3614 lines
190 KiB
Plaintext
3614 lines
190 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Brain Stroke prediction- DecisionTree"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Context:\n",
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"\n",
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"A stroke is a medical condition in which poor blood flow to the brain causes cell death. There are two main types of stroke: ischemic, due to lack of blood flow, and hemorrhagic, due to bleeding. Both cause parts of the brain to stop functioning properly. Signs and symptoms of a stroke may include an inability to move or feel on one side of the body, problems understanding or speaking, dizziness, or loss of vision to one side. Signs and symptoms often appear soon after the stroke has occurred. If symptoms last less than one or two hours, the stroke is a transient ischemic attack (TIA), also called a mini-stroke. A hemorrhagic stroke may also be associated with a severe headache. The symptoms of a stroke can be permanent. Long-term complications may include pneumonia and loss of bladder control.\n",
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"\n",
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"The main risk factor for stroke is high blood pressure. Other risk factors include high blood cholesterol, tobacco smoking, obesity, diabetes mellitus, a previous TIA, end-stage kidney disease, and atrial fibrillation. An ischemic stroke is typically caused by blockage of a blood vessel, though there are also less common causes. A hemorrhagic stroke is caused by either bleeding directly into the brain or into the space between the brain's membranes. Bleeding may occur due to a ruptured brain aneurysm. Diagnosis is typically based on a physical exam and supported by medical imaging such as a CT scan or MRI scan. A CT scan can rule out bleeding, but may not necessarily rule out ischemia, which early on typically does not show up on a CT scan. Other tests such as an electrocardiogram (ECG) and blood tests are done to determine risk factors and rule out other possible causes. Low blood sugar may cause similar symptoms.\n",
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"\n",
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"Prevention includes decreasing risk factors, surgery to open up the arteries to the brain in those with problematic carotid narrowing, and warfarin in people with atrial fibrillation. Aspirin or statins may be recommended by physicians for prevention. A stroke or TIA often requires emergency care. An ischemic stroke, if detected within three to four and half hours, may be treatable with a medication that can break down the clot. Some hemorrhagic strokes benefit from surgery. Treatment to attempt recovery of lost function is called stroke rehabilitation, and ideally takes place in a stroke unit; however, these are not available in much of the world.\n",
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"\n",
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"### Attribute Information:\n",
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"\n",
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"1) gender: \"Male\", \"Female\" or \"Other\"\n",
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"2) age: age of the patient\n",
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"3) hypertension: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension\n",
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"4) heartdisease: 0 if the patient doesn't have any heart diseases, 1 if the patient has a heart disease 5) evermarried: \"No\" or \"Yes\"\n",
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"6) worktype: \"children\", \"Govtjov\", \"Neverworked\", \"Private\" or \"Self-employed\" 7) Residencetype: \"Rural\" or \"Urban\"\n",
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"8) avgglucoselevel: average glucose level in blood\n",
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"9) bmi: body mass index\n",
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"10) smoking_status: \"formerly smoked\", \"never smoked\", \"smokes\" or \"Unknown\"*\n",
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"11) stroke: 1 if the patient had a stroke or 0 if not\n",
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"\n",
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"*Note: \"Unknown\" in smoking_status means that the information is unavailable for this patient"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## IMPORTING LIBRARIES AND LOADING 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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np \n",
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"import pandas as pd \n",
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"import os\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"\n",
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"import warnings\n",
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"warnings.filterwarnings('ignore')\n",
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"\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.metrics import accuracy_score\n",
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"from sklearn.metrics import matthews_corrcoef\n",
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"from sklearn.metrics import f1_score\n",
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"\n",
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"from sklearn.neighbors import KNeighborsClassifier\n",
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"from sklearn.svm import SVC\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"from sklearn.tree import DecisionTreeClassifier\n",
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"from sklearn.neural_network import MLPClassifier\n",
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"from sklearn.ensemble import StackingClassifier\n",
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"from sklearn.linear_model import LogisticRegression\n",
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"\n",
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"import joblib as joblib"
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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": 2,
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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>30669</td>\n",
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" <td>Male</td>\n",
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" <td>3.0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>No</td>\n",
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" <td>children</td>\n",
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" <td>Rural</td>\n",
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" <td>95.12</td>\n",
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" <td>18.0</td>\n",
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" <td>NaN</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>30468</td>\n",
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" <td>Male</td>\n",
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" <td>58.0</td>\n",
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" <td>1</td>\n",
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" <td>0</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>87.96</td>\n",
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" <td>39.2</td>\n",
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" <td>never smoked</td>\n",
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" <td>0</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>16523</td>\n",
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" <td>Female</td>\n",
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" <td>8.0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>No</td>\n",
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" <td>Private</td>\n",
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" <td>Urban</td>\n",
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" <td>110.89</td>\n",
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" <td>17.6</td>\n",
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" <td>NaN</td>\n",
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" <td>0</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>56543</td>\n",
