stroke-pred-logreg-vs-randomforest-r.ipynb

{
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   },
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
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   "id": "arabic-schema",
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   },
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "── \u001b[1mAttaching packages\u001b[22m ─────────────────────────────────────── tidyverse 1.3.0 ──\n",
      "\n",
      "\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.3     \u001b[32m✔\u001b[39m \u001b[34mpurrr  \u001b[39m 0.3.4\n",
      "\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.1.0     \u001b[32m✔\u001b[39m \u001b[34mdplyr  \u001b[39m 1.0.5\n",
      "\u001b[32m✔\u001b[39m \u001b[34mtidyr  \u001b[39m 1.1.3     \u001b[32m✔\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n",
      "\u001b[32m✔\u001b[39m \u001b[34mreadr  \u001b[39m 1.4.0     \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 0.5.0\n",
      "\n",
      "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n",
      "\n",
      "Loaded ROSE 0.0-3\n",
      "\n",
      "\n",
      "corrplot 0.84 loaded\n",
      "\n",
      "Loading required package: lattice\n",
      "\n",
      "\n",
      "Attaching package: ‘caret’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:purrr’:\n",
      "\n",
      "    lift\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:httr’:\n",
      "\n",
      "    progress\n",
      "\n",
      "\n",
      "randomForest 4.6-14\n",
      "\n",
      "Type rfNews() to see new features/changes/bug fixes.\n",
      "\n",
      "\n",
      "Attaching package: ‘randomForest’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:dplyr’:\n",
      "\n",
      "    combine\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:ggplot2’:\n",
      "\n",
      "    margin\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(tidyverse)\n",
    "library(ROSE)\n",
    "library(corrplot)\n",
    "library(caret)\n",
    "library(randomForest)\n",
    "set.seed(101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "potential-porter",
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "'data.frame':\t5110 obs. of  12 variables:\n",
      " $ id               : int  9046 51676 31112 60182 1665 56669 53882 10434 27419 60491 ...\n",
      " $ gender           : Factor w/ 3 levels \"Female\",\"Male\",..: 2 1 2 1 1 2 2 1 1 1 ...\n",
      " $ age              : num  67 61 80 49 79 81 74 69 59 78 ...\n",
      " $ hypertension     : int  0 0 0 0 1 0 1 0 0 0 ...\n",
      " $ heart_disease    : int  1 0 1 0 0 0 1 0 0 0 ...\n",
      " $ ever_married     : Factor w/ 2 levels \"No\",\"Yes\": 2 2 2 2 2 2 2 1 2 2 ...\n",
      " $ work_type        : Factor w/ 5 levels \"children\",\"Govt_job\",..: 4 5 4 4 5 4 4 4 4 4 ...\n",
      " $ Residence_type   : Factor w/ 2 levels \"Rural\",\"Urban\": 2 1 1 2 1 2 1 2 1 2 ...\n",
      " $ avg_glucose_level: num  229 202 106 171 174 ...\n",
      " $ bmi              : Factor w/ 419 levels \"10.3\",\"11.3\",..: 240 419 199 218 114 164 148 102 419 116 ...\n",
      " $ smoking_status   : Factor w/ 4 levels \"formerly smoked\",..: 1 2 2 3 2 1 2 2 4 4 ...\n",
      " $ stroke           : int  1 1 1 1 1 1 1 1 1 1 ...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "'null data0: FALSE'"
      ],
      "text/latex": [
       "'null data0: FALSE'"
      ],
      "text/markdown": [
       "'null data0: FALSE'"
      ],
      "text/plain": [
       "[1] \"null data0: FALSE\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'Amount of Strokes: 249'"
      ],
      "text/latex": [
       "'Amount of Strokes: 249'"
      ],
      "text/markdown": [
       "'Amount of Strokes: 249'"
      ],
      "text/plain": [
       "[1] \"Amount of Strokes: 249\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#load\n",
    "data <- read.csv(\"../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv\")\n",
    "#see structure\n",
    "str(data)\n",
    "#check for nulls\n",
    "paste0(\"null data0: \", is.null(data))\n",
    "#check how many strokes\n",
    "paste0(\"Amount of Strokes: \", sum(data$stroke))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bizarre-business",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "         0          1 \n",
       "0.95127202 0.04872798 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prop.table(table(data$stroke))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "martial-corps",
   "metadata": {
    "papermill": {
     "duration": 0.043831,
     "end_time": "2021-06-14T00:53:54.829973",
     "exception": false,
     "start_time": "2021-06-14T00:53:54.786142",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Data is a lot unbalanced. Results will not be good if this is not taken it into account (that was confirmed during some tests).  <br>\n",
    "Data will be balanced after train/test split.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "automatic-blind",
   "metadata": {
    "papermill": {
     "duration": 0.043879,
     "end_time": "2021-06-14T00:53:54.917802",
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     "start_time": "2021-06-14T00:53:54.873923",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Clean data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "induced-charge",
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    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Female   Male  Other \n",
       "  2994   2115      1 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Table of Strokes (1)"
     ]
    },
    {
     "data": {
      "text/plain": [
       "        \n",
       "            0    1\n",
       "  Female 2853  141\n",
       "  Male   2007  108\n",
       "  Other     1    0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gender to numeric     id gender age hypertension heart_disease ever_married     work_type\n",
      "1  9046      1  67            0             1          Yes       Private\n",
      "2 51676      0  61            0             0          Yes Self-employed\n",
      "3 31112      1  80            0             1          Yes       Private\n",
      "4 60182      0  49            0             0          Yes       Private\n",
      "5  1665      0  79            1             0          Yes Self-employed\n",
      "6 56669      1  81            0             0          Yes       Private\n",
      "  Residence_type avg_glucose_level  bmi  smoking_status stroke\n",
      "1          Urban            228.69 36.6 formerly smoked      1\n",
      "2          Rural            202.21  N/A    never smoked      1\n",
      "3          Rural            105.92 32.5    never smoked      1\n",
      "4          Urban            171.23 34.4          smokes      1\n",
      "5          Rural            174.12   24    never smoked      1\n",
      "6          Urban            186.21   29 formerly smoked      1\n"
     ]
    }
   ],
   "source": [
    "table(factor(data$gender))\n",
    "\n",
    "cat(\"\\nTable of Strokes (1)\")\n",
    "table(data$gender, data$stroke)\n",
    "\n",
    "cat(\"\\nGender to numeric\")\n",
    "\n",
    "data$gender <- as.character(data$gender)  \n",
    "for (i in 1:length(data$gender)) {\n",
    "    if (data$gender[i] == \"Male\") {\n",
    "        data$gender[i] <- 1\n",
    "    } \n",
    "    else if (data$gender[i] == \"Female\") {\n",
    "        data$gender[i] <- 0\n",
    "    } \n",
    "    else {    #replace other with NA...or something in between\n",
    "        data$gender[i] <- 0.5\n",
    "    }\n",
    "}\n",
    "data$gender <- as.numeric(data$gender)\n",
    "print(head(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "treated-baseline",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  No  Yes \n",
       "1757 3353 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Table of Strokes"
     ]
    },
    {
     "data": {
      "text/plain": [
       "     \n",
       "         0    1\n",
       "  No  1728   29\n",
       "  Yes 3133  220"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obviously, ever been married is much worse to have a Stroke!....\n",
      "....probably just because it corresponds to someone older :) \n",
      "\n",
      "Ever_married to binary"
     ]
    }
   ],
   "source": [
    "table(factor(data$ever_married))\n",
    "cat(\"\\nTable of Strokes\")\n",
    "table(data$ever_married, data$stroke)\n",
    "cat(\"Obviously, ever been married is much worse to have a Stroke!....\\n\")\n",
    "cat(\"....probably just because it corresponds to someone older :) \\n\" )\n",
    "\n",
