naivebayes
This commit is contained in:
commit
0517754510
5111
healthcare-dataset-stroke-data.csv
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5111
healthcare-dataset-stroke-data.csv
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390
main.ipynb
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390
main.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd \n",
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"import numpy as np\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"#Wczytanie i normalizacja danych\n",
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"def NormalizeData(data):\n",
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" for col in data.columns:\n",
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" if data[col].dtype == object: \n",
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" data[col] = data[col].str.lower()\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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" if col == 'bmi':\n",
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" bins = [0, 21, 28, 40]\n",
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" labels=['low','mid','high']\n",
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" data[col] = pd.cut(data[col], bins=bins, labels=labels)\n",
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" if col == 'age':\n",
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" bins = [18, 30, 40, 50, 60, 70, 120]\n",
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" labels = ['18-29', '30-39', '40-49', '50-59', '60-69', '70+']\n",
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" data[col] = pd.cut(data[col], bins, labels = labels,include_lowest = True)\n",
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" if col == 'stroke':\n",
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" data[col] = data[col].replace({1: 'yes'})\n",
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" data[col] = data[col].replace({0: 'no'})\n",
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" if col == 'hypertension':\n",
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" data[col] = data[col].replace({1: 'yes'})\n",
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" data[col] = data[col].replace({0: 'no'})\n",
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" if col == 'heart_disease':\n",
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" data[col] = data[col].replace({1: 'yes'})\n",
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" data[col] = data[col].replace({0: 'no'})\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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"data = pd.read_csv(\"healthcare-dataset-stroke-data.csv\")\n",
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"data = NormalizeData(data)"
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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": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>id</th>\n",
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" <th>gender</th>\n",
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" <th>age</th>\n",
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" <th>hypertension</th>\n",
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" <th>heart_disease</th>\n",
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" <th>ever_married</th>\n",
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" <th>work_type</th>\n",
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" <th>Residence_type</th>\n",
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" <th>avg_glucose_level</th>\n",
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" <th>bmi</th>\n",
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" <th>smoking_status</th>\n",
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" <th>stroke</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>9046</td>\n",
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" <td>male</td>\n",
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" <td>60-69</td>\n",
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" <td>no</td>\n",
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" <td>yes</td>\n",
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" <td>yes</td>\n",
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" <td>private</td>\n",
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" <td>urban</td>\n",
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" <td>228.69</td>\n",
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" <td>high</td>\n",
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" <td>formerly_smoked</td>\n",
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" <td>yes</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>31112</td>\n",
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" <td>male</td>\n",
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" <td>70+</td>\n",
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" <td>no</td>\n",
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" <td>yes</td>\n",
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" <td>yes</td>\n",
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" <td>private</td>\n",
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" <td>rural</td>\n",
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" <td>105.92</td>\n",
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" <td>high</td>\n",
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" <td>never_smoked</td>\n",
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" <td>yes</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>60182</td>\n",
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" <td>female</td>\n",
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" <td>40-49</td>\n",
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" <td>no</td>\n",
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" <td>no</td>\n",
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" <td>yes</td>\n",
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" <td>private</td>\n",
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" <td>urban</td>\n",
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" <td>171.23</td>\n",
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" <td>high</td>\n",
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" <td>smokes</td>\n",
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" <td>yes</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>1665</td>\n",
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" <td>female</td>\n",
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" <td>70+</td>\n",
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" <td>yes</td>\n",
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" <td>no</td>\n",
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" <td>yes</td>\n",
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" <td>self_employed</td>\n",
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" <td>rural</td>\n",
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" <td>174.12</td>\n",
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" <td>mid</td>\n",
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" <td>never_smoked</td>\n",
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" <td>yes</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>56669</td>\n",
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" <td>male</td>\n",
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" <td>70+</td>\n",
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" <td>no</td>\n",
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" <td>no</td>\n",
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" <td>yes</td>\n",
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" <td>private</td>\n",
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" <td>urban</td>\n",
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" <td>186.21</td>\n",
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" <td>high</td>\n",
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" <td>formerly_smoked</td>\n",
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" <td>yes</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5102</th>\n",
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" <td>45010</td>\n",
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" <td>female</td>\n",
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" <td>50-59</td>\n",
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" <td>no</td>\n",
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" <td>no</td>\n",
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" <td>yes</td>\n",
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" <td>private</td>\n",
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" <td>rural</td>\n",
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" <td>77.93</td>\n",
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" <td>mid</td>\n",
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" <td>never_smoked</td>\n",
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" <td>no</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5106</th>\n",
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" <td>44873</td>\n",
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" <td>female</td>\n",
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" <td>70+</td>\n",
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" <td>no</td>\n",
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" <td>no</td>\n",
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" <td>yes</td>\n",
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" <td>self_employed</td>\n",
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" <td>urban</td>\n",
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" <td>125.20</td>\n",
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" <td>high</td>\n",
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" <td>never_smoked</td>\n",
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" <td>no</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5107</th>\n",
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" <td>19723</td>\n",
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" <td>female</td>\n",