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" <td>Female</td>\n",
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" <td>70.0</td>\n",
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" <td>0</td>\n",
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" <td>0</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>69.04</td>\n",
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" <td>35.9</td>\n",
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" <td>formerly smoked</td>\n",
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" <td>0</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>46136</td>\n",
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" <td>Male</td>\n",
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" <td>14.0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>No</td>\n",
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" <td>Never_worked</td>\n",
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" <td>Rural</td>\n",
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" <td>161.28</td>\n",
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" <td>19.1</td>\n",
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" <td>NaN</td>\n",
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" <td>0</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>32257</td>\n",
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" <td>Female</td>\n",
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" <td>47.0</td>\n",
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" <td>0</td>\n",
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" <td>0</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>210.95</td>\n",
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" <td>50.1</td>\n",
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" <td>NaN</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>52800</td>\n",
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" <td>Female</td>\n",
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" <td>52.0</td>\n",
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" <td>0</td>\n",
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" <td>0</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>77.59</td>\n",
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" <td>17.7</td>\n",
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" <td>formerly smoked</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>41413</td>\n",
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" <td>Female</td>\n",
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" <td>75.0</td>\n",
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|
" <td>0</td>\n",
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|
" <td>1</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>243.53</td>\n",
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" <td>27.0</td>\n",
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" <td>never smoked</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
|
|
" <td>15266</td>\n",
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" <td>Female</td>\n",
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" <td>32.0</td>\n",
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" <td>0</td>\n",
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" <td>0</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>77.67</td>\n",
|
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" <td>32.3</td>\n",
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" <td>smokes</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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|
" <tr>\n",
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|
" <th>9</th>\n",
|
|
" <td>28674</td>\n",
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|
" <td>Female</td>\n",
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" <td>74.0</td>\n",
|
|
" <td>1</td>\n",
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|
" <td>0</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>205.84</td>\n",
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" <td>54.6</td>\n",
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" <td>never smoked</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\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 30669 Male 3.0 0 0 No \n",
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"1 30468 Male 58.0 1 0 Yes \n",
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"2 16523 Female 8.0 0 0 No \n",
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"3 56543 Female 70.0 0 0 Yes \n",
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"4 46136 Male 14.0 0 0 No \n",
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"5 32257 Female 47.0 0 0 Yes \n",
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"6 52800 Female 52.0 0 0 Yes \n",
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"7 41413 Female 75.0 0 1 Yes \n",
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"8 15266 Female 32.0 0 0 Yes \n",
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"9 28674 Female 74.0 1 0 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 children Rural 95.12 18.0 NaN \n",
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"1 Private Urban 87.96 39.2 never smoked \n",
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"2 Private Urban 110.89 17.6 NaN \n",
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"3 Private Rural 69.04 35.9 formerly smoked \n",
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"4 Never_worked Rural 161.28 19.1 NaN \n",
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"5 Private Urban 210.95 50.1 NaN \n",
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"6 Private Urban 77.59 17.7 formerly smoked \n",
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"7 Self-employed Rural 243.53 27.0 never smoked \n",
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"8 Private Rural 77.67 32.3 smokes \n",
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"9 Self-employed Urban 205.84 54.6 never smoked \n",
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"\n",
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" stroke \n",
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"0 0 \n",
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"1 0 \n",
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"2 0 \n",
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"3 0 \n",
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"4 0 \n",
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"5 0 \n",
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"6 0 \n",
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"7 0 \n",
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"8 0 \n",
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"9 0 "
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]
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},
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"execution_count": 2,
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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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"df = pd.read_csv('trainFile.csv')\n",
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"df.head(10)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## DATA EXPLORATION"
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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": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 43400 entries, 0 to 43399\n",
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"Data columns (total 12 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 id 43400 non-null int64 \n",
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" 1 gender 43400 non-null object \n",
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" 2 age 43400 non-null float64\n",
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" 3 hypertension 43400 non-null int64 \n",
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" 4 heart_disease 43400 non-null int64 \n",
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" 5 ever_married 43400 non-null object \n",
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" 6 work_type 43400 non-null object \n",
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" 7 Residence_type 43400 non-null object \n",
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" 8 avg_glucose_level 43400 non-null float64\n",