    "cat(\"\\nEver_married to binary\")\n",
    "data$ever_married <- as.numeric(data$ever_married) - 1 #to become binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "occupied-trouble",
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    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "     children      Govt_job  Never_worked       Private Self-employed \n",
       "          687           657            22          2925           819 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Table of Strokes (1)"
     ]
    },
    {
     "data": {
      "text/plain": [
       "               \n",
       "                   0    1\n",
       "  children       685    2\n",
       "  Govt_job       624   33\n",
       "  Never_worked    22    0\n",
       "  Private       2776  149\n",
       "  Self-employed  754   65"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Work_type to ... Let's separate in children and never worked (which could also be very young people) - make a new variable 'young' to them\n",
      "The remaing are separated in new dummy variables, wither work for others or self-employed (which seems more stressful)     id gender age hypertension heart_disease ever_married     work_type\n",
      "1  9046      1  67            0             1            1       Private\n",
      "2 51676      0  61            0             0            1 Self-employed\n",
      "3 31112      1  80            0             1            1       Private\n",
      "4 60182      0  49            0             0            1       Private\n",
      "5  1665      0  79            1             0            1 Self-employed\n",
      "6 56669      1  81            0             0            1       Private\n",
      "  Residence_type avg_glucose_level  bmi  smoking_status stroke young\n",
      "1          Urban            228.69 36.6 formerly smoked      1     0\n",
      "2          Rural            202.21  N/A    never smoked      1     0\n",
      "3          Rural            105.92 32.5    never smoked      1     0\n",
      "4          Urban            171.23 34.4          smokes      1     0\n",
      "5          Rural            174.12   24    never smoked      1     0\n",
      "6          Urban            186.21   29 formerly smoked      1     0\n",
      "  work_others work_self\n",
      "1           0         0\n",
      "2           0         1\n",
      "3           0         0\n",
      "4           0         0\n",
      "5           0         1\n",
      "6           0         0\n"
     ]
    }
   ],
   "source": [
    "#work_type is more difficult.... must transform each type to binary... start with children\n",
    "table(factor(data$work_type))\n",
    "cat(\"\\nTable of Strokes (1)\")\n",
    "table(data$work_type, data$stroke)\n",
    "\n",
    "cat(\"\\nWork_type to ... Let's separate in children and never worked (which could also be very young people) - make a new variable 'young' to them\\n\")\n",
    "cat(\"The remaing are separated in new dummy variables, wither work for others or self-employed (which seems more stressful)\")\n",
    "\n",
    "data$work_type <- as.character(data$work_type)\n",
    "for (i in 1:length(data$id)) {\n",
    "    if (data$work_type[i] == \"children\" || data$work_type[i] == \"Never_worked\" ) {\n",
    "        data$young[i] <- 1\n",
    "    }\n",
    "    else {\n",
    "        data$young[i] <- 0\n",
    "    }      \n",
    "}\n",
    "\n",
    "#data$work_type <- as.character(data$work_type)\n",
    "for (i in 1:length(data$id)) {\n",
    "    if (data$work_type[i] == \"Govt_job\" || data$work_type[i] == \"Private Self\" ) { \n",
    "        data$work_others[i] <- 1\n",
    "    }\n",
    "    else {\n",
    "        data$work_others[i] <- 0\n",
    "    }      \n",
    "}\n",
    "\n",
    "for (i in 1:length(data$id)) {\n",
    "    if (data$work_type[i] == \"Self-employed\" ) { \n",
    "        data$work_self[i] <- 1\n",
    "    }\n",
    "    else {\n",
    "        data$work_self[i] <- 0\n",
    "    }      \n",
    "}\n",
    "print(head(data))"
   ]
  },
  {
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   "execution_count": 7,
   "id": "certified-things",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Rural Urban \n",
       " 2514  2596 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Table of Strokes (1)"
     ]
    },
    {
     "data": {
      "text/plain": [
       "       \n",
       "           0    1\n",
       "  Rural 2400  114\n",
       "  Urban 2461  135"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Residence_type to binary"
     ]
    }
   ],
   "source": [
    "table(factor(data$Residence_type))\n",
    "\n",
    "cat(\"\\nTable of Strokes (1)\")\n",
    "table(data$Residence_type, data$stroke)\n",
    "\n",
    "cat(\"\\nResidence_type to binary\")\n",
    "data$Residence_type <- as.numeric(data$Residence_type) - 1 #to become binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "little-outside",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "bmi to numeric"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in eval(expr, envir, enclos):\n",
      "“NAs introduced by coercion”\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "replace na with mean value"
     ]
    },
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "  10.30   23.80   28.40   28.89   32.80   97.60 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat(\"\\nbmi to numeric\")\n",
    "#bmi is tricky in R... need to convert level (not the factor) to numeric\n",
    "#levels(data$bmi)\n",
    "data$bmi <- as.numeric(levels(data$bmi))[data$bmi]\n",
    "cat(\"\\nreplace na with mean value\")\n",
    "data$bmi[is.na(data$bmi)] <- mean(data$bmi, na.rm = TRUE)\n",
    "summary(data$bmi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cardiac-routine",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-14T00:53:56.179450Z",
     "iopub.status.busy": "2021-06-14T00:53:56.178736Z",
     "iopub.status.idle": "2021-06-14T00:53:56.352399Z",
     "shell.execute_reply": "2021-06-14T00:53:56.351672Z"
    },
    "papermill": {
     "duration": 0.211824,
     "end_time": "2021-06-14T00:53:56.352523",
     "exception": false,
     "start_time": "2021-06-14T00:53:56.140699",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "formerly smoked    never smoked          smokes         Unknown \n",
       "            885            1892             789            1544 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Table of Strokes (1)"
     ]
    },
    {
     "data": {
      "text/plain": [
       "                 \n",
       "                     0    1\n",
       "  formerly smoked  815   70\n",
       "  never smoked    1802   90\n",
       "  smokes           747   42\n",
       "  Unknown         1497   47"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Smoking_status to ....never unknown what is unkonown... There are several unknown, so what to do?..\n",
      "Assume unknown is somewhere between never smoked and formerly smoked... give them 10.."
     ]
    }
   ],
   "source": [
    "table(factor(data$smoking_status))\n",
    "\n",
    "cat(\"\\nTable of Strokes (1)\")\n",
    "table(data$smoking_status, data$stroke)\n",
    "\n",
    "cat(\"\\nSmoking_status to ....never unknown what is unkonown... There are several unknown, so what to do?..\")\n",
    "\n",
    "\n",
    "cat(\"\\nAssume unknown is somewhere between never smoked and formerly smoked... give them 10..\")\n",
    "data$smoking_status <- as.character(data$smoking_status)\n",
    "for (i in 1:length(data$id)) {\n",
    "    if (data$smoking_status[i] == \"Unknown\") {\n",
    "        data$smoking_status[i] <- 10\n",
    "    } \n",
    "#never smoked is 0\n",
    "    else if (data$smoking_status[i] == \"never smoked\") { \n",
    "        data$smoking_status[i] <- 0\n",
    "    } \n",
    "#formerly smoked is 20\n",
    "    else if (data$smoking_status[i] == \"formerly smoked\") {\n",
    "        data$smoking_status[i] <- 30\n",
    "    } \n",
    "#smokes is 30\n",
    "    else if (data$smoking_status[i] == \"smokes\") {\n",
    "        data$smoking_status[i] <- 50\n",
    "    } \n",
    "}\n",
    "data$smoking_status <- as.numeric(data$smoking_status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "mediterranean-mounting",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-14T00:53:56.431690Z",
     "iopub.status.busy": "2021-06-14T00:53:56.430344Z",
     "iopub.status.idle": "2021-06-14T00:53:56.475762Z",
     "shell.execute_reply": "2021-06-14T00:53:56.474479Z"
    },
    "papermill": {
     "duration": 0.085744,
