimum adherence to the plan of the algorithm naive bates is 1.3900234223382184\n",
      "\n",
      "The minimum adherence to the plan of the algorithm decision tree is 1.4035162681769844\n",
      "\n",
      "The minimum adherence to the plan of the algorithm random forest is 1.3826966736061514\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for algorithm, conf_mat in confusion_matrices.items():\n",
    "    min_ad_plan = minimum_adheresplan_prob(\n",
    "            len_x_test, conf_mat[1][0] + conf_mat[1][1], \n",
    "            conf_mat[0][1]/len_x_test, \n",
    "            conf_mat[1][0]/len_x_test, \n",
    "            5*conf_mat[1][1]/len_x_test)\n",
    "    print(f\"The minimum adherence to the plan of the algorithm {algorithm} is {min_ad_plan}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94ce2f88-5ff4-47e6-a291-404ffb06d00a",
   "metadata": {},
   "source": [
    "## Random Forest Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "1bcb265f-c90a-4a6f-a97d-0a67b899860d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "55a21f23-d4ce-40f5-aaa8-ae4898ef2fa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import make_scorer\n",
    "\n",
    "def minimize_adherence(y_true, y_pred):\n",
    "    cm = confusion_matrix(y_test,y_head_rf)\n",
    "    heart_attacks = len(y_test[y_test==1])\n",
    "    total_cases = len(y_true)\n",
    "    fpp = cm[0][1]/total_cases\n",
    "    fnp = cm[1][0]/total_cases\n",
    "    tpp = cm[1][1]/total_cases\n",
    "    \n",
    "    adherence =  minimum_adheresplan_prob(total_cases, heart_attacks, fpp, fnp, tpp)\n",
    "    \n",
    "    return adherence if (adherence > 0 and adherence < 1) else -1000\n",
    "\n",
    "adherence_metric_func = make_scorer(minimize_adherence, greater_is_better=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45574c74-74e6-4612-b181-6c5cdb131aab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 4 folds for each of 300 candidates, totalling 1200 fits\n"
     ]
    }
   ],
   "source": [
    "param_grid = {\n",
    "    'max_depth': [8, 10, 12, 14, 16],\n",
    "    'max_features': [3,4,5],\n",
    "    'n_estimators': [50, 100, 150, 200],\n",
    "    'class_weight': ['balanced', 'balanced_subsample', {0:1,1:20}, {0:1,1:50}, {0:1,1:100}]\n",
    "}\n",
    "\n",
    "rf = RandomForestClassifier()\n",
    "\n",
    "grid_search = GridSearchCV(estimator = rf, param_grid = param_grid, \n",
    "                          cv = 4, n_jobs = -1, verbose = 2,\n",
    "                          scoring={'recall': 'recall', \n",
    "                                   'f1_weighted':'f1_weighted', \n",
    "                                   'adherence': adherence_metric_func},\n",
    "                          refit='adherence')\n",
    "\n",
    "grid_search.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c08a6e0f-2dd9-45bc-a050-2c8201bc3d48",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c547e7eb-b0a1-416f-a5ab-176ca064f862",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92840d96-66d8-4777-8f52-b7e0bd120e7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_head_rf = grid_search.best_estimator_.predict(x_test)\n",
    "cm_rf = confusion_matrix(y_test,y_head_rf)\n",
    "cm_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d99a21f-073b-4136-beea-5937d4ea89d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_head_rf = grid_search.best_estimator_.predict(x_test)\n",
    "cm_rf = confusion_matrix(y_test,y_head_rf)\n",
    "min_ad_plan = minimum_adheresplan_prob(\n",
    "            len(x_test), cm_rf[1][0] + cm_rf[1][1], \n",
    "            cm_rf[0][1]/len(x_test), \n",
    "            cm_rf[1][0]/len(x_test), \n",
    "            cm_rf[1][1]/len(x_test))\n",
    "print(f\"The minimum adherence to the plan is {min_ad_plan}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45676890-2379-44ce-acb8-80c1f4d148e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "minimize_adherence(y_test, y_head_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abc29984-1c18-4ed7-9bf9-f32eda99b0eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, recall_score, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52a07b9d-e101-4ae8-a0b5-2cfeabb4f5d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_score(y_test, y_head_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97e3b5db-a577-4907-bd69-c9bcc8b17c2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "recall_score(y_test, y_head_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9474ad6c-801b-4dc9-aaf0-8376977d42dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "f1_score(y_test, y_head_rf, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fa90a29-e455-484e-ab0a-72d3c5d740e5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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