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" 9 bmi 41938 non-null float64\n",
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" 10 smoking_status 30108 non-null object \n",
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" 11 stroke 43400 non-null int64 \n",
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"dtypes: float64(3), int64(4), object(5)\n",
|
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"memory usage: 4.0+ MB\n"
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]
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}
|
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],
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"source": [
|
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"df.info()"
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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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"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",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>id</th>\n",
|
|
" <th>age</th>\n",
|
|
" <th>hypertension</th>\n",
|
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" <th>heart_disease</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>stroke</th>\n",
|
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" </tr>\n",
|
|
" </thead>\n",
|
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" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>count</th>\n",
|
|
" <td>43400.000000</td>\n",
|
|
" <td>43400.000000</td>\n",
|
|
" <td>43400.000000</td>\n",
|
|
" <td>43400.000000</td>\n",
|
|
" <td>43400.000000</td>\n",
|
|
" <td>41938.000000</td>\n",
|
|
" <td>43400.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>mean</th>\n",
|
|
" <td>36326.142350</td>\n",
|
|
" <td>42.217894</td>\n",
|
|
" <td>0.093571</td>\n",
|
|
" <td>0.047512</td>\n",
|
|
" <td>104.482750</td>\n",
|
|
" <td>28.605038</td>\n",
|
|
" <td>0.018041</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>std</th>\n",
|
|
" <td>21072.134879</td>\n",
|
|
" <td>22.519649</td>\n",
|
|
" <td>0.291235</td>\n",
|
|
" <td>0.212733</td>\n",
|
|
" <td>43.111751</td>\n",
|
|
" <td>7.770020</td>\n",
|
|
" <td>0.133103</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>min</th>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>0.080000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>55.000000</td>\n",
|
|
" <td>10.100000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>25%</th>\n",
|
|
" <td>18038.500000</td>\n",
|
|
" <td>24.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>77.540000</td>\n",
|
|
" <td>23.200000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>50%</th>\n",
|
|
" <td>36351.500000</td>\n",
|
|
" <td>44.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>91.580000</td>\n",
|
|
" <td>27.700000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>75%</th>\n",
|
|
" <td>54514.250000</td>\n",
|
|
" <td>60.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>112.070000</td>\n",
|
|
" <td>32.900000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>max</th>\n",
|
|
" <td>72943.000000</td>\n",
|
|
" <td>82.000000</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>291.050000</td>\n",
|
|
" <td>97.600000</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" id age hypertension heart_disease \\\n",
|
|
"count 43400.000000 43400.000000 43400.000000 43400.000000 \n",
|
|
"mean 36326.142350 42.217894 0.093571 0.047512 \n",
|
|
"std 21072.134879 22.519649 0.291235 0.212733 \n",
|
|
"min 1.000000 0.080000 0.000000 0.000000 \n",
|
|
"25% 18038.500000 24.000000 0.000000 0.000000 \n",
|
|
"50% 36351.500000 44.000000 0.000000 0.000000 \n",
|
|
"75% 54514.250000 60.000000 0.000000 0.000000 \n",
|
|
"max 72943.000000 82.000000 1.000000 1.000000 \n",
|
|
"\n",
|
|
" avg_glucose_level bmi stroke \n",
|
|
"count 43400.000000 41938.000000 43400.000000 \n",
|
|
"mean 104.482750 28.605038 0.018041 \n",
|
|
"std 43.111751 7.770020 0.133103 \n",
|
|
"min 55.000000 10.100000 0.000000 \n",
|
|
"25% 77.540000 23.200000 0.000000 \n",
|
|
"50% 91.580000 27.700000 0.000000 \n",
|
|
"75% 112.070000 32.900000 0.000000 \n",
|
|
"max 291.050000 97.600000 1.000000 "
|
|
]
|
|
},
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df.describe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"['Male' 'Female' 'Other']\n",
|
|
"['children' 'Private' 'Never_worked' 'Self-employed' 'Govt_job']\n",
|
|
"['Rural' 'Urban']\n",
|
|
"[nan 'never smoked' 'formerly smoked' 'smokes']\n",
|
|
"['No' 'Yes']\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(df['gender'].unique())\n",
|
|
"print(df['work_type'].unique())\n",
|
|
"print(df['Residence_type'].unique())\n",
|
|
"print(df['smoking_status'].unique())\n",
|
|
"print(df['ever_married'].unique())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## DATA PREPROCESSING"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
|
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|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>id</th>\n",
|
|
" <th>gender</th>\n",
|
|
" <th>age</th>\n",
|
|
" <th>hypertension</th>\n",
|
|
" <th>heart_disease</th>\n",
|
|
" <th>ever_married</th>\n",
|
|
" <th>work_type</th>\n",
|
|
" <th>Residence_type</th>\n",
|
|
" <th>avg_glucose_level</th>\n",
|
|
" <th>bmi</th>\n",
|
|
" <th>smoking_status</th>\n",
|
|
" <th>stroke</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>30669</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>3.0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>children</td>\n",
|
|
" <td>Rural</td>\n",
|
|
" <td>95.12</td>\n",
|
|
" <td>18.0</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>30468</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>58.0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>Private</td>\n",
|
|
" <td>Urban</td>\n",
|
|
" <td>87.96</td>\n",
|
|
" <td>39.2</td>\n",
|
|
" <td>never smoked</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>16523</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>8.0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>Private</td>\n",
|
|
" <td>Urban</td>\n",
|
|
" <td>110.89</td>\n",
|
|
" <td>17.6</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>56543</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>70.0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>Private</td>\n",
|
|
" <td>Rural</td>\n",
|
|
" <td>69.04</td>\n",
|
|
" <td>35.9</td>\n",
|
|
" <td>formerly smoked</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>46136</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>14.0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>Never_worked</td>\n",
|
|
" <td>Rural</td>\n",
|
|
" <td>161.28</td>\n",
|
|
" <td>19.1</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" id gender age hypertension heart_disease ever_married \\\n",
|
|
"0 30669 0 3.0 0 0 0 \n",
|
|
"1 30468 0 58.0 1 0 1 \n",
|
|
"2 16523 1 8.0 0 0 0 \n",
|
|
"3 56543 1 70.0 0 0 1 \n",
|
|
"4 46136 0 14.0 0 0 0 \n",
|
|
"\n",
|
|
" work_type Residence_type avg_glucose_level bmi smoking_status \\\n",
|
|
"0 children Rural 95.12 18.0 NaN \n",
|
|
"1 Private Urban 87.96 39.2 never smoked \n",
|
|
"2 Private Urban 110.89 17.6 NaN \n",
|
|
"3 Private Rural 69.04 35.9 formerly smoked \n",
|
|
"4 Never_worked Rural 161.28 19.1 NaN \n",
|
|
"\n",
|
|
" stroke \n",
|
|
"0 0 \n",
|
|
"1 0 \n",
|
|
"2 0 \n",
|
|
"3 0 \n",
|
|
"4 0 "
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df['ever_married'] = [ 0 if i !='Yes' else 1 for i in df['ever_married'] ]\n",
|
|
"df['gender'] = [0 if i != 'Female' else 1 for i in df['gender']]\n",
|
|
"df.head(5)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>id</th>\n",
|
|
" <th>gender</th>\n",
|
|
" <th>age</th>\n",
|
|
" <th>hypertension</th>\n",
|
|
" <th>heart_disease</th>\n",
|
|
" <th>ever_married</th>\n",
|
|
" <th>avg_glucose_level</th>\n",
|
|
" <th>bmi</th>\n",
|
|
" <th>stroke</th>\n",
|
|
" <th>work_type_Govt_job</th>\n",
|
|
" <th>work_type_Never_worked</th>\n",
|
|
" <th>work_type_Private</th>\n",
|
|
" <th>work_type_Self-employed</th>\n",
|
|
" <th>work_type_children</th>\n",
|
|
" <th>Residence_type_Rural</th>\n",
|
|
" <th>Residence_type_Urban</th>\n",
|
|
" <th>smoking_status_formerly smoked</th>\n",
|
|
" <th>smoking_status_never smoked</th>\n",
|
|
" <th>smoking_status_smokes</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>10782</th>\n",
|
|
" <td>51842</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>22.00</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>105.08</td>\n",