     "end_time": "2021-06-14T00:53:56.475960",
     "exception": false,
     "start_time": "2021-06-14T00:53:56.390216",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "5110"
      ],
      "text/latex": [
       "5110"
      ],
      "text/markdown": [
       "5110"
      ],
      "text/plain": [
       "[1] 5110"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "drop unused columns'data.frame':\t5110 obs. of  13 variables:\n",
      " $ gender           : num  1 0 1 0 0 1 1 0 0 0 ...\n",
      " $ age              : num  67 61 80 49 79 81 74 69 59 78 ...\n",
      " $ hypertension     : int  0 0 0 0 1 0 1 0 0 0 ...\n",
      " $ heart_disease    : int  1 0 1 0 0 0 1 0 0 0 ...\n",
      " $ ever_married     : num  1 1 1 1 1 1 1 0 1 1 ...\n",
      " $ Residence_type   : num  1 0 0 1 0 1 0 1 0 1 ...\n",
      " $ avg_glucose_level: num  229 202 106 171 174 ...\n",
      " $ bmi              : num  36.6 28.9 32.5 34.4 24 ...\n",
      " $ smoking_status   : num  30 0 0 50 0 30 0 0 10 10 ...\n",
      " $ stroke           : int  1 1 1 1 1 1 1 1 1 1 ...\n",
      " $ young            : num  0 0 0 0 0 0 0 0 0 0 ...\n",
      " $ work_others      : num  0 0 0 0 0 0 0 0 0 0 ...\n",
      " $ work_self        : num  0 1 0 0 1 0 0 0 0 0 ...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>gender</dt><dd>FALSE</dd><dt>age</dt><dd>FALSE</dd><dt>hypertension</dt><dd>FALSE</dd><dt>heart_disease</dt><dd>FALSE</dd><dt>ever_married</dt><dd>FALSE</dd><dt>Residence_type</dt><dd>FALSE</dd><dt>avg_glucose_level</dt><dd>FALSE</dd><dt>bmi</dt><dd>FALSE</dd><dt>smoking_status</dt><dd>FALSE</dd><dt>stroke</dt><dd>FALSE</dd><dt>young</dt><dd>FALSE</dd><dt>work_others</dt><dd>FALSE</dd><dt>work_self</dt><dd>FALSE</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[gender] FALSE\n",
       "\\item[age] FALSE\n",
       "\\item[hypertension] FALSE\n",
       "\\item[heart\\textbackslash{}\\_disease] FALSE\n",
       "\\item[ever\\textbackslash{}\\_married] FALSE\n",
       "\\item[Residence\\textbackslash{}\\_type] FALSE\n",
       "\\item[avg\\textbackslash{}\\_glucose\\textbackslash{}\\_level] FALSE\n",
       "\\item[bmi] FALSE\n",
       "\\item[smoking\\textbackslash{}\\_status] FALSE\n",
       "\\item[stroke] FALSE\n",
       "\\item[young] FALSE\n",
       "\\item[work\\textbackslash{}\\_others] FALSE\n",
       "\\item[work\\textbackslash{}\\_self] FALSE\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "gender\n",
       ":   FALSEage\n",
       ":   FALSEhypertension\n",
       ":   FALSEheart_disease\n",
       ":   FALSEever_married\n",
       ":   FALSEResidence_type\n",
       ":   FALSEavg_glucose_level\n",
       ":   FALSEbmi\n",
       ":   FALSEsmoking_status\n",
       ":   FALSEstroke\n",
       ":   FALSEyoung\n",
       ":   FALSEwork_others\n",
       ":   FALSEwork_self\n",
       ":   FALSE\n",
       "\n"
      ],
      "text/plain": [
       "           gender               age      hypertension     heart_disease \n",
       "            FALSE             FALSE             FALSE             FALSE \n",
       "     ever_married    Residence_type avg_glucose_level               bmi \n",
       "            FALSE             FALSE             FALSE             FALSE \n",
       "   smoking_status            stroke             young       work_others \n",
       "            FALSE             FALSE             FALSE             FALSE \n",
       "        work_self \n",
       "            FALSE "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#confirm changes\n",
    "length(data$id)\n",
    "for (i in 1:length(data$id)) {\n",
    "    if (data$stroke[i] >1) {\n",
    "        print(\"wtf!?\")\n",
    "    } \n",
    "}\n",
    "cat(\"drop unused columns\")\n",
    "data <- data[ -c(1,7) ]\n",
    "str(data)\n",
    "apply(data, 2, function(x) any(is.na(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "brilliant-fellow",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-14T00:53:56.557161Z",
     "iopub.status.busy": "2021-06-14T00:53:56.555966Z",
     "iopub.status.idle": "2021-06-14T00:53:56.927622Z",
     "shell.execute_reply": "2021-06-14T00:53:56.925658Z"
    },
    "papermill": {
     "duration": 0.413338,
     "end_time": "2021-06-14T00:53:56.927754",
     "exception": false,
     "start_time": "2021-06-14T00:53:56.514416",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "check the correlations"
     ]
    },
    {
     "data": {
      "image/png": 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i4TKLH2/R3UmgXxGob2ac2BHYx8iXkr2XGxmZLJRO/hTH1d26oOyC6g0yDrY+la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OPFhPzd5kwGUVRmSnyJTWh6SwpEiKYuE5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\nyyp8O4vK9pVVllcE/KFwOCIBEEXB5bDnZbs7tsvu1qldQa7HCtXmwrSze63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40Skgq6qR0jNBk8K8BiN2iqohHkoBc4PTUQOS3FOlVNXr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\nDHZEREREAfH/NzJBLucvHVAAAAAASUVORK5CYII="
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat(\"check the correlations\")\n",
    "correlations <- cor(data, method = \"pearson\", use = \"complete.obs\")\n",
    "corrplot(correlations, method=\"circle\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acoustic-operator",
   "metadata": {
    "papermill": {
     "duration": 0.040413,
     "end_time": "2021-06-14T00:53:57.009430",
     "exception": false,
     "start_time": "2021-06-14T00:53:56.969017",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "\"Young\" are clearly correlated with othesr...  \"ever married\" also a some... let's not consider them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "champion-neighborhood",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-14T00:53:57.095426Z",
     "iopub.status.busy": "2021-06-14T00:53:57.094315Z",
     "iopub.status.idle": "2021-06-14T00:53:57.134082Z",
     "shell.execute_reply": "2021-06-14T00:53:57.132488Z"
    },
    "papermill": {
     "duration": 0.084141,
     "end_time": "2021-06-14T00:53:57.134209",
     "exception": false,
     "start_time": "2021-06-14T00:53:57.050068",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        age hypertension avg_glucose_level  work_self stroke\n",
      "1 1.0513314   -0.3285697       2.706110617 -0.4368378      1\n",
      "2 0.7859932   -0.3285697       2.121350940  2.2887313      1\n",
      "3 1.6262309   -0.3285697      -0.005027809 -0.4368378      1\n",
      "4 0.2553167   -0.3285697       1.437217451 -0.4368378      1\n",
      "5 1.5820079    3.0428986       1.501037522  2.2887313      1\n",
      "6 1.6704540   -0.3285697       1.768021830 -0.4368378      1\n"
     ]
    }
   ],
   "source": [
    "data1 <- data[,c(\"gender\",\"age\",\"hypertension\",\"heart_disease\",\"Residence_type\",\"avg_glucose_level\",\"bmi\",\"smoking_status\",\"work_others\",\"work_self\",\"stroke\")]\n",
    "#after first run analysis, the focus was decided on this variables\n",
    "data2 <- data[,c(\"age\",\"hypertension\",\"avg_glucose_level\",\"work_self\",\"stroke\")]\n",
    "\n",
    "#normalize data (excep stroke)\n",
    "#data3 <- scale(data1[,c(-11)])\n",
    "data_scaled0 <- scale(data2[,c(-5)])\n",
    "data_scaled <- cbind(data_scaled0, data$stroke)\n",
    "\n",
    "data_scaled <- as.data.frame(data_scaled)\n",
    "data_scaled <- rename(data_scaled,\"stroke\" =  V5)\n",
    "print(head(data_scaled))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "colored-suicide",
   "metadata": {
    "papermill": {
     "duration": 0.04095,
     "end_time": "2021-06-14T00:53:57.216180",
     "exception": false,
     "start_time": "2021-06-14T00:53:57.175230",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Nice! Data is great, let's implement ML - be carefull, the stroke is binary!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "honey-privacy",
   "metadata": {
    "papermill": {
     "duration": 0.040754,
     "end_time": "2021-06-14T00:53:57.298679",
     "exception": false,
     "start_time": "2021-06-14T00:53:57.257925",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Let's try to use a logistic regression first:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "superior-juvenile",
   "metadata": {
    "papermill": {
     "duration": 0.040798,
     "end_time": "2021-06-14T00:53:57.380657",
     "exception": false,
     "start_time": "2021-06-14T00:53:57.339859",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Train Test Split and Scale**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "lucky-tuesday",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-14T00:53:57.466260Z",
     "iopub.status.busy": "2021-06-14T00:53:57.465086Z",
