262 lines
137 KiB
Plaintext
262 lines
137 KiB
Plaintext
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"metadata": {
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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"file_extension": ".py",
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"name": "python",
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"nbconvert_exporter": "python",
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"language": "python"
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"nbformat": 4,
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"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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]
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}
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],
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"source": [
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"import kaggle\n",
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"import os\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"from sklearn.preprocessing import MinMaxScaler\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"def downloadCSV():\n",
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" kaggle.api.authenticate()\n",
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" kaggle.api.dataset_download_files('fedesoriano/stroke-prediction-dataset', path='.', unzip=True)\n",
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" data = pd.read_csv('healthcare-dataset-stroke-data.csv')\n",
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"\n",
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"def dropNaN():\n",
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" data = pd.read_csv('healthcare-dataset-stroke-data.csv')\n",
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" data = data.dropna()\n",
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" return data\n",
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"\n",
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"def NormalizeData(data):\n",
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" data = data.astype({\"age\": np.int64})\n",
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" for col in data.columns:\n",
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" if data[col].dtype == object: # STRINGS TO LOWERCASE\n",
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" data[col] = data[col].str.lower()\n",
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" if data[col].dtype == np.float64: # FLOATS TO VALUES IN [ 0, 1]\n",
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" dataReshaped = data[col].values.reshape(-1,1)\n",
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" scaler = MinMaxScaler(feature_range=(0, 1))\n",
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" data[col] = scaler.fit_transform(dataReshaped)\n",
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" if col == 'ever_married': # YES/NO TO 1/0\n",
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" data[col] = data[col].map(dict(yes=1, no=0))\n",
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" if col == 'smoking_status':\n",
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" data[col] = data[col].str.replace(\" \", \"_\")\n",
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" if col == 'work_type':\n",
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" data[col] = data[col].str.replace(\"-\", \"_\")\n",
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" return data\n",
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"\n",
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"def saveToCSV(data1,data2,data3):\n",
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" data1.to_csv(\"data_train.csv\", index=False)\n",
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" data2.to_csv(\"data_test.csv\",index=False)\n",
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" data3.to_csv(\"data_val.csv\",index=False)\n",
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"\n",
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"def describeDataset(dt, dt2, dv):\n",
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" data = pd.read_csv('healthcare-dataset-stroke-data.csv')\n",
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" print(\"Whole dataset size: \", data.size)\n",
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" print(\"Train dataset size: \", dt.size)\n",
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" print(\"Test dataset size: \", dt2.size)\n",
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" print(\"Validate dataset size: \", dv.size)\n",
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" print(data.describe(include='all'))\n",
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"\n",
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"\n",
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"downloadCSV()\n",
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"data = dropNaN()\n",
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"data = NormalizeData(data)\n",
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"\n",
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"data_train, data_test = train_test_split(data, test_size=0.2, random_state=1)\n",
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"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",
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"saveToCSV(data_train,data_test,data_val)\n",
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"describeDataset(data_train,data_test,data_val)\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"<AxesSubplot:>"
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]
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},
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"metadata": {},
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"execution_count": 27
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},
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{
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"output_type": "display_data",
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"data": {
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"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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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},
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"metadata": {
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"needs_background": "light"
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}
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}
|
|
],
|
|
"source": [
|
|
"data_orginal['stroke'].value_counts().plot(kind=\"bar\")\n"
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|
]
|
|
},
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|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"output_type": "execute_result",
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|
"data": {
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|
"text/plain": [
|
|
"<AxesSubplot:>"
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|
]
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|
},
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|
"metadata": {},
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|
"execution_count": 24
|
|
},
|
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{
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"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 432x288 with 1 Axes>",
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},
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"metadata": {
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"needs_background": "light"
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}
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}
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],
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"source": [
|
|
"data_orginal['heart_disease'].value_counts().plot(kind=\"bar\")\n"
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|
]
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|
},
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{
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|
"cell_type": "code",
|
|
"execution_count": 25,
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|
"metadata": {},
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"outputs": [
|
|
{
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|
"output_type": "execute_result",
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"data": {
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"text/plain": [
|
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"<AxesSubplot:>"
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]
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},
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"metadata": {},
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"execution_count": 25
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},
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{
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"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 432x288 with 1 Axes>",
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"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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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\n"
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
}
|
|
}
|
|
],
|
|
"source": [
|
|
"data_orginal['Residence_type'].value_counts().plot(kind=\"bar\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
]
|
|
} |