|
|
" <td>21.7</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>21306</th>\n",
|
|
" <td>58692</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>82.00</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>108.70</td>\n",
|
|
" <td>22.5</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>36821</th>\n",
|
|
" <td>68523</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>35.00</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>217.05</td>\n",
|
|
" <td>35.0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>23585</th>\n",
|
|
" <td>14192</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0.24</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>82.77</td>\n",
|
|
" <td>15.8</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2096</th>\n",
|
|
" <td>58543</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>50.00</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>89.95</td>\n",
|
|
" <td>48.9</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" id gender age hypertension heart_disease ever_married \\\n",
|
|
"10782 51842 1 22.00 0 0 0 \n",
|
|
"21306 58692 0 82.00 0 0 1 \n",
|
|
"36821 68523 0 35.00 0 0 1 \n",
|
|
"23585 14192 1 0.24 0 0 0 \n",
|
|
"2096 58543 1 50.00 0 0 1 \n",
|
|
"\n",
|
|
" avg_glucose_level bmi stroke work_type_Govt_job \\\n",
|
|
"10782 105.08 21.7 0 0 \n",
|
|
"21306 108.70 22.5 1 0 \n",
|
|
"36821 217.05 35.0 0 0 \n",
|
|
"23585 82.77 15.8 0 0 \n",
|
|
"2096 89.95 48.9 0 1 \n",
|
|
"\n",
|
|
" work_type_Never_worked work_type_Private work_type_Self-employed \\\n",
|
|
"10782 0 1 0 \n",
|
|
"21306 0 1 0 \n",
|
|
"36821 0 1 0 \n",
|
|
"23585 0 0 0 \n",
|
|
"2096 0 0 0 \n",
|
|
"\n",
|
|
" work_type_children Residence_type_Rural Residence_type_Urban \\\n",
|
|
"10782 0 0 1 \n",
|
|
"21306 0 1 0 \n",
|
|
"36821 0 0 1 \n",
|
|
"23585 1 0 1 \n",
|
|
"2096 0 0 1 \n",
|
|
"\n",
|
|
" smoking_status_formerly smoked smoking_status_never smoked \\\n",
|
|
"10782 0 1 \n",
|
|
"21306 0 1 \n",
|
|
"36821 0 0 \n",
|
|
"23585 0 0 \n",
|
|
"2096 1 0 \n",
|
|
"\n",
|
|
" smoking_status_smokes \n",
|
|
"10782 0 \n",
|
|
"21306 0 \n",
|
|
"36821 0 \n",
|
|
"23585 0 \n",
|
|
"2096 0 "
|
|
]
|
|
},
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df = pd.get_dummies(df, columns = ['work_type', 'Residence_type','smoking_status'])\n",
|
|
"df.sample(5)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
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"metadata": {},
|
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"outputs": [
|
|
{
|
|
"data": {
|
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"text/plain": [
|
|
"id 0\n",
|
|
"gender 0\n",
|
|
"age 0\n",
|
|
"hypertension 0\n",
|
|
"heart_disease 0\n",
|
|
"ever_married 0\n",
|
|
"avg_glucose_level 0\n",
|
|
"bmi 1462\n",
|
|
"stroke 0\n",
|
|
"work_type_Govt_job 0\n",
|
|
"work_type_Never_worked 0\n",
|
|
"work_type_Private 0\n",
|
|
"work_type_Self-employed 0\n",
|
|
"work_type_children 0\n",
|
|
"Residence_type_Rural 0\n",
|
|
"Residence_type_Urban 0\n",
|
|
"smoking_status_formerly smoked 0\n",
|
|
"smoking_status_never smoked 0\n",
|
|
"smoking_status_smokes 0\n",
|
|
"dtype: int64"
|
|
]
|
|
},
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df.isnull().sum()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
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"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"df = df.dropna(how = 'any', axis=0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"X = df.drop(['stroke','id'], axis = 1)\n",
|
|
"y = df['stroke']"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
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"metadata": {},
|
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"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>gender</th>\n",
|
|
" <th>age</th>\n",
|
|
" <th>hypertension</th>\n",
|
|
" <th>heart_disease</th>\n",
|
|
" <th>ever_married</th>\n",
|
|
" <th>avg_glucose_level</th>\n",
|
|
" <th>bmi</th>\n",
|
|
" <th>work_type_Govt_job</th>\n",
|
|
" <th>work_type_Never_worked</th>\n",
|
|
" <th>work_type_Private</th>\n",
|
|
" <th>work_type_Self-employed</th>\n",
|
|
" <th>work_type_children</th>\n",
|
|
" <th>Residence_type_Rural</th>\n",
|
|
" <th>Residence_type_Urban</th>\n",
|
|
" <th>smoking_status_formerly smoked</th>\n",
|
|
" <th>smoking_status_never smoked</th>\n",
|
|
" <th>smoking_status_smokes</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>0</td>\n",
|
|
" <td>3.0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>95.12</td>\n",
|
|
" <td>18.0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>0</td>\n",
|
|
" <td>58.0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>87.96</td>\n",
|
|
" <td>39.2</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>8.0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>110.89</td>\n",
|
|
" <td>17.6</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>70.0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>69.04</td>\n",
|
|
" <td>35.9</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>0</td>\n",
|
|
" <td>14.0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>161.28</td>\n",
|
|
" <td>19.1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>47.0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>210.95</td>\n",
|
|
" <td>50.1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>52.0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>77.59</td>\n",
|
|
" <td>17.7</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>75.0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>243.53</td>\n",
|
|
" <td>27.0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>32.0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>77.67</td>\n",
|
|
" <td>32.3</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>74.0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>205.84</td>\n",
|
|
" <td>54.6</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" gender age hypertension heart_disease ever_married avg_glucose_level \\\n",
|
|
"0 0 3.0 0 0 0 95.12 \n",
|
|
"1 0 58.0 1 0 1 87.96 \n",
|
|
"2 1 8.0 0 0 0 110.89 \n",
|
|
"3 1 70.0 0 0 1 69.04 \n",
|
|
"4 0 14.0 0 0 0 161.28 \n",
|
|
"5 1 47.0 0 0 1 210.95 \n",
|
|
"6 1 52.0 0 0 1 77.59 \n",
|
|
"7 1 75.0 0 1 1 243.53 \n",
|
|
"8 1 32.0 0 0 1 77.67 \n",
|
|
"9 1 74.0 1 0 1 205.84 \n",
|
|
"\n",
|
|
" bmi work_type_Govt_job work_type_Never_worked work_type_Private \\\n",
|
|
"0 18.0 0 0 0 \n",
|
|
"1 39.2 0 0 1 \n",
|
|
"2 17.6 0 0 1 \n",
|
|
"3 35.9 0 0 1 \n",
|
|
"4 19.1 0 1 0 \n",
|
|
"5 50.1 0 0 1 \n",
|
|
"6 17.7 0 0 1 \n",
|
|
"7 27.0 0 0 0 \n",
|
|
"8 32.3 0 0 1 \n",
|
|
"9 54.6 0 0 0 \n",
|
|
"\n",
|
|
" work_type_Self-employed work_type_children Residence_type_Rural \\\n",
|
|
"0 0 1 1 \n",
|
|
"1 0 0 0 \n",
|
|
"2 0 0 0 \n",
|
|
"3 0 0 1 \n",
|
|
"4 0 0 1 \n",
|
|
"5 0 0 0 \n",
|
|
"6 0 0 0 \n",
|
|
"7 1 0 1 \n",
|
|
"8 0 0 1 \n",
|
|
"9 1 0 0 \n",
|
|
"\n",
|
|
" Residence_type_Urban smoking_status_formerly smoked \\\n",
|
|
"0 0 0 \n",
|
|
"1 1 0 \n",
|
|
"2 1 0 \n",
|
|
"3 0 1 \n",
|
|
"4 0 0 \n",
|
|
"5 1 0 \n",
|
|
"6 1 1 \n",
|
|
"7 0 0 \n",
|
|
"8 0 0 \n",
|
|
"9 1 0 \n",
|
|
"\n",
|
|
" smoking_status_never smoked smoking_status_smokes \n",
|
|
"0 0 0 \n",
|
|
"1 1 0 \n",
|
|
"2 0 0 \n",
|
|
"3 0 0 \n",
|
|
"4 0 0 \n",
|
|
"5 0 0 \n",
|
|
"6 0 0 \n",
|
|
"7 1 0 \n",
|
|
"8 0 1 \n",
|
|
"9 1 0 "
|
|
]
|
|
},
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"X.head(10)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Target and Feature values / Train Test Split"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"((33550, 17), (8388, 17))"
|
|
]
|
|
},
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"X_train, X_test, y_train , y_test = train_test_split(X,y, test_size = 0.2, random_state = 42)\n",
|
|
"X_train.shape, X_test.shape"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## MODEL BUILDING"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### KNeighborsClassifier\n",
|
|
"\n",
|
|
"Classifier implementing the k-nearest neighbors vote.\n",
|
|
"\n",
|
|
"class sklearn.neighbors.KNeighborsClassifier(n_neighbors=5, *, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Model performance for Training set\n",
|
|
"- Accuracy: 0.9855141579731743\n",
|
|
"- MCC: 0.2714217088023517\n",
|
|
"- F1 score: 0.9803223813632442\n",
|
|
"----------------------------------\n",
|
|
"Model performance for Test set\n",
|
|
"- Accuracy: 0.9834287076776347\n",