     "iopub.status.idle": "2021-06-14T00:53:57.519906Z",
     "shell.execute_reply": "2021-06-14T00:53:57.518524Z"
    },
    "papermill": {
     "duration": 0.098859,
     "end_time": "2021-06-14T00:53:57.520011",
     "exception": false,
     "start_time": "2021-06-14T00:53:57.421152",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Test split - using caret\n"
     ]
    },
    {
     "data": {
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       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>age</th><th scope=col>hypertension</th><th scope=col>avg_glucose_level</th><th scope=col>work_self</th><th scope=col>stroke</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1.0513314</td><td>-0.3285697</td><td> 2.7061106</td><td>-0.4368378</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>0.7859932</td><td>-0.3285697</td><td> 2.1213509</td><td> 2.2887313</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>0.2553167</td><td>-0.3285697</td><td> 1.4372175</td><td>-0.4368378</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>1.5820079</td><td> 3.0428986</td><td> 1.5010375</td><td> 2.2887313</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>1.6704540</td><td>-0.3285697</td><td> 1.7680218</td><td>-0.4368378</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>8</th><td>1.1397775</td><td>-0.3285697</td><td>-0.2596456</td><td>-0.4368378</td><td>1</td></tr>\n",
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      "text/latex": [
       "A data.frame: 6 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & age & hypertension & avg\\_glucose\\_level & work\\_self & stroke\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 1.0513314 & -0.3285697 &  2.7061106 & -0.4368378 & 1\\\\\n",
       "\t2 & 0.7859932 & -0.3285697 &  2.1213509 &  2.2887313 & 1\\\\\n",
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       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 5\n",
       "\n",
       "| <!--/--> | age &lt;dbl&gt; | hypertension &lt;dbl&gt; | avg_glucose_level &lt;dbl&gt; | work_self &lt;dbl&gt; | stroke &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| 1 | 1.0513314 | -0.3285697 |  2.7061106 | -0.4368378 | 1 |\n",
       "| 2 | 0.7859932 | -0.3285697 |  2.1213509 |  2.2887313 | 1 |\n",
       "| 4 | 0.2553167 | -0.3285697 |  1.4372175 | -0.4368378 | 1 |\n",
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       "| 6 | 1.6704540 | -0.3285697 |  1.7680218 | -0.4368378 | 1 |\n",
       "| 8 | 1.1397775 | -0.3285697 | -0.2596456 | -0.4368378 | 1 |\n",
       "\n"
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      "text/plain": [
       "  age       hypertension avg_glucose_level work_self  stroke\n",
       "1 1.0513314 -0.3285697    2.7061106        -0.4368378 1     \n",
       "2 0.7859932 -0.3285697    2.1213509         2.2887313 1     \n",
       "4 0.2553167 -0.3285697    1.4372175        -0.4368378 1     \n",
       "5 1.5820079  3.0428986    1.5010375         2.2887313 1     \n",
       "6 1.6704540 -0.3285697    1.7680218        -0.4368378 1     \n",
       "8 1.1397775 -0.3285697   -0.2596456        -0.4368378 1     "
      ]
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     "text": [
      "'data.frame':\t3577 obs. of  5 variables:\n",
      " $ age              : num  1.051 0.786 0.255 1.582 1.67 ...\n",
      " $ hypertension     : num  -0.329 -0.329 -0.329 3.043 -0.329 ...\n",
      " $ avg_glucose_level: num  2.71 2.12 1.44 1.5 1.77 ...\n",
      " $ work_self        : num  -0.437 2.289 -0.437 2.289 -0.437 ...\n",
      " $ stroke           : num  1 1 1 1 1 1 1 1 1 1 ...\n"
     ]
    },
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       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>age</th><th scope=col>hypertension</th><th scope=col>avg_glucose_level</th><th scope=col>work_self</th><th scope=col>stroke</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
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       "A data.frame: 6 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & age & hypertension & avg\\_glucose\\_level & work\\_self & stroke\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
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       "\t31 & 0.6091010 &  3.0428986 &  2.339310835 & -0.4368378 & 1\\\\\n",
       "\\end{tabular}\n"
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      "text/markdown": [
       "\n",
       "A data.frame: 6 × 5\n",
       "\n",
       "| <!--/--> | age &lt;dbl&gt; | hypertension &lt;dbl&gt; | avg_glucose_level &lt;dbl&gt; | work_self &lt;dbl&gt; | stroke &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| 3 | 1.6262309 | -0.3285697 | -0.005027809 | -0.4368378 | 1 |\n",
       "| 7 | 1.3608927 |  3.0428986 | -0.796264185 | -0.4368378 | 1 |\n",
       "| 12 | 0.7859932 | -0.3285697 |  0.316060019 | -0.4368378 | 1 |\n",
       "| 22 | 0.3879858 |  3.0428986 |  2.807692736 |  2.2887313 | 1 |\n",
       "| 27 | 0.9628853 | -0.3285697 | -0.114118172 | -0.4368378 | 1 |\n",
       "| 31 | 0.6091010 |  3.0428986 |  2.339310835 | -0.4368378 | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "   age       hypertension avg_glucose_level work_self  stroke\n",
       "3  1.6262309 -0.3285697   -0.005027809      -0.4368378 1     \n",
       "7  1.3608927  3.0428986   -0.796264185      -0.4368378 1     \n",
       "12 0.7859932 -0.3285697    0.316060019      -0.4368378 1     \n",
       "22 0.3879858  3.0428986    2.807692736       2.2887313 1     \n",
       "27 0.9628853 -0.3285697   -0.114118172      -0.4368378 1     \n",
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      ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t1533 obs. of  5 variables:\n",
      " $ age              : num  1.626 1.361 0.786 0.388 0.963 ...\n",
      " $ hypertension     : num  -0.329 3.043 -0.329 3.043 -0.329 ...\n",
      " $ avg_glucose_level: num  -0.00503 -0.79626 0.31606 2.80769 -0.11412 ...\n",
      " $ work_self        : num  -0.437 -0.437 -0.437 2.289 -0.437 ...\n",
      " $ stroke           : num  1 1 1 1 1 1 1 1 1 1 ...\n"
     ]
    }
   ],
   "source": [
    "cat(\"Train Test split - using caret\\n\")\n",
    "trainIndex <- createDataPartition(data_scaled$stroke, p = 0.7, \n",
    "                                  list = FALSE, \n",
    "                                  times = 1)\n",
    "dataTrain0 <- data_scaled[ trainIndex,]\n",
    "dataTest  <- data_scaled[-trainIndex,]\n",
    "head(dataTrain0)\n",
    "str(dataTrain0)\n",
    "head(dataTest)\n",
    "str(dataTest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sized-junior",
   "metadata": {
    "papermill": {
     "duration": 0.042557,
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Balance Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "upper-hopkins",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-14T00:53:57.694251Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Balance (train) data by oversampling. From 5% do 30%\n",
      "'data.frame':\t4865 obs. of  5 variables:\n",
      " $ age              : num  0.653 -1.558 -1.292 0.167 1.405 ...\n",
      " $ hypertension     : num  3.043 -0.329 -0.329 -0.329 -0.329 ...\n",
      " $ avg_glucose_level: num  -0.402 0.105 1.217 2.314 3.034 ...\n",
      " $ work_self        : num  -0.437 -0.437 -0.437 -0.437 2.289 ...\n",
      " $ stroke           : num  0 0 0 0 0 0 0 0 0 0 ...\n"
     ]
    }
   ],
   "source": [
    "cat(\"Balance (train) data by oversampling. From 5% do 30%\\n\")\n",
    "dataTrain <- ovun.sample(as.factor(stroke)~.,data = dataTrain0, method = 'over',p = 0.3)$data\n",
    "str(dataTrain)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "animated-policy",
   "metadata": {
    "papermill": {
     "duration": 0.065514,
     "end_time": "2021-06-14T00:53:57.857107",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Run Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eleven-semester",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-14T00:53:57.992339Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "logit_2 <- glm(stroke~., family = binomial,data = dataTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "enhanced-luther",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-14T00:53:58.178750Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = stroke ~ ., family = binomial, data = dataTrain)\n",
       "\n",
       "Deviance Residuals: \n",
       "    Min       1Q   Median       3Q      Max  \n",
       "-2.0157  -0.7053  -0.2954   0.7655   3.1797  \n",
       "\n",
       "Coefficients:\n",