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" <td>30-39</td>\n",
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" <td>no</td>\n",
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" <td>no</td>\n",
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" <td>yes</td>\n",
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" <td>self_employed</td>\n",
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" <td>rural</td>\n",
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" <td>82.99</td>\n",
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" <td>high</td>\n",
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" <td>never_smoked</td>\n",
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" <td>no</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5108</th>\n",
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" <td>37544</td>\n",
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" <td>male</td>\n",
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" <td>50-59</td>\n",
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" <td>no</td>\n",
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" <td>no</td>\n",
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" <td>yes</td>\n",
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" <td>private</td>\n",
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" <td>rural</td>\n",
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" <td>166.29</td>\n",
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" <td>mid</td>\n",
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" <td>formerly_smoked</td>\n",
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" <td>no</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5109</th>\n",
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" <td>44679</td>\n",
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" <td>female</td>\n",
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" <td>40-49</td>\n",
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" <td>no</td>\n",
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" <td>no</td>\n",
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" <td>yes</td>\n",
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" <td>govt_job</td>\n",
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" <td>urban</td>\n",
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" <td>85.28</td>\n",
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" <td>mid</td>\n",
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" <td>unknown</td>\n",
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" <td>no</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>3681 rows × 12 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" id gender age hypertension heart_disease ever_married \\\n",
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"0 9046 male 60-69 no yes yes \n",
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"2 31112 male 70+ no yes yes \n",
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"3 60182 female 40-49 no no yes \n",
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"4 1665 female 70+ yes no yes \n",
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"5 56669 male 70+ no no yes \n",
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"... ... ... ... ... ... ... \n",
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"5102 45010 female 50-59 no no yes \n",
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"5106 44873 female 70+ no no yes \n",
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"5107 19723 female 30-39 no no yes \n",
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"5108 37544 male 50-59 no no yes \n",
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"5109 44679 female 40-49 no no yes \n",
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"\n",
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" work_type Residence_type avg_glucose_level bmi smoking_status \\\n",
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"0 private urban 228.69 high formerly_smoked \n",
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"2 private rural 105.92 high never_smoked \n",
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"3 private urban 171.23 high smokes \n",
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"4 self_employed rural 174.12 mid never_smoked \n",
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"5 private urban 186.21 high formerly_smoked \n",
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"... ... ... ... ... ... \n",
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"5102 private rural 77.93 mid never_smoked \n",
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"5106 self_employed urban 125.20 high never_smoked \n",
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"5107 self_employed rural 82.99 high never_smoked \n",
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"5108 private rural 166.29 mid formerly_smoked \n",
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"5109 govt_job urban 85.28 mid unknown \n",
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"\n",
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" stroke \n",
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"0 yes \n",
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"2 yes \n",
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"3 yes \n",
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"4 yes \n",
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"5 yes \n",
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"... ... \n",
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"5102 no \n",
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"5106 no \n",
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"5107 no \n",
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"5108 no \n",
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"5109 no \n",
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"\n",
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"[3681 rows x 12 columns]"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data\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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"#podział danych na treningowy i testowy \n",
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"data_train, data_test = train_test_split(data, random_state = 42)\n",
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"\n",
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"X_train =data_train[['gender', 'age', 'ever_married', 'Residence_type', 'bmi','smoking_status', 'work_type','hypertension','heart_disease']]\n",
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"Y_train = data_train['stroke']\n",
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"\n",
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"#rozdzielenie etykiet i cech\n",
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"X_test =data_test[['gender', 'age', 'ever_married', 'Residence_type', 'bmi','smoking_status', 'work_type','hypertension','heart_disease']]\n",
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"Y_test = data_test['stroke']"
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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": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"class NaiveBayes:\n",
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" def __init__(self):\n",
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" self.features = list\n",
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" self.likelihoods = {}\n",
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" self.class_priors = {}\n",
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" self.pred_priors = {}\n",
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"\n",
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" self.X_train = np.array\n",
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" self.y_train = np.array\n",
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" self.train_size = int\n",
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" self.num_feats = int\n",
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" \n",
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" def fit(self, x_train, y_train):\n",
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"\n",
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" self.features = list(X.columns)\n",
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" self.X_train = x_train\n",
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" self.y_train = y_train\n",
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" self.train_size = X.shape[0]\n",
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" self.num_feats = X.shape[1]\n",
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"\n",
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" for feature in self.features:\n",
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" self.likelihoods[feature] = {}\n",
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" self.pred_priors[feature] = {}\n",
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"\n",
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" for feat_val in np.unique(self.X_train[feature]):\n",
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" self.pred_priors[feature].update({feat_val: 0})\n",
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"\n",
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" for outcome in np.unique(self.y_train):\n",
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" self.likelihoods[feature].update({feat_val+'_'+outcome:0})\n",
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" self.class_priors.update({outcome: 0})\n",
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"\n",
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" self._calc_class_prior()\n",
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" self._calc_likelihoods()\n",
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" self._calc_predictor_prior()\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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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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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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