|
|
"- MCC: -0.004865438031361363\n",
|
|
"- F1 score: 0.9767491714651221\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"knn = KNeighborsClassifier(3) # Define classifier\n",
|
|
"knn.fit(X_train, y_train) # Train model\n",
|
|
"\n",
|
|
"# Make predictions\n",
|
|
"y_train_pred = knn.predict(X_train)\n",
|
|
"y_test_pred = knn.predict(X_test)\n",
|
|
"\n",
|
|
"# Training set performance\n",
|
|
"knn_train_accuracy = accuracy_score(y_train, y_train_pred) # Calculate Accuracy\n",
|
|
"knn_train_mcc = matthews_corrcoef(y_train, y_train_pred) # Calculate MCC\n",
|
|
"knn_train_f1 = f1_score(y_train, y_train_pred, average='weighted') # Calculate F1-score\n",
|
|
"\n",
|
|
"# Test set performance\n",
|
|
"knn_test_accuracy = accuracy_score(y_test, y_test_pred) # Calculate Accuracy\n",
|
|
"knn_test_mcc = matthews_corrcoef(y_test, y_test_pred) # Calculate MCC\n",
|
|
"knn_test_f1 = f1_score(y_test, y_test_pred, average='weighted') # Calculate F1-score\n",
|
|
"\n",
|
|
"print('Model performance for Training set')\n",
|
|
"print('- Accuracy: %s' % knn_train_accuracy)\n",
|
|
"print('- MCC: %s' % knn_train_mcc)\n",
|
|
"print('- F1 score: %s' % knn_train_f1)\n",
|
|
"print('----------------------------------')\n",
|
|
"print('Model performance for Test set')\n",
|
|
"print('- Accuracy: %s' % knn_test_accuracy)\n",
|
|
"print('- MCC: %s' % knn_test_mcc)\n",
|
|
"print('- F1 score: %s' % knn_test_f1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Support Vector Classification (SVC)\n",
|
|
"\n",
|
|
"C-Support Vector Classification.\n",
|
|
"SVC, NuSVC and LinearSVC are classes capable of performing binary and multi-class classification on a dataset.\n",
|
|
"\n",
|
|
"The implementation is based on libsvm. The fit time scales at least quadratically with the number of samples and may be impractical beyond tens of thousands of samples.\n",
|
|
"\n",
|
|
"class sklearn.svm.SVC(*, C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', break_ties=False, random_state=None)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Model performance for Training set\n",
|
|
"- Accuracy: 0.9845901639344262\n",
|
|
"- MCC: 0.0\n",
|
|
"- F1 score: 0.9769450726235194\n",
|
|
"----------------------------------\n",
|
|
"Model performance for Test set\n",
|
|
"- Accuracy: 0.9849785407725322\n",
|
|
"- MCC: 0.0\n",
|
|
"- F1 score: 0.9775246491126318\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"svm_rbf = SVC(gamma='auto', C=1)\n",
|
|
"svm_rbf.fit(X_train, y_train)\n",
|
|
"\n",
|
|
"# Make predictions\n",
|
|
"y_train_pred = svm_rbf.predict(X_train)\n",
|
|
"y_test_pred = svm_rbf.predict(X_test)\n",
|
|
"\n",
|
|
"# Training set performance\n",
|
|
"svm_rbf_train_accuracy = accuracy_score(y_train, y_train_pred) # Calculate Accuracy\n",
|
|
"svm_rbf_train_mcc = matthews_corrcoef(y_train, y_train_pred) # Calculate MCC\n",
|
|
"svm_rbf_train_f1 = f1_score(y_train, y_train_pred, average='weighted') # Calculate F1-score\n",
|
|
"\n",
|
|
"# Test set performance\n",
|
|
"svm_rbf_test_accuracy = accuracy_score(y_test, y_test_pred) # Calculate Accuracy\n",
|
|
"svm_rbf_test_mcc = matthews_corrcoef(y_test, y_test_pred) # Calculate MCC\n",
|
|
"svm_rbf_test_f1 = f1_score(y_test, y_test_pred, average='weighted') # Calculate F1-score\n",
|
|
"\n",
|
|
"print('Model performance for Training set')\n",
|
|
"print('- Accuracy: %s' % svm_rbf_train_accuracy)\n",
|
|
"print('- MCC: %s' % svm_rbf_train_mcc)\n",
|
|
"print('- F1 score: %s' % svm_rbf_train_f1)\n",
|
|
"print('----------------------------------')\n",
|
|
"print('Model performance for Test set')\n",
|
|
"print('- Accuracy: %s' % svm_rbf_test_accuracy)\n",
|
|
"print('- MCC: %s' % svm_rbf_test_mcc)\n",
|
|
"print('- F1 score: %s' % svm_rbf_test_f1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Decision Tree Classifier\n",
|
|
"\n",
|
|
"A decision tree classifier.\n",
|
|
"Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. A tree can be seen as a piecewise constant approximation.\n",
|
|
"\n",
|
|
"class sklearn.tree.DecisionTreeClassifier(*, criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, class_weight=None, ccp_alpha=0.0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Model performance for Training set\n",
|
|
"- Accuracy: 0.9848286140089418\n",
|
|
"- MCC: 0.12344663420900479\n",
|
|
"- F1 score: 0.9775321005492623\n",
|
|
"----------------------------------\n",
|
|
"Model performance for Test set\n",
|
|
"- Accuracy: 0.9845016690510253\n",
|
|
"- MCC: -0.002697410465010962\n",
|
|
"- F1 score: 0.9772861696142702\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"dt = DecisionTreeClassifier(max_depth=5) # Define classifier\n",
|
|
"dt.fit(X_train, y_train) # Train model\n",
|
|
"\n",
|
|
"# Make predictions\n",
|
|
"y_train_pred = dt.predict(X_train)\n",
|
|
"y_test_pred = dt.predict(X_test)\n",
|
|
"\n",
|
|
"# Training set performance\n",
|
|
"dt_train_accuracy = accuracy_score(y_train, y_train_pred) # Calculate Accuracy\n",
|
|
"dt_train_mcc = matthews_corrcoef(y_train, y_train_pred) # Calculate MCC\n",
|
|
"dt_train_f1 = f1_score(y_train, y_train_pred, average='weighted') # Calculate F1-score\n",
|
|
"\n",
|
|
"# Test set performance\n",
|
|
"dt_test_accuracy = accuracy_score(y_test, y_test_pred) # Calculate Accuracy\n",
|
|
"dt_test_mcc = matthews_corrcoef(y_test, y_test_pred) # Calculate MCC\n",
|
|
"dt_test_f1 = f1_score(y_test, y_test_pred, average='weighted') # Calculate F1-score\n",
|
|
"\n",
|
|
"print('Model performance for Training set')\n",
|
|
"print('- Accuracy: %s' % dt_train_accuracy)\n",
|
|
"print('- MCC: %s' % dt_train_mcc)\n",
|
|
"print('- F1 score: %s' % dt_train_f1)\n",
|
|
"print('----------------------------------')\n",
|
|
"print('Model performance for Test set')\n",
|
|
"print('- Accuracy: %s' % dt_test_accuracy)\n",
|
|
"print('- MCC: %s' % dt_test_mcc)\n",
|
|
"print('- F1 score: %s' % dt_test_f1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Random Forest Classifier\n",
|
|
"\n",
|
|
"A random forest classifier.\n",
|
|
"\n",
|
|
"A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is controlled with the max_samples parameter if bootstrap=True (default), otherwise the whole dataset is used to build each tree.\n",
|
|
"\n",
|
|
"class sklearn.ensemble.RandomForestClassifier(n_estimators=100, *, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='sqrt', max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None, ccp_alpha=0.0, max_samples=None)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Model performance for Training set\n",
|
|
"- Accuracy: 0.9955886736214605\n",
|
|
"- MCC: 0.8429410132848043\n",
|
|
"- F1 score: 0.9952251633166039\n",
|
|
"----------------------------------\n",
|
|
"Model performance for Test set\n",
|
|
"- Accuracy: 0.9848593228421555\n",
|
|
"- MCC: -0.0013484639974045787\n",
|
|
"- F1 score: 0.9774650399810066\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"rf = RandomForestClassifier(n_estimators=10) # Define classifier\n",
|
|
"rf.fit(X_train, y_train) # Train model\n",
|
|
"\n",
|
|
"# Make predictions\n",
|
|
"y_train_pred = rf.predict(X_train)\n",
|
|
"y_test_pred = rf.predict(X_test)\n",
|
|
"\n",
|
|
"# Training set performance\n",
|
|
"rf_train_accuracy = accuracy_score(y_train, y_train_pred) # Calculate Accuracy\n",
|
|
"rf_train_mcc = matthews_corrcoef(y_train, y_train_pred) # Calculate MCC\n",
|
|
"rf_train_f1 = f1_score(y_train, y_train_pred, average='weighted') # Calculate F1-score\n",
|
|
"\n",
|
|
"# Test set performance\n",
|
|
"rf_test_accuracy = accuracy_score(y_test, y_test_pred) # Calculate Accuracy\n",
|
|
"rf_test_mcc = matthews_corrcoef(y_test, y_test_pred) # Calculate MCC\n",
|
|
"rf_test_f1 = f1_score(y_test, y_test_pred, average='weighted') # Calculate F1-score\n",
|
|
"\n",
|
|
"print('Model performance for Training set')\n",
|
|
"print('- Accuracy: %s' % rf_train_accuracy)\n",
|
|
"print('- MCC: %s' % rf_train_mcc)\n",
|
|
"print('- F1 score: %s' % rf_train_f1)\n",
|
|
"print('----------------------------------')\n",
|
|
"print('Model performance for Test set')\n",
|
|
"print('- Accuracy: %s' % rf_test_accuracy)\n",
|
|
"print('- MCC: %s' % rf_test_mcc)\n",
|
|
"print('- F1 score: %s' % rf_test_f1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Multi-layer Perceptron classifier\n",
|
|
"\n",
|
|
"This model optimizes the log-loss function using LBFGS or stochastic gradient descent.\n",
|
|
"\n",
|
|