       "                  Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept)       -1.89400    0.06050 -31.308  < 2e-16 ***\n",
       "age                1.61301    0.05948  27.120  < 2e-16 ***\n",
       "hypertension       0.13666    0.02863   4.773 1.81e-06 ***\n",
       "avg_glucose_level  0.20896    0.03017   6.927 4.30e-12 ***\n",
       "work_self         -0.10161    0.03378  -3.008  0.00263 ** \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "(Dispersion parameter for binomial family taken to be 1)\n",
       "\n",
       "    Null deviance: 5949.6  on 4864  degrees of freedom\n",
       "Residual deviance: 4301.2  on 4860  degrees of freedom\n",
       "AIC: 4311.2\n",
       "\n",
       "Number of Fisher Scoring iterations: 5\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. \n",
       "0.005556 0.059791 0.232703 0.300719 0.532186 0.873094 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(logit_2)\n",
    "summary(logit_2$fitted.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "painful-lemon",
   "metadata": {
    "execution": {
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
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      "Predict on Train"
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      "Predict on Test"
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       "  3   7  12  22  27  31  33  39  42  47  51  52  60  62  69  72  74  78  81  83 \n",
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   ],
   "source": [
    "cat(\"Predict on Train\")\n",
    "pred_train0 <- predict(logit_2, type = \"response\")\n",
    "pred_train <- ifelse(pred_train0 >0.25,1,0)\n",
    "sum(pred_train)\n",
    "#how they look\n",
    "length(pred_train)\n",
    "head(pred_train,50)\n",
    "\n",
    "cat(\"Predict on Test\")\n",
    "pred_test0 <- predict(logit_2, newdata = dataTest, type = \"response\")\n",
    "pred_test <- ifelse(pred_test0 >0.25,1,0)\n",
    "sum(pred_test)\n",
    "#how they look\n",
    "length(pred_test)\n",
    "head(pred_test,50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ultimate-driving",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-14T00:53:58.492536Z",
     "iopub.status.busy": "2021-06-14T00:53:58.491275Z",
     "iopub.status.idle": "2021-06-14T00:53:58.608943Z",
     "shell.execute_reply": "2021-06-14T00:53:58.608480Z"
    },
    "papermill": {
     "duration": 0.17179,
     "end_time": "2021-06-14T00:53:58.609038",
     "exception": false,
     "start_time": "2021-06-14T00:53:58.437248",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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19LFS4RsSx/+f1w0cx3G4+IJpSUTTzXdZHW16z9hkkWWjLKyyv248LXyZmi6fW7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lcuUPQFAVTPkKfCl2uTIFIYRhhDbiV/7I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\n0c4qjykqKy5IS0mMigg7tHX1KP+2H317UrxjyPKzE6xfm9jD+MxMml69wXz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vrDlN008v7u3u9uzAzQsjPY1r7H3y17cdp23lixRPQQkq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\nrUijATLYAAAAAElFTkSuQmCC"
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Confusion Matrix\n",
    "a1 <- table(Predictions = pred_train, TrueLabels = dataTrain$stroke)\n",
    "fourfoldplot(a1, color = c(\"#CC6666\", \"#99CC99\"),\n",
    "             conf.level = 0, margin = 1, main = \"Confusion Matrix - Train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "controversial-evening",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-14T00:53:58.710018Z",
     "iopub.status.busy": "2021-06-14T00:53:58.709403Z",
     "iopub.status.idle": "2021-06-14T00:53:58.733562Z",
     "shell.execute_reply": "2021-06-14T00:53:58.732532Z"
    },
    "papermill": {
     "duration": 0.076418,
     "end_time": "2021-06-14T00:53:58.733667",
     "exception": false,
     "start_time": "2021-06-14T00:53:58.657249",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =  0.7280576\n",
      "Precision =  0.5299658\n",
      "Recall =  0.8462064\n",
      "F1 score =  0.6517505"
     ]
    }
   ],
   "source": [
    "#Accuracy\n",
    "acc1 <- ( a1[2,2] + a1[1,1] ) / (length(dataTrain$stroke))\n",
    "cat(\"Accuracy = \",acc1 )\n",
    "#F1\n",
    "Precision1 =  a1[2,2] / ( a1[2,2] + a1[2,1] )\n",
    "cat(\"\\nPrecision = \",Precision1 )\n",
    "Recall1 =  a1[2,2] / ( a1[2,2] + a1[1,2] )\n",
    "cat(\"\\nRecall = \",Recall1 )\n",
    "F1score1  = 2*(Recall1 * Precision1) / (Recall1 + Precision1)\n",
    "cat(\"\\nF1 score = \",F1score1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "based-glossary",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-14T00:53:58.838191Z",
     "iopub.status.busy": "2021-06-14T00:53:58.836409Z",
     "iopub.status.idle": "2021-06-14T00:53:58.923020Z",
     "shell.execute_reply": "2021-06-14T00:53:58.922537Z"
    },
    "papermill": {
     "duration": 0.139454,
     "end_time": "2021-06-14T00:53:58.923115",
     "exception": false,
     "start_time": "2021-06-14T00:53:58.783661",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kze/aT4tqmhfg4/3uEtZbALWtoyf8+syfuC6WISn25Uzb/cVXfM7cKrfjrYhR3r\nWVCn46jNPCl3G4X7p36/4dtp9zupeyaEsDhaJ6Myim9P0/Ss/A/NuMXZgoUifrKRsmToX4eJ\nB4tvxc9645z/Xqph06J/3LRQWPCqb7HoXoL0qNUFZS//ki6np0iQVtCz/anwMh4HACoQgh1A\nVVQ42JWeqkGHwmlJrHDIMO+0U9YRr42yl6Qc7baxPJmR4Mkqt/yYxXmQf9EuNWxlwSEa/3ZZ\n1raxAXMKV1uaYCfif2uUPzizdf9DsvZ8d3aj/M1dCy//2WAn6pv/coCGxZAiV+MK9eKNt9EW\nd1PR68Yv1KvwZ27rdUVW2c8XNBH30a69vGBh9tf/CrbdEZspdcMHY5sbGBgYGBhY1pssa+c/\nITX8t4JDH0zIkt+54CKxyz8h5VgDAPwivBULoCAMHAfdfnfZQVXeg7NDtwyUsUY43S9C3Gq7\nbb+pssy/GVrMPleLyyGE0LRgyb148cK36w6JG2xl47Orusja1rT92ikWmnLKK+7bxz9fZ/HF\n7dU7B8jq5jRnOofD4XA4wuzADzkCWd1KKSfp9NkkyaNmvQ+sU2fJeIqNUl52SFJSbsrVo4nZ\nUnuNW9Va1oE0bDTEDVr0feYuUuhdjfvPk6Ru6Lrv6devX79+/fr57XZZO/8Jwrz4grYep4Tf\nDvr5HXLic+T3BIDKhGAHUL1RLFXHth7L9l+PeXHSWV/eM+wURfnU0Za6KjvhyPtsSX5a4FFL\n3k44urMsJHEk4likuHH6UrS4od9onbWKvBlEff6U+WarVGG774kbqvp9BhrIfEFB3WQ8P5+9\n3GhbGolP9ogbFIu73s1UTk9j53Ua+fevDz2QMrYIxeJOMdOQtTkl7X1bVd3uNvmncKJ/yz+3\nnvnGF5Wy8l9EF3o5Q1aa/d45vyHMFlZUQQBQdngrFqCq8wt8UU9N+o+qiqaesbGxBrdU/0Jj\ncy2tuNJTV1bcWXGDoliH5vgckbufyBTJiHRpryUv2N7JnyreemRT+TWYd29PyKPSVCsWdk0y\n/q2m5bDSb/WLvt6VXLhS1fMwkX3xkhBCcXT6G6geTMgihMT7J5B+1kU6cFTqaLHL+NoqS/XM\n3z0bzzpPCBHmJayc1n/NHL2WHTq3d3N1cXFp6exkpF5Rf2+zlU0K2iklpcnk/A6qZjIDNwBU\nPgQ7gKrOpn79BurSB5YrE5aSsaxVGWGSZ+FpWrR/795S7pCfLblQV3D3U1rfX5sAACAASURB\nVKdR0TFQilBSb1zKnYsVXEfUsDEs04a/IjNMMsQJR03eSHJiDfLfCMkMlzKHB4vzMzOVOc48\nd0tz0fR5a0NScgkhQl7Kw6snH149SQihWEp1nTt59OzZy3OQW8Oi00L8IjbXrKAdmyfvOhwt\nzMgQItgBVEW4FQtQU1CUzHQoSP+Z59JooSTKfH8KrcSLUyzlMh0iI39iBraqvDu85Yv+fpux\n5Itt+WOtEGFOed6R7Dj+r1dxn45sWTaku6uu8vdzp0X8d4+vrls4rb2jWcfRi0PS88rxoFxN\nl4J24Fd5M4XkpX8fGMWknlY51gAAvwjBDgCIhp3kOTCOSp3Sv3uVGrFAvJVt/jNhaa/TpB8g\nnyBL5lxYUhU8MJcVmSW/ZznStJW84SHICS2x87tsSSbWtCvbeyElYimbDvNZcPzyg6SMxEc3\nzq5f6jugW1vz/IFOaFoYcPAvl/qeieX3BJ6ydlvz/Jv1UQHypiPLSblR0B5gWc4nDgC/AsEO\nAIi6haO4Icj9FJFb5itPPfQlN+MiDr2Q3zPh/g35HYqwd5C87ZEedl5+z6S42JiYmJiYmOS8\nXw06hm0k96xzki/Jj020MOPkV8nLsAatDX7xuLKwlPVc3D1nLVrtd+VudFr6q3tnxnetI16V\nGXNl6PHwcjzUMCN1cevTvw/l9Pt84o5kAyU9Dz3cigWoQhDsAIBoWswueLtz0fOv8jsH3Lh+\n7dq1a9euvcm/WOU+0FLcSHo1O5InLxce/ut1mQqrO1PyTF5WwoHHGTJvO4oEKTaWtSwsLCws\nLBZ/LuGqYYmMWo0XN2hRru/DeDk9vz7/I1UgSX5DuprL6Vl6n04d3LVr165duw4cC5aymuI2\natN39+XXQwwlI+2F7vxYLscVGzZFMpxh6oflCbJD7ZE9koMaOK5Sxa8RgKoEP5EAQFhKRksc\n9cXtC8MW5Umf7pUQQmIDfDp26dqtW7fu3Xtm5T8+7+AzRdwQ5sX3+/OWrG2TgtYs+5BapsKM\nnJeJ5yKjRbzxCwJkdYu5OS1dICKEUCzuJPNfvTOoajCgd/41yLOj5+fIijd03qLhR8VNrnbb\nCSbqv3hcscjDSyaJeY+V9QdBsdS66UmGtqHK+tatXHbjJINIC3hRY05+kton4/OedZ/TxW23\n1T3K8egA8OsQ7ACAEELGHJaEs/TPe1wm786VlmYyI68O6Cd5Z9ak9YaW+c97aVh4z7aVvA/7\nYqPH9ANBxbfNirnh0WlR4ZHSSkNJvdG/nrXF7bdbey84I+XqFO/b/e4D/MRtA8dVDaUNDSMs\n2/1l1vqN7cSt9Mh/20zZI21j0f5pbXd9lOTUVit2KZdTvqo9yk7cyMt84bX9qdQ+GZ/+92ek\n5MKkZf7l0nKhajh4natk0JMbE7s/TOEV6UALUqZ2lIQ/Fb3O+9ubEQCoUipoRgsA+BUlzxVb\navKmuv/R7kJzzxs37/PPsQvP3oRl5An52emf3z/btsTHRlXyXi2ba3YhIbvwtukRRzTZ3/+h\n2Kzf9FPXnyamZAjysqI/Ptuz0sdChUMIYbE16uWPD1LKuWLzMl86aUgSJMVS6jJ+6fXAD5k8\noUiYGxvx/szWPxsbquav5f77+YcZTjfYSOKmaevNgrJ8MiJhtnd93YLTMXcZdODc7fjULJqm\ns9MS7p7/d4jr92GctW1HpgtEpdxzYR/+bSvupmW5sND5BhsqSd5goCj20DmrAl5+Ss3MpWk6\nN+Pbp9DH2/767fsfhLLxo3Re4X1ebPJ9DJSej+PlHF2WzNjjBdOFqeg777v8vGCCufDHF4c0\n0S/Yv8/16J/YPwBUKAQ7gKqIkWAnzIvzbv19iNr8bFH0AhhLSW/55ajim7/aP025pPkKJhx8\nOzd/VrFSBjuapr8+21VbpWgZSj9O20BR1Kgdz4pseNbl+7wRWhY2jRrY2zkuKuVBc1PudjQv\neneVpVR01BU1E7dbidlFtv2VYEfT9PO1vYt/dCxO0U+AopQmH3xfZJ+/Huxomv5w8jd2oY9X\nVdfYoUE9c8Mf7nG3//N/P7dzAKhQuBULABIsJZMddz5smuapUejaG03/MMSdfsOexx+9n9/d\novjmjcb88+b0qiZG0t+RZCubzj/0ZPfIkof8Lc6g2cSXr0/3bf7D7F78Qnd1lTVslp0M/ndS\nsyIbtt31u0p+1kyPDn8d8iE+pbQTm3J12159Fzi9X4vCE3+J+N/vylIU5dRn6pMPNzsalvNr\noU5zzl9ePVFP6Ye/n0WCH/4g1ExbrD8Xsn2kffkeWsxu0KYgv1X1tCQXSnO+JbwLCY35miH+\nT7aS/tSNF/z/xtN1AFURZp4AgO8otuZv/5wdP/fJ/oP/Xbl6821ETOLXFEpFy8CkdvNWrr0G\njRzWw1lJ9lU5O0/f5z1H++3bf+78hcCQyMTEr5S6vrmFdYc+Q8Z6T2hurkYI8Vy+3iozjxBi\nXOzqlxxatr3PPPV4cv7giXMXbj58GRefkMaj9PQN6jZx6dS1r7f3ABNps6XpO05/fVXz92Vb\nn7wLT8ikTUxNbRoXnfVLDiWN+ptPB84NvHTg1PlrN+9HxCYkpWSq6RmamNZu496196AxvVrK\nm1f3V3T33RU1YuqxQ0dvBb6NjoqKioqKS+EZ17KysrKyqm3dplv/IX076HDK87WJIhz7+77s\n0t/v3yMn/C69iYiKi09R0zexqGXTqc+g4aNGOtUqnzdFAKDcUXQZn2UGAIByQQszXz++f/v2\n7WfvHQ4dGs10OQCgCBDsAAAAABQEnrEDAAAAUBAIdgAAAAAKAsEOAAAAQEEg2AEAAAAoCAQ7\nAAAAAAWBYAcAAACgIBDsAAAAABQEgh0AAACAgkCwAwAAAFAQCHYAAAAACgLBDgAAAEBBINgB\nAAAAKAgEOwAAAAAFgWAHAAAAoCAQ7AAAAAAUBIIdAAAAgIJAsAMAAABQEAh2AAAAAAoCwQ4A\nAABAQXCYLkDx7dix48yZM0xXAVCjRUZGEkJq167NcB0ANVu/fv0mT57MdBUKjqJpmukaFFy7\ndu3u37+vra3NdCEA1RJN0yKRiJZN3KegUfD/hRuEEIqidHR0mDgDACCEkLS0tDZt2ty5c4fp\nQhQcrthVBiMjo7i4OKarAKiK+Hx+bGxsdHR0TExMbGxsTExMQkJCXFxcfHx84tfE5KRkoVAo\nZ3M2m62irsJV5bI5bK4al81hc5Q4yirK4rUqGiosFosQEuQfpKyknJKSUhmnBADSmJqaMl1C\njYBgBwCVQSQSRUdHh4eHf/r0KSIiIjIyMjIyMiIyIj4uXiQSFemsqq6qbaCtZaplVs9MU09T\nQ0dDXUtdXUtdXVtdVUO14H9cNS5HqVR/iU11nVoB5wQAUOUg2AFA+UtLSwsNDQ0NDX3//v2H\nDx8+fvz48eNHHo9XuI+WvpaeiV4ThyZ6xnp6Jnrahto6hjo6Rjra+tpKXCWmKgcAqNYQ7ADg\nV+Xm5oaEhLx69SokJOT169dvQt7ExsQWrGWz2frm+nbN7YwtjQ1rGRqYGRiYGRiYGyC9AQCU\nOwQ7ACiz9PT0oKCgoKCg4ODg4ODg9+/fCwQC8SquKtfE2sSlsYt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abW2uqUwIEebFdXcZ+zQuu3ifz3f3tR3hJ25r1hqxzK5Ue66ORCLR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\nt/edX5R0eu+Ba3cDg0OjsnNzi3hET7+FUat2zv36uUyYObKb7IFaAJ8Ae3t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5y9qIx8OGbnVZcuhpzWOLU/xcBq0RiWRnRmlYGh0Pu1nS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\nQmCC"
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Confusion Matrix\n",
    "b1 <- table(Predictions = pred_test, TrueLabels = dataTest$stroke)\n",
    "fourfoldplot(b1, color = c(\"#CC6666\", \"#99CC99\"),\n",
    "             conf.level = 0, margin = 1, main = \"Confusion Matrix - Test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "herbal-encoding",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-14T00:53:59.028728Z",
     "iopub.status.busy": "2021-06-14T00:53:59.027431Z",
     "iopub.status.idle": "2021-06-14T00:53:59.054677Z",
     "shell.execute_reply": "2021-06-14T00:53:59.053536Z"
    },
    "papermill": {
     "duration": 0.08145,
     "end_time": "2021-06-14T00:53:59.054910",
     "exception": false,
     "start_time": "2021-06-14T00:53:58.973460",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =  0.6816699\n",
      "Precision =  0.125\n",
      "Recall =  0.9324324\n",
      "F1 score =  0.2204473"
     ]
    }
   ],
   "source": [
    "#Accuracy\n",
    "acc1 <- ( b1[2,2] + b1[1,1] ) / (length(dataTest$stroke))\n",
    "cat(\"Accuracy = \",acc1 )\n",
    "#F1\n",
    "Precision1 =  b1[2,2] / ( b1[2,2] + b1[2,1] )\n",
    "cat(\"\\nPrecision = \",Precision1 )\n",
    "Recall1 =  b1[2,2] / ( b1[2,2] + b1[1,2] )\n",
    "cat(\"\\nRecall = \",Recall1 )\n",
    "F1score1  = 2*(Recall1 * Precision1) / (Recall1 + Precision1)\n",
    "cat(\"\\nF1 score = \",F1score1 )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "major-mystery",
   "metadata": {
    "papermill": {
     "duration": 0.078329,
     "end_time": "2021-06-14T00:53:59.211220",
     "exception": false,
     "start_time": "2021-06-14T00:53:59.132891",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "F1 score reasonable for the Train set, but very bad for Test set... Data might have become overfitted because of oversampling."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incorporate-lewis",
   "metadata": {
    "papermill": {
     "duration": 0.076887,
     "end_time": "2021-06-14T00:53:59.367082",
     "exception": false,
     "start_time": "2021-06-14T00:53:59.290195",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Let's try to improve using Random Forest:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dress-valuation",
   "metadata": {
    "papermill": {
     "duration": 0.078628,
     "end_time": "2021-06-14T00:53:59.523237",
     "exception": false,
     "start_time": "2021-06-14T00:53:59.444609",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Transform data back to categorical - Random Forest can and should dealt with it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "powered-arnold",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-14T00:53:59.681563Z",
     "iopub.status.busy": "2021-06-14T00:53:59.680310Z",
     "iopub.status.idle": "2021-06-14T00:53:59.700375Z",
     "shell.execute_reply": "2021-06-14T00:53:59.699319Z"
    },
    "papermill": {
     "duration": 0.100562,
     "end_time": "2021-06-14T00:53:59.700496",
     "exception": false,
     "start_time": "2021-06-14T00:53:59.599934",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Test split - using caret\n"
     ]
    }
   ],
   "source": [
    "cat(\"Train Test split - using caret\\n\")\n",
    "data2$hypertension <- as.factor(data2$hypertension)\n",
    "data2$work_self <- as.factor(data2$work_self)\n",
    "data2$stroke <- as.factor(data2$stroke)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "related-trunk",
   "metadata": {
    "papermill": {
     "duration": 0.077102,
     "end_time": "2021-06-14T00:53:59.855079",
     "exception": false,
     "start_time": "2021-06-14T00:53:59.777977",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Train Test Split**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "drawn-darkness",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-14T00:54:00.013844Z",
     "iopub.status.busy": "2021-06-14T00:54:00.012523Z",
     "iopub.status.idle": "2021-06-14T00:54:00.060531Z",
     "shell.execute_reply": "2021-06-14T00:54:00.058864Z"
    },
    "papermill": {
     "duration": 0.128562,
     "end_time": "2021-06-14T00:54:00.060659",
     "exception": false,
     "start_time": "2021-06-14T00:53:59.932097",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Test split - using caret\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>age</th><th scope=col>hypertension</th><th scope=col>avg_glucose_level</th><th scope=col>work_self</th><th scope=col>stroke</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>67</td><td>0</td><td>228.69</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>80</td><td>0</td><td>105.92</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>79</td><td>1</td><td>174.12</td><td>1</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>74</td><td>1</td><td> 70.09</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>8</th><td>69</td><td>0</td><td> 94.39</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>78</td><td>0</td><td> 58.57</td><td>0</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & age & hypertension & avg\\_glucose\\_level & work\\_self & stroke\\\\\n",
       "  & <dbl> & <fct> & <dbl> & <fct> & <fct>\\\\\n",
       "\\hline\n",
       "\t1 & 67 & 0 & 228.69 & 0 & 1\\\\\n",
       "\t3 & 80 & 0 & 105.92 & 0 & 1\\\\\n",
       "\t5 & 79 & 1 & 174.12 & 1 & 1\\\\\n",
       "\t7 & 74 & 1 &  70.09 & 0 & 1\\\\\n",
       "\t8 & 69 & 0 &  94.39 & 0 & 1\\\\\n",
       "\t10 & 78 & 0 &  58.57 & 0 & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 5\n",
       "\n",
       "| <!--/--> | age &lt;dbl&gt; | hypertension &lt;fct&gt; | avg_glucose_level &lt;dbl&gt; | work_self &lt;fct&gt; | stroke &lt;fct&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| 1 | 67 | 0 | 228.69 | 0 | 1 |\n",
       "| 3 | 80 | 0 | 105.92 | 0 | 1 |\n",
       "| 5 | 79 | 1 | 174.12 | 1 | 1 |\n",
       "| 7 | 74 | 1 |  70.09 | 0 | 1 |\n",
       "| 8 | 69 | 0 |  94.39 | 0 | 1 |\n",
       "| 10 | 78 | 0 |  58.57 | 0 | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "   age hypertension avg_glucose_level work_self stroke\n",
       "1  67  0            228.69            0         1     \n",
       "3  80  0            105.92            0         1     \n",
       "5  79  1            174.12            1         1     \n",
       "7  74  1             70.09            0         1     \n",
       "8  69  0             94.39            0         1     \n",
       "10 78  0             58.57            0         1     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t3578 obs. of  5 variables:\n",
      " $ age              : num  67 80 79 74 69 78 61 54 79 64 ...\n",
      " $ hypertension     : Factor w/ 2 levels \"0\",\"1\": 1 1 2 2 1 1 1 1 1 1 ...\n",
      " $ avg_glucose_level: num  228.7 105.9 174.1 70.1 94.4 ...\n",
      " $ work_self        : Factor w/ 2 levels \"0\",\"1\": 1 1 2 1 1 1 1 1 1 1 ...\n",
