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     "text": [
      "Whole dataset size:  61320\nTrain dataset size:  35340\nTest dataset size:  11784\nValidate dataset size:  11784\n                  id  gender          age  hypertension  heart_disease  \\\ncount    5110.000000    5110  5110.000000   5110.000000    5110.000000   \nunique           NaN       3          NaN           NaN            NaN   \ntop              NaN  Female          NaN           NaN            NaN   \nfreq             NaN    2994          NaN           NaN            NaN   \nmean    36517.829354     NaN    43.226614      0.097456       0.054012   \nstd     21161.721625     NaN    22.612647      0.296607       0.226063   \nmin        67.000000     NaN     0.080000      0.000000       0.000000   \n25%     17741.250000     NaN    25.000000      0.000000       0.000000   \n50%     36932.000000     NaN    45.000000      0.000000       0.000000   \n75%     54682.000000     NaN    61.000000      0.000000       0.000000   \nmax     72940.000000     NaN    82.000000      1.000000       1.000000   \n\n       ever_married work_type Residence_type  avg_glucose_level          bmi  \\\ncount          5110      5110           5110        5110.000000  4909.000000   \nunique            2         5              2                NaN          NaN   \ntop             Yes   Private          Urban                NaN          NaN   \nfreq           3353      2925           2596                NaN          NaN   \nmean            NaN       NaN            NaN         106.147677    28.893237   \nstd             NaN       NaN            NaN          45.283560     7.854067   \nmin             NaN       NaN            NaN          55.120000    10.300000   \n25%             NaN       NaN            NaN          77.245000    23.500000   \n50%             NaN       NaN            NaN          91.885000    28.100000   \n75%             NaN       NaN            NaN         114.090000    33.100000   \nmax             NaN       NaN            NaN         271.740000    97.600000   \n\n       smoking_status       stroke  \ncount            5110  5110.000000  \nunique              4          NaN  \ntop      never smoked          NaN  \nfreq             1892          NaN  \nmean              NaN     0.048728  \nstd               NaN     0.215320  \nmin               NaN     0.000000  \n25%               NaN     0.000000  \n50%               NaN     0.000000  \n75%               NaN     0.000000  \nmax               NaN     1.000000  \n"
     ]
    }
   ],
   "source": [
    "import kaggle\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "def downloadCSV():\n",
    "    kaggle.api.authenticate()\n",
    "    kaggle.api.dataset_download_files('fedesoriano/stroke-prediction-dataset', path='.', unzip=True)\n",
    "    data = pd.read_csv('healthcare-dataset-stroke-data.csv')\n",
    "\n",
    "def dropNaN():\n",
    "    data = pd.read_csv('healthcare-dataset-stroke-data.csv')\n",
    "    data = data.dropna()\n",
    "    return data\n",
    "\n",
    "def NormalizeData(data):\n",
    "    data = data.astype({\"age\": np.int64})\n",
    "    for col in data.columns:\n",
    "        if data[col].dtype == object: # STRINGS TO LOWERCASE\n",
    "            data[col] = data[col].str.lower()\n",
    "        if data[col].dtype == np.float64: # FLOATS TO VALUES IN [ 0, 1]\n",
    "            dataReshaped = data[col].values.reshape(-1,1)\n",
    "            scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "            data[col] = scaler.fit_transform(dataReshaped)\n",
    "        if col == 'ever_married': # YES/NO TO 1/0\n",
    "            data[col] = data[col].map(dict(yes=1, no=0))\n",
    "        if col == 'smoking_status':\n",
    "            data[col] = data[col].str.replace(\" \", \"_\")\n",
    "        if col == 'work_type':\n",
    "            data[col] = data[col].str.replace(\"-\", \"_\")\n",
    "    return data\n",
    "\n",
    "def saveToCSV(data1,data2,data3):\n",
    "    data1.to_csv(\"data_train.csv\", index=False)\n",
    "    data2.to_csv(\"data_test.csv\",index=False)\n",
    "    data3.to_csv(\"data_val.csv\",index=False)\n",
    "\n",
    "def describeDataset(dt, dt2, dv):\n",
    "    data = pd.read_csv('healthcare-dataset-stroke-data.csv')\n",
    "    print(\"Whole dataset size: \", data.size)\n",
    "    print(\"Train dataset size: \", dt.size)\n",
    "    print(\"Test dataset size: \", dt2.size)\n",
    "    print(\"Validate dataset size: \", dv.size)\n",
    "    print(data.describe(include='all'))\n",
    "\n",
    "\n",
    "downloadCSV()\n",
    "data = dropNaN()\n",
    "data = NormalizeData(data)\n",
    "\n",
    "data_train, data_test = train_test_split(data, test_size=0.2, random_state=1)\n",
    "data_train, data_val = train_test_split(data_train, test_size=0.25, random_state=1) ## Twice to get 0.6, 0.2, 0.2\n",
    "saveToCSV(data_train,data_test,data_val)\n",
    "describeDataset(data_train,data_test,data_val)\n",
    "\n"
   ]
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "data_orginal = pd.read_csv('healthcare-dataset-stroke-data.csv')\n",
    "data_orginal['work_type'].value_counts().plot(kind=\"bar\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "metadata": {},
     "execution_count": 23
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "data_orginal['stroke'].value_counts().plot(kind=\"bar\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "metadata": {},
     "execution_count": 24
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "data_orginal['heart_disease'].value_counts().plot(kind=\"bar\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "metadata": {},
     "execution_count": 25
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "data_orginal['smoking_status'].value_counts().plot(kind=\"bar\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "metadata": {},
     "execution_count": 28
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "data_orginal['Residence_type'].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}