"class sklearn.neural_network.MLPClassifier(hidden_layer_sizes=(100,), activation='relu', *, solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, n_iter_no_change=10, max_fun=15000)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Model performance for Training set\n",
|
|
"- Accuracy: 0.9845901639344262\n",
|
|
"- MCC: 0.0\n",
|
|
"- F1 score: 0.9769450726235194\n",
|
|
"----------------------------------\n",
|
|
"Model performance for Test set\n",
|
|
"- Accuracy: 0.9849785407725322\n",
|
|
"- MCC: 0.0\n",
|
|
"- F1 score: 0.9775246491126318\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"mlp = MLPClassifier(alpha=1, max_iter=1000)\n",
|
|
"mlp.fit(X_train, y_train)\n",
|
|
"\n",
|
|
"# Make predictions\n",
|
|
"y_train_pred = mlp.predict(X_train)\n",
|
|
"y_test_pred = mlp.predict(X_test)\n",
|
|
"\n",
|
|
"# Training set performance\n",
|
|
"mlp_train_accuracy = accuracy_score(y_train, y_train_pred) # Calculate Accuracy\n",
|
|
"mlp_train_mcc = matthews_corrcoef(y_train, y_train_pred) # Calculate MCC\n",
|
|
"mlp_train_f1 = f1_score(y_train, y_train_pred, average='weighted') # Calculate F1-score\n",
|
|
"\n",
|
|
"# Test set performance\n",
|
|
"mlp_test_accuracy = accuracy_score(y_test, y_test_pred) # Calculate Accuracy\n",
|
|
"mlp_test_mcc = matthews_corrcoef(y_test, y_test_pred) # Calculate MCC\n",
|
|
"mlp_test_f1 = f1_score(y_test, y_test_pred, average='weighted') # Calculate F1-score\n",
|
|
"\n",
|
|
"print('Model performance for Training set')\n",
|
|
"print('- Accuracy: %s' % mlp_train_accuracy)\n",
|
|
"print('- MCC: %s' % mlp_train_mcc)\n",
|
|
"print('- F1 score: %s' % mlp_train_f1)\n",
|
|
"print('----------------------------------')\n",
|
|
"print('Model performance for Test set')\n",
|
|
"print('- Accuracy: %s' % mlp_test_accuracy)\n",
|
|
"print('- MCC: %s' % mlp_test_mcc)\n",
|
|
"print('- F1 score: %s' % mlp_test_f1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Stacking Classifier\n",
|
|
"\n",
|
|
"Stack of estimators with a final classifier.\n",
|
|
"\n",
|
|
"Stacked generalization consists in stacking the output of individual estimator and use a classifier to compute the final prediction. Stacking allows to use the strength of each individual estimator by using their output as input of a final estimator.\n",
|
|
"\n",
|
|
"Note that estimators_ are fitted on the full X while final_estimator_ is trained using cross-validated predictions of the base estimators using cross_val_predict.\n",
|
|
"\n",
|
|
"class sklearn.ensemble.StackingClassifier(estimators, final_estimator=None, *, cv=None, stack_method='auto', n_jobs=None, passthrough=False, verbose=0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Model performance for Training set\n",
|
|
"- Accuracy: 0.9848286140089418\n",
|
|
"- MCC: 0.12344663420900479\n",
|
|
"- F1 score: 0.9775321005492623\n",
|
|
"----------------------------------\n",
|
|
"Model performance for Test set\n",
|
|
"- Accuracy: 0.9849785407725322\n",
|
|
"- MCC: 0.0\n",
|
|
"- F1 score: 0.9775246491126318\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"estimator_list = [\n",
|
|
" ('knn',knn),\n",
|
|
" ('svm_rbf',svm_rbf),\n",
|
|
" ('dt',dt),\n",
|
|
" ('rf',rf),\n",
|
|
" ('mlp',mlp) ]\n",
|
|
"\n",
|
|
"# Build stack model\n",
|
|
"stack_model = StackingClassifier(\n",
|
|
" estimators=estimator_list, final_estimator=LogisticRegression()\n",
|
|
")\n",
|
|
"\n",
|
|
"# Train stacked model\n",
|
|
"stack_model.fit(X_train, y_train)\n",
|
|
"\n",
|
|
"# Make predictions\n",
|
|
"y_train_pred = stack_model.predict(X_train)\n",
|
|
"y_test_pred = stack_model.predict(X_test)\n",
|
|
"\n",
|
|
"# Training set model performance\n",
|
|
"stack_model_train_accuracy = accuracy_score(y_train, y_train_pred) # Calculate Accuracy\n",
|
|
"stack_model_train_mcc = matthews_corrcoef(y_train, y_train_pred) # Calculate MCC\n",
|
|
"stack_model_train_f1 = f1_score(y_train, y_train_pred, average='weighted') # Calculate F1-score\n",
|
|
"\n",
|
|
"# Test set model performance\n",
|
|
"stack_model_test_accuracy = accuracy_score(y_test, y_test_pred) # Calculate Accuracy\n",
|
|
"stack_model_test_mcc = matthews_corrcoef(y_test, y_test_pred) # Calculate MCC\n",
|
|
"stack_model_test_f1 = f1_score(y_test, y_test_pred, average='weighted') # Calculate F1-score\n",
|
|
"\n",
|
|
"print('Model performance for Training set')\n",
|
|
"print('- Accuracy: %s' % stack_model_train_accuracy)\n",
|
|
"print('- MCC: %s' % stack_model_train_mcc)\n",
|
|
"print('- F1 score: %s' % stack_model_train_f1)\n",
|
|
"print('----------------------------------')\n",
|
|
"print('Model performance for Test set')\n",
|
|
"print('- Accuracy: %s' % stack_model_test_accuracy)\n",
|
|
"print('- MCC: %s' % stack_model_test_mcc)\n",
|
|
"print('- F1 score: %s' % stack_model_test_f1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Run the Line 2 only then model changed!!!\n",
|
|
"# joblib.dump(stack_model, 'stroke-prediction-model.joblib')\n",
|
|
"\n",
|
|
"model = joblib.load('stroke-prediction-model.joblib') # Model File"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## PREDICTION TESTING"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"######################################################### PROGRAM STARTED #########################################################\n",
|
|
"\n",
|
|
"Array 0 = [ 0 20 1 1 0 247 14 0 1 0 0 0 0 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 1 = [ 1 8 0 1 0 4 36 1 1 0 1 1 1 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 2 = [ 1 3 0 1 0 167 33 0 0 1 1 0 1 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 3 = [ 1 39 0 0 0 157 19 0 1 0 0 0 1 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 4 = [ 0 39 1 0 1 155 46 0 1 0 0 1 0 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 5 = [ 0 5 0 0 1 136 30 0 1 0 1 1 1 0 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 6 = [ 1 68 1 0 1 106 2 1 0 0 0 0 0 0 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 7 = [ 0 58 0 0 1 221 6 1 1 1 0 1 0 0 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 8 = [ 1 1 0 1 0 12 5 0 1 0 1 0 1 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 9 = [ 0 60 0 0 1 123 43 1 1 0 0 1 1 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 10 = [ 1 30 1 0 1 52 37 0 0 1 1 0 1 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 11 = [ 0 26 0 1 1 38 31 1 0 1 1 0 0 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 12 = [ 0 43 1 0 0 215 39 0 1 0 0 1 0 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 13 = [ 0 78 0 0 1 210 41 0 1 1 0 1 0 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 14 = [ 1 31 0 0 1 270 44 0 0 0 0 1 1 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 15 = [ 1 75 0 0 1 136 24 1 1 0 0 0 1 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 16 = [ 1 60 0 1 1 248 38 1 0 1 0 0 0 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 17 = [ 0 55 1 0 1 39 47 1 1 0 0 0 1 0 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 18 = [ 1 76 1 1 0 261 31 1 1 0 0 0 1 0 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 19 = [ 0 60 1 1 1 81 25 1 0 0 0 1 0 0 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 20 = [ 0 36 0 0 1 148 3 0 0 1 1 0 0 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 21 = [ 0 24 0 0 0 199 12 1 0 1 0 0 1 0 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 22 = [ 0 10 0 0 1 13 15 1 0 1 1 0 0 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 23 = [ 0 82 1 1 0 266 30 0 1 0 1 0 0 0 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 24 = [ 1 7 1 1 1 153 38 1 1 1 1 1 0 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 25 = [ 0 44 0 1 0 88 42 1 1 0 0 1 0 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 26 = [ 0 45 0 0 1 129 16 0 1 1 1 1 0 0 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 27 = [ 0 2 0 1 1 74 44 1 0 1 1 0 0 0 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 28 = [ 1 16 0 1 0 171 42 1 0 1 1 1 0 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 29 = [ 0 74 0 0 1 269 16 0 0 1 1 0 1 0 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 30 = [ 0 39 0 1 0 170 20 0 0 0 0 0 0 0 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 31 = [ 1 64 0 0 1 50 10 0 0 1 0 1 0 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 32 = [ 0 28 0 1 1 129 19 0 0 1 0 0 0 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 33 = [ 0 44 1 0 1 122 27 1 0 0 1 1 1 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 34 = [ 1 71 0 0 1 164 36 0 1 0 1 0 1 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 