      " $ stroke           : Factor w/ 2 levels \"0\",\"1\": 2 2 2 2 2 2 2 2 2 2 ...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>age</th><th scope=col>hypertension</th><th scope=col>avg_glucose_level</th><th scope=col>work_self</th><th scope=col>stroke</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>2</th><td>61</td><td>0</td><td>202.21</td><td>1</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>49</td><td>0</td><td>171.23</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>81</td><td>0</td><td>186.21</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>59</td><td>0</td><td> 76.15</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>11</th><td>81</td><td>1</td><td> 80.43</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>14</th><td>78</td><td>0</td><td>219.84</td><td>0</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
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      "text/latex": [
       "A data.frame: 6 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & age & hypertension & avg\\_glucose\\_level & work\\_self & stroke\\\\\n",
       "  & <dbl> & <fct> & <dbl> & <fct> & <fct>\\\\\n",
       "\\hline\n",
       "\t2 & 61 & 0 & 202.21 & 1 & 1\\\\\n",
       "\t4 & 49 & 0 & 171.23 & 0 & 1\\\\\n",
       "\t6 & 81 & 0 & 186.21 & 0 & 1\\\\\n",
       "\t9 & 59 & 0 &  76.15 & 0 & 1\\\\\n",
       "\t11 & 81 & 1 &  80.43 & 0 & 1\\\\\n",
       "\t14 & 78 & 0 & 219.84 & 0 & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 5\n",
       "\n",
       "| <!--/--> | age &lt;dbl&gt; | hypertension &lt;fct&gt; | avg_glucose_level &lt;dbl&gt; | work_self &lt;fct&gt; | stroke &lt;fct&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| 2 | 61 | 0 | 202.21 | 1 | 1 |\n",
       "| 4 | 49 | 0 | 171.23 | 0 | 1 |\n",
       "| 6 | 81 | 0 | 186.21 | 0 | 1 |\n",
       "| 9 | 59 | 0 |  76.15 | 0 | 1 |\n",
       "| 11 | 81 | 1 |  80.43 | 0 | 1 |\n",
       "| 14 | 78 | 0 | 219.84 | 0 | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "   age hypertension avg_glucose_level work_self stroke\n",
       "2  61  0            202.21            1         1     \n",
       "4  49  0            171.23            0         1     \n",
       "6  81  0            186.21            0         1     \n",
       "9  59  0             76.15            0         1     \n",
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       "14 78  0            219.84            0         1     "
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t1532 obs. of  5 variables:\n",
      " $ age              : num  61 49 81 59 81 78 50 52 79 42 ...\n",
      " $ hypertension     : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 2 1 2 2 1 1 ...\n",
      " $ avg_glucose_level: num  202.2 171.2 186.2 76.2 80.4 ...\n",
      " $ work_self        : Factor w/ 2 levels \"0\",\"1\": 2 1 1 1 1 1 2 2 2 1 ...\n",
      " $ stroke           : Factor w/ 2 levels \"0\",\"1\": 2 2 2 2 2 2 2 2 2 2 ...\n"
     ]
    }
   ],
   "source": [
    "cat(\"Train Test split - using caret\\n\")\n",
    "trainIndex2 <- createDataPartition(data2$stroke, p = 0.7, \n",
    "                                  list = FALSE, \n",
    "                                  times = 1)\n",
    "dataTrain02 <- data2[ trainIndex2,]\n",
    "dataTest2  <- data2[-trainIndex2,]\n",
    "head(dataTrain02)\n",
    "str(dataTrain02)\n",
    "head(dataTest2)\n",
    "str(dataTest2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "duplicate-drink",
   "metadata": {
    "papermill": {
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     "start_time": "2021-06-14T00:54:00.115378",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Balance Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "genetic-assembly",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-14T00:54:00.304180Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Balance (train) data by oversampling. From 5% do 30%\n",
      "'data.frame':\t4826 obs. of  5 variables:\n",
      " $ age              : num  3 58 8 52 75 32 79 37 40 35 ...\n",
      " $ hypertension     : Factor w/ 2 levels \"0\",\"1\": 1 2 1 1 1 1 1 1 1 1 ...\n",
      " $ avg_glucose_level: num  95.1 88 110.9 77.6 243.5 ...\n",
      " $ work_self        : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 2 1 1 1 1 1 ...\n",
      " $ stroke           : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n"
     ]
    }
   ],
   "source": [
    "cat(\"Balance (train) data by oversampling. From 5% do 30%\\n\")\n",
    "dataTrain2 <- ovun.sample(as.factor(stroke)~.,data = dataTrain02, method = 'over',p = 0.3)$data\n",
    "str(dataTrain2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "constant-hormone",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Run Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "corrected-theme",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-14T00:54:00.572996Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "rf <- randomForest(stroke ~., data = dataTrain2, ntree = 1000, mtry = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "worldwide-affiliation",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-14T00:54:03.123750Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       " randomForest(formula = stroke ~ ., data = dataTrain2, ntree = 1000,      mtry = 1) \n",
       "               Type of random forest: classification\n",
       "                     Number of trees: 1000\n",
       "No. of variables tried at each split: 1\n",
       "\n",
       "        OOB estimate of  error rate: 19.91%\n",
       "Confusion matrix:\n",
       "     0   1 class.error\n",
       "0 3147 256  0.07522774\n",
       "1  705 718  0.49543219"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "                Length Class  Mode     \n",
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       "type               1   -none- character\n",
       "predicted       4826   factor numeric  \n",
       "err.rate        3000   -none- numeric  \n",
       "confusion          6   -none- numeric  \n",
       "votes           9652   matrix numeric  \n",
       "oob.times       4826   -none- numeric  \n",
       "classes            2   -none- character\n",
       "importance         4   -none- numeric  \n",
       "importanceSD       0   -none- NULL     \n",
       "localImportance    0   -none- NULL     \n",
       "proximity          0   -none- NULL     \n",
       "ntree              1   -none- numeric  \n",
       "mtry               1   -none- numeric  \n",
       "forest            14   -none- list     \n",
       "y               4826   factor numeric  \n",
       "test               0   -none- NULL     \n",
       "inbag              0   -none- NULL     \n",
       "terms              3   terms  call     "
      ]
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "rf\n",
    "summary(rf)"
   ]
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   "id": "historical-orchestra",
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      "Predict on Train"
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       "</style><dl class=dl-inline><dt>2</dt><dd>0</dd><dt>4</dt><dd>0</dd><dt>6</dt><dd>1</dd><dt>9</dt><dd>0</dd><dt>11</dt><dd>1</dd><dt>14</dt><dd>1</dd><dt>16</dt><dd>0</dd><dt>22</dt><dd>0</dd><dt>23</dt><dd>0</dd><dt>32</dt><dd>0</dd><dt>35</dt><dd>0</dd><dt>36</dt><dd>0</dd><dt>38</dt><dd>1</dd><dt>41</dt><dd>0</dd><dt>43</dt><dd>0</dd><dt>44</dt><dd>0</dd><dt>47</dt><dd>0</dd><dt>55</dt><dd>1</dd><dt>57</dt><dd>0</dd><dt>68</dt><dd>0</dd><dt>70</dt><dd>1</dd><dt>74</dt><dd>1</dd><dt>75</dt><dd>0</dd><dt>76</dt><dd>0</dd><dt>77</dt><dd>1</dd><dt>81</dt><dd>0</dd><dt>82</dt><dd>0</dd><dt>83</dt><dd>0</dd><dt>86</dt><dd>0</dd><dt>87</dt><dd>0</dd><dt>88</dt><dd>0</dd><dt>92</dt><dd>0</dd><dt>94</dt><dd>1</dd><dt>100</dt><dd>0</dd><dt>105</dt><dd>0</dd><dt>106</dt><dd>0</dd><dt>108</dt><dd>0</dd><dt>111</dt><dd>1</dd><dt>112</dt><dd>0</dd><dt>116</dt><dd>1</dd><dt>129</dt><dd>1</dd><dt>132</dt><dd>1</dd><dt>133</dt><dd>1</dd><dt>134</dt><dd>0</dd><dt>139</dt><dd>1</dd><dt>143</dt><dd>1</dd><dt>145</dt><dd>1</dd><dt>151</dt><dd>1</dd><dt>152</dt><dd>1</dd><dt>161</dt><dd>0</dd></dl>\n",
       "\n",
       "<details>\n",
       "\t<summary style=display:list-item;cursor:pointer>\n",
       "\t\t<strong>Levels</strong>:\n",
       "\t</summary>\n",
       "\t<style>\n",
       "\t.list-inline {list-style: none; margin:0; padding: 0}\n",
       "\t.list-inline>li {display: inline-block}\n",
       "\t.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "\t</style>\n",
       "\t<ol class=list-inline><li>'0'</li><li>'1'</li></ol>\n",
