2021-03-20 20:04:51 +01:00
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{
"cell_type": "code",
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"execution_count": 31,
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"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"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"
]
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],
"source": [
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"import kaggle\n",
"import os\n",
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"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",
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" 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",
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"\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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}
}
],
"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:>"
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},
"metadata": {},
"execution_count": 23
},
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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": {
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}
}
],
"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": {
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},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"data_orginal['smoking_status'].value_counts().plot(kind=\"bar\")\n"
]
},
{
"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"metadata": {},
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"execution_count": 28
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},
{
"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": []
}
]
}