       "</details>"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[2] 0\n",
       "\\item[4] 0\n",
       "\\item[6] 1\n",
       "\\item[9] 0\n",
       "\\item[11] 1\n",
       "\\item[14] 1\n",
       "\\item[16] 0\n",
       "\\item[22] 0\n",
       "\\item[23] 0\n",
       "\\item[32] 0\n",
       "\\item[35] 0\n",
       "\\item[36] 0\n",
       "\\item[38] 1\n",
       "\\item[41] 0\n",
       "\\item[43] 0\n",
       "\\item[44] 0\n",
       "\\item[47] 0\n",
       "\\item[55] 1\n",
       "\\item[57] 0\n",
       "\\item[68] 0\n",
       "\\item[70] 1\n",
       "\\item[74] 1\n",
       "\\item[75] 0\n",
       "\\item[76] 0\n",
       "\\item[77] 1\n",
       "\\item[81] 0\n",
       "\\item[82] 0\n",
       "\\item[83] 0\n",
       "\\item[86] 0\n",
       "\\item[87] 0\n",
       "\\item[88] 0\n",
       "\\item[92] 0\n",
       "\\item[94] 1\n",
       "\\item[100] 0\n",
       "\\item[105] 0\n",
       "\\item[106] 0\n",
       "\\item[108] 0\n",
       "\\item[111] 1\n",
       "\\item[112] 0\n",
       "\\item[116] 1\n",
       "\\item[129] 1\n",
       "\\item[132] 1\n",
       "\\item[133] 1\n",
       "\\item[134] 0\n",
       "\\item[139] 1\n",
       "\\item[143] 1\n",
       "\\item[145] 1\n",
       "\\item[151] 1\n",
       "\\item[152] 1\n",
       "\\item[161] 0\n",
       "\\end{description*}\n",
       "\n",
       "\\emph{Levels}: \\begin{enumerate*}\n",
       "\\item '0'\n",
       "\\item '1'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "2\n",
       ":   04\n",
       ":   06\n",
       ":   19\n",
       ":   011\n",
       ":   114\n",
       ":   116\n",
       ":   022\n",
       ":   023\n",
       ":   032\n",
       ":   035\n",
       ":   036\n",
       ":   038\n",
       ":   141\n",
       ":   043\n",
       ":   044\n",
       ":   047\n",
       ":   055\n",
       ":   157\n",
       ":   068\n",
       ":   070\n",
       ":   174\n",
       ":   175\n",
       ":   076\n",
       ":   077\n",
       ":   181\n",
       ":   082\n",
       ":   083\n",
       ":   086\n",
       ":   087\n",
       ":   088\n",
       ":   092\n",
       ":   094\n",
       ":   1100\n",
       ":   0105\n",
       ":   0106\n",
       ":   0108\n",
       ":   0111\n",
       ":   1112\n",
       ":   0116\n",
       ":   1129\n",
       ":   1132\n",
       ":   1133\n",
       ":   1134\n",
       ":   0139\n",
       ":   1143\n",
       ":   1145\n",
       ":   1151\n",
       ":   1152\n",
       ":   1161\n",
       ":   0\n",
       "\n",
       "\n",
       "**Levels**: 1. '0'\n",
       "2. '1'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  2   4   6   9  11  14  16  22  23  32  35  36  38  41  43  44  47  55  57  68 \n",
       "  0   0   1   0   1   1   0   0   0   0   0   0   1   0   0   0   0   1   0   0 \n",
       " 70  74  75  76  77  81  82  83  86  87  88  92  94 100 105 106 108 111 112 116 \n",
       "  1   1   0   0   1   0   0   0   0   0   0   0   1   0   0   0   0   1   0   1 \n",
       "129 132 133 134 139 143 145 151 152 161 \n",
       "  1   1   1   0   1   1   1   1   1   0 \n",
       "Levels: 0 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat(\"Predict on Train\")\n",
    "predRF_train <- predict(rf)\n",
    "#predRF_train <- ifelse(predRF_train0 >0.10,1,0)\n",
    "#sum(predRF_train)\n",
    "#how they look\n",
    "length(predRF_train)\n",
    "head(predRF_train,50)\n",
    "\n",
    "cat(\"Predict on Test\")\n",
    "predRF_test <- predict(rf, newdata = dataTest2)\n",
    "#predRF_test <- ifelse(predRF_test0 >0.10,1,0)\n",
    "#sum(predRF_test)\n",
    "#how they look\n",
    "length(predRF_test)\n",
    "head(predRF_test,50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "illegal-exploration",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-14T00:54:03.524212Z",
     "iopub.status.busy": "2021-06-14T00:54:03.523227Z",
     "iopub.status.idle": "2021-06-14T00:54:03.610234Z",
     "shell.execute_reply": "2021-06-14T00:54:03.609772Z"
    },
    "papermill": {
     "duration": 0.148993,
     "end_time": "2021-06-14T00:54:03.610339",
     "exception": false,
     "start_time": "2021-06-14T00:54:03.461346",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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MRe9epYWFjY1pdy87uOTTt/Cy1CiCDn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\nB/79977oaDxcFoC5/Jz8XUt2ubi6zJo1i3UWEAgUOwCxqFGjx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JcQ7AAAgEkRTyNObD6RGJ3YsWPHI0eOtG/fnumKAOoxBDsAAGBGXnae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\ngh0AAAAASyDYAQAAALAEgh0AAAAAS/wfsi5ETmedYoMAAAAASUVORK5CYII="
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Confusion Matrix\n",
    "a2 <- table(Predictions = predRF_train, TrueLabels = dataTrain2$stroke)\n",
    "fourfoldplot(a2, color = c(\"#CC6666\", \"#99CC99\"),\n",
    "             conf.level = 0, margin = 1, main = \"Confusion Matrix - Train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "nonprofit-engagement",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-14T00:54:03.734361Z",
     "iopub.status.busy": "2021-06-14T00:54:03.733416Z",
     "iopub.status.idle": "2021-06-14T00:54:03.756416Z",
     "shell.execute_reply": "2021-06-14T00:54:03.755680Z"
    },
    "papermill": {
     "duration": 0.086118,
     "end_time": "2021-06-14T00:54:03.756529",
     "exception": false,
     "start_time": "2021-06-14T00:54:03.670411",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =  0.8008703\n",
      "Precision =  0.7371663\n",
      "Recall =  0.5045678\n",
      "F1 score =  0.5990822"
     ]
    }
   ],
   "source": [
    "#Accuracy\n",
    "acc2 <- ( a2[2,2] + a2[1,1] ) / (length(dataTrain2$stroke))\n",
    "cat(\"Accuracy = \",acc2 )\n",
    "#F1\n",
    "Precision2 =  a2[2,2] / ( a2[2,2] + a2[2,1] )\n",
    "cat(\"\\nPrecision = \",Precision2 )\n",
    "Recall2 =  a2[2,2] / ( a2[2,2] + a2[1,2] )\n",
    "cat(\"\\nRecall = \",Recall2 )\n",
    "F1score2  = 2*(Recall2 * Precision2) / (Recall2 + Precision2)\n",
    "cat(\"\\nF1 score = \",F1score2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "powered-fishing",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-14T00:54:03.884145Z",
     "iopub.status.busy": "2021-06-14T00:54:03.883306Z",
     "iopub.status.idle": "2021-06-14T00:54:03.965982Z",
     "shell.execute_reply": "2021-06-14T00:54:03.965545Z"
    },
    "papermill": {
     "duration": 0.146946,
     "end_time": "2021-06-14T00:54:03.966081",
     "exception": false,
     "start_time": "2021-06-14T00:54:03.819135",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IiI8PDwyOisJz056s6uLYdPnnv0+osf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\ncFD3CwEAAHCYdo7ymsejI3aEELFJs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7CwBHoNhxULNmzQghabjMDgC0272oewq5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\nAABJRU5ErkJggg=="
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Confusion Matrix\n",
    "b2 <- table(Predictions = predRF_test, TrueLabels = dataTest2$stroke)\n",
    "fourfoldplot(b2, color = c(\"#CC6666\", \"#99CC99\"),\n",
    "             conf.level = 0, margin = 1, main = \"Confusion Matrix - Test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "infinite-faculty",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-14T00:54:04.101166Z",
     "iopub.status.busy": "2021-06-14T00:54:04.099854Z",
     "iopub.status.idle": "2021-06-14T00:54:04.128072Z",
     "shell.execute_reply": "2021-06-14T00:54:04.126482Z"
    },
    "papermill": {
     "duration": 0.096525,
     "end_time": "2021-06-14T00:54:04.128202",
     "exception": false,
     "start_time": "2021-06-14T00:54:04.031677",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =  0.9027415\n",
      "Precision =  0.2222222\n",
      "Recall =  0.4054054\n",
      "F1 score =  0.2870813"
     ]
    }
   ],
   "source": [
    "#Accuracy\n",
    "acc2 <- ( b2[2,2] + b2[1,1] ) / (length(dataTest2$stroke))\n",
    "cat(\"Accuracy = \",acc2 )\n",
    "#F1\n",
    "Precision2 =  b2[2,2] / ( b2[2,2] + b2[2,1] )\n",
    "cat(\"\\nPrecision = \",Precision2 )\n",
    "Recall2 =  b2[2,2] / ( b2[2,2] + b2[1,2] )\n",
    "cat(\"\\nRecall = \",Recall2 )\n",
    "F1score2  = 2*(Recall2 * Precision2) / (Recall2 + Precision2)\n",
    "cat(\"\\nF1 score = \",F1score2 )"
   ]
  },
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   "source": [
    "Result is better for Random Forest model. The Accuracy is much better. <br>\n",
    "F1 score is good for the Train set, but not so good for Test set... again, data might have become overfitted.\n"
   ]
  }
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