35 = [ 1 33 0 0 1 59 9 0 1 1 0 1 0 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 36 = [ 0 72 1 0 1 134 31 0 1 1 0 1 1 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 37 = [ 0 20 0 1 1 107 23 1 1 1 0 0 1 1 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 38 = [ 0 26 1 0 0 264 12 0 1 0 0 0 1 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 39 = [ 1 26 1 1 0 234 8 1 0 1 1 0 1 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 40 = [ 0 4 0 1 1 240 35 0 1 1 1 1 0 0 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 41 = [ 0 14 1 0 1 212 23 0 1 0 1 0 0 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 42 = [ 1 78 0 0 0 48 24 1 1 1 0 0 1 0 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 43 = [ 1 46 1 1 0 143 15 0 0 0 1 0 1 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 44 = [ 0 10 0 0 1 158 27 0 1 0 1 0 1 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 45 = [ 1 49 1 1 1 185 6 1 0 0 0 1 1 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 46 = [ 1 42 1 0 0 96 44 0 0 0 0 0 1 0 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 47 = [ 1 32 0 0 1 110 23 0 1 1 1 1 0 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 48 = [ 1 14 1 1 0 223 27 1 0 1 0 1 0 0 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 49 = [ 0 68 1 0 0 109 34 1 1 1 0 0 0 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 50 = [ 0 8 1 0 0 247 39 1 0 0 1 0 0 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 51 = [ 1 63 1 1 1 124 11 0 0 0 1 1 0 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 52 = [ 0 50 0 0 1 264 15 0 0 0 1 1 1 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 53 = [ 0 68 0 0 1 247 44 1 1 0 0 0 0 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 54 = [ 1 39 0 1 1 227 2 1 0 0 1 0 0 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 55 = [ 1 27 1 0 1 66 3 0 0 1 1 0 0 1 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 56 = [ 1 72 1 1 1 19 24 1 0 0 1 0 1 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 57 = [ 0 71 0 1 0 238 14 0 1 1 0 0 1 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 58 = [ 1 26 1 0 0 230 7 0 0 1 1 0 0 0 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 59 = [ 1 72 1 0 0 153 11 0 0 0 0 1 1 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 60 = [ 0 25 1 0 0 90 29 0 0 0 0 1 0 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 61 = [ 1 13 1 0 1 255 12 1 0 0 0 0 0 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 62 = [ 1 75 0 0 0 165 22 0 0 1 0 1 1 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 63 = [ 1 27 0 1 0 178 8 0 0 0 1 0 0 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 64 = [ 0 25 1 1 0 170 15 1 0 0 1 1 0 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 65 = [ 1 82 0 1 0 164 44 1 0 0 1 1 0 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 66 = [ 1 48 0 0 1 87 40 1 1 0 1 0 1 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 67 = [ 1 75 1 1 1 34 29 1 1 0 1 0 0 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 68 = [ 0 80 1 1 1 178 6 0 0 0 0 0 0 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 69 = [ 1 16 0 0 1 97 33 0 0 0 1 0 0 0 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 70 = [ 0 58 1 1 1 104 39 1 1 1 0 0 1 0 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 71 = [ 1 62 0 0 0 157 15 0 1 0 0 0 0 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 72 = [ 1 3 0 1 0 15 14 0 1 1 1 1 0 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 73 = [ 1 0 1 0 1 58 19 0 1 0 1 0 0 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 74 = [ 0 70 0 1 0 211 29 1 1 1 0 0 0 0 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 75 = [ 0 30 0 0 0 135 44 0 0 1 1 0 0 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 76 = [ 1 72 0 0 1 235 5 0 0 0 0 0 0 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 77 = [ 1 35 0 1 0 202 45 0 0 0 1 0 1 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 78 = [ 0 64 0 0 1 190 38 1 1 0 1 1 0 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 79 = [ 0 6 0 1 1 188 26 0 0 0 0 0 1 0 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 80 = [ 0 75 0 1 0 217 44 0 1 0 1 1 0 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 81 = [ 1 36 0 0 1 38 42 1 0 0 1 1 0 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 82 = [ 1 72 0 0 1 127 26 1 1 1 1 0 1 0 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 83 = [ 1 30 0 1 0 13 4 1 0 0 1 0 1 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 84 = [ 0 1 1 1 1 226 23 0 1 1 1 1 0 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 85 = [ 1 55 1 1 0 12 21 0 1 1 1 1 1 0 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 86 = [ 0 54 0 0 0 128 39 1 1 1 0 0 0 0 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 87 = [ 1 39 0 0 0 266 32 0 1 0 1 0 0 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 88 = [ 0 37 0 1 0 232 23 0 1 0 0 0 0 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 89 = [ 0 74 0 0 1 19 18 1 0 1 0 0 1 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 90 = [ 1 33 0 1 0 133 40 1 1 0 1 0 0 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 91 = [ 0 52 1 0 0 18 42 1 1 1 0 0 1 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 92 = [ 1 5 1 1 1 147 11 0 0 1 0 0 0 0 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 93 = [ 1 18 1 0 0 49 28 0 0 0 1 1 1 0 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 94 = [ 0 62 0 1 1 83 17 0 1 0 0 0 1 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 95 = [ 1 67 1 0 0 195 32 0 0 1 1 1 0 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 96 = [ 0 18 0 0 1 119 33 1 0 1 0 1 1 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 97 = [ 0 35 1 0 1 103 14 1 0 0 1 1 1 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 98 = [ 0 19 1 1 1 131 27 0 0 0 1 0 1 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 99 = [ 1 67 0 1 0 134 15 0 1 0 1 1 0 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 100 = [ 0 7 1 1 0 90 6 1 0 1 1 0 1 0 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 101 = [ 1 48 0 1 0 48 5 0 1 0 1 0 1 1 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 102 = [ 0 63 1 1 1 37 16 0 1 0 1 1 0 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 103 = [ 0 10 1 1 1 254 28 1 1 1 1 0 0 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 104 = [ 0 45 1 0 1 194 36 0 1 0 1 0 0 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 105 = [ 0 62 1 1 0 139 13 0 0 1 0 0 0 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 106 = [ 0 62 0 0 1 103 8 1 0 0 1 0 0 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 107 = [ 1 71 0 1 0 145 5 0 0 0 0 1 1 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 108 = [ 1 0 1 0 0 160 30 1 1 0 0 0 1 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 109 = [ 0 28 1 0 0 172 3 1 1 0 0 1 1 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 110 = [ 1 47 1 0 0 182 9 1 1 1 0 0 1 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 111 = [ 0 29 0 1 1 188 24 1 0 0 1 0 0 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 112 = [ 1 38 0 0 0 28 39 0 0 1 1 0 1 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 113 = [ 0 63 0 1 1 132 35 0 1 1 0 1 1 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 114 = [ 1 13 1 1 1 247 12 0 0 0 0 1 1 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 115 = [ 0 17 0 1 0 131 46 0 1 0 1 1 1 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 116 = [ 1 79 1 0 0 206 40 0 0 1 1 0 0 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 117 = [ 1 69 1 0 1 151 14 1 0 1 0 1 1 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 118 = [ 0 73 1 0 0 36 27 1 1 0 1 1 0 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 119 = [ 1 67 0 1 0 221 43 0 0 1 0 0 0 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 120 = [ 0 4 1 1 0 215 37 0 1 0 0 1 1 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 121 = [ 0 65 1 1 1 218 14 1 1 0 0 1 0 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 122 = [ 0 21 0 0 0 261 31 0 0 1 0 0 1 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 123 = [ 1 58 0 0 1 4 10 1 0 1 0 0 1 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 124 = [ 1 5 0 1 0 74 48 1 0 0 0 0 0 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 125 = [ 1 71 1 1 0 94 7 1 0 0 0 1 0 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 126 = [ 1 75 1 0 1 185 27 0 1 1 0 0 0 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 127 = [ 0 81 1 0 1 210 23 0 1 1 1 1 0 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 128 = [ 0 72 0 1 1 88 3 1 0 1 1 1 0 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 129 = [ 1 30 1 0 0 85 4 0 1 1 1 1 0 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 130 = [ 1 55 1 0 1 73 40 1 1 0 1 1 1 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 131 = [ 1 65 0 1 1 85 35 0 0 1 1 1 1 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 132 = [ 0 60 1 0 0 96 21 0 1 0 1 1 1 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 133 = [ 1 12 0 1 0 206 43 0 0 1 0 1 0 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 134 = [ 0 76 1 0 1 137 24 1 0 1 0 1 0 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 135 = [ 1 9 0 0 1 80 37 0 0 0 1 1 0 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 136 = [ 1 68 1 0 1 227 5 0 0 0 0 1 0 0 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 137 = [ 1 45 0 0 0 88 10 1 0 0 1 0 1 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 138 = [ 1 78 1 0 0 94 48 1 0 0 1 1 1 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 139 = [ 0 31 0 0 1 206 13 1 0 1 1 0 0 0 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 140 = [ 0 31 1 0 1 157 40 1 0 1 1 1 0 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 141 = [ 1 50 0 1 1 158 39 1 1 0 1 0 1 0 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 142 = [ 1 19 0 0 0 94 35 1 0 1 0 1 0 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 143 = [ 0 53 1 0 1 16 1 1 0 0 1 0 0 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 144 = [ 0 41 1 1 0 97 19 0 1 0 0 1 0 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 145 = [ 0 9 0 1 1 75 39 1 1 0 0 0 1 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 146 = [ 1 14 1 1 0 267 12 0 1 1 0 1 0 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 147 = [ 0 72 1 1 0 114 24 1 0 1 0 1 1 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 148 = [ 0 43 1 1 0 208 20 1 0 1 1 1 0 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 149 = [ 0 33 0 1 1 152 18 0 1 1 1 1 0 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 150 = [ 1 46 0 1 1 6 43 1 1 1 0 0 0 0 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 151 = [ 0 70 0 1 0 90 17 1 0 1 0 1 0 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 152 = [ 0 60 1 1 0 138 10 0 0 0 1 1 1 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 153 = [ 0 25 1 1 1 229 33 0 1 1 0 1 1 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 154 = [ 0 81 0 1 1 142 40 1 0 0 1 1 0 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 155 = [ 0 80 1 0 0 74 3 0 1 0 0 0 1 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 156 = [ 1 76 0 0 1 132 37 1 1 1 1 1 1 0 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 157 = [ 0 49 0 0 0 20 36 1 1 0 0 0 1 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 158 = [ 1 13 1 0 0 47 32 0 0 1 1 1 1 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 159 = [ 0 13 0 0 0 174 32 0 0 0 1 1 1 0 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 160 = [ 0 22 1 1 1 91 42 0 0 1 1 1 0 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 161 = [ 0 0 1 1 1 68 4 0 0 1 1 1 1 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 162 = [ 1 64 0 0 1 114 44 1 0 0 0 1 0 0 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 163 = [ 0 5 1 0 0 226 5 1 0 1 0 0 1 1 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 164 = [ 1 38 1 1 0 224 15 0 0 1 1 1 0 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 165 = [ 1 28 1 0 1 75 28 0 1 0 1 1 1 1 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 166 = [ 0 48 0 0 0 154 42 0 0 0 1 1 1 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 167 = [ 1 30 1 0 1 111 6 0 0 1 0 1 1 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 168 = [ 1 63 1 1 0 152 39 1 1 1 1 1 1 0 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 169 = [ 0 18 0 0 1 51 3 0 0 1 0 0 1 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 170 = [ 0 0 0 1 1 26 12 0 1 0 1 1 0 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 171 = [ 0 9 0 1 1 74 39 0 1 0 0 1 0 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 172 = [ 0 9 0 1 0 167 37 0 0 1 1 1 0 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 173 = [ 1 29 0 0 0 137 39 0 0 0 1 1 0 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 174 = [ 1 51 1 0 0 237 10 0 1 1 0 0 1 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 175 = [ 0 75 0 0 1 216 27 0 0 0 1 0 0 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 176 = [ 0 43 1 1 1 64 11 0 1 0 1 0 1 0 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 177 = [ 0 22 1 0 0 267 26 0 0 0 0 1 1 0 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 178 = [ 0 68 0 0 0 145 29 0 0 1 0 0 0 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 179 = [ 0 22 1 1 1 128 3 0 1 1 1 1 0 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 180 = [ 1 58 1 0 1 232 22 1 1 1 0 0 0 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 181 = [ 1 72 0 1 0 209 3 0 0 1 1 1 1 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 182 = [ 1 63 1 1 0 11 39 1 0 0 0 0 1 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 183 = [ 0 16 0 1 1 187 46 1 1 1 0 1 1 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 184 = [ 1 43 0 1 0 77 30 1 1 0 0 0 0 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 185 = [ 0 60 1 1 1 100 7 0 0 1 1 1 0 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 186 = [ 1 36 1 0 0 108 48 1 1 1 0 1 1 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 187 = [ 0 35 0 0 0 177 45 1 1 1 0 0 1 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 188 = [ 0 79 1 1 0 172 26 0 0 0 0 0 0 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 189 = [ 0 1 1 0 0 33 28 0 0 0 0 0 1 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 190 = [ 0 22 1 1 0 86 7 1 1 0 1 0 1 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 191 = [ 1 21 1 0 1 13 18 1 1 1 0 1 1 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 192 = [ 1 52 0 1 1 105 31 0 0 1 0 0 0 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 193 = [ 1 35 0 1 1 50 18 1 1 0 0 1 1 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 194 = [ 0 66 1 0 0 125 8 1 0 0 0 1 0 0 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 195 = [ 1 12 0 1 0 159 21 0 1 0 0 0 0 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 196 = [ 0 22 1 1 0 186 3 0 1 1 0 1 0 0 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 197 = [ 0 43 0 1 1 161 38 1 0 0 1 0 0 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 198 = [ 0 69 0 0 1 166 9 0 0 1 1 1 1 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 199 = [ 1 77 0 0 1 175 40 1 1 0 1 0 0 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 200 = [ 0 13 0 0 0 241 0 0 0 0 1 1 1 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 201 = [ 0 52 1 1 1 4 10 1 0 0 0 1 0 0 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 202 = [ 1 2 0 0 1 75 32 0 0 0 0 0 0 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 203 = [ 1 60 0 0 0 129 16 0 1 0 1 0 1 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 204 = [ 0 7 1 0 0 179 28 0 0 1 1 1 1 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 205 = [ 1 33 0 1 1 214 11 0 1 1 1 0 0 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 206 = [ 0 43 1 0 0 98 41 0 0 0 1 0 1 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 207 = [ 0 42 0 0 1 259 14 0 1 0 1 0 1 0 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 208 = [ 1 22 0 0 1 247 39 0 0 0 0 0 1 1 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 209 = [ 0 60 1 0 0 135 2 1 1 1 1 0 0 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 210 = [ 1 47 1 0 0 19 40 1 0 1 1 1 1 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 211 = [ 0 81 0 0 1 31 1 0 0 1 1 0 0 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 212 = [ 1 71 1 0 0 189 0 0 1 1 0 0 0 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 213 = [ 1 33 1 0 0 2 44 0 1 1 1 1 1 0 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 214 = [ 0 68 0 0 1 253 30 1 1 1 1 0 0 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 215 = [ 0 77 0 1 0 198 15 1 0 0 0 1 1 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 216 = [ 0 37 1 0 0 208 16 0 1 0 0 1 0 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 217 = [ 1 11 0 0 0 89 40 0 1 1 1 0 0 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 218 = [ 1 32 0 1 1 174 11 1 1 0 0 1 1 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 219 = [ 0 1 1 1 1 78 43 1 0 1 0 0 1 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 220 = [ 1 15 1 1 1 78 10 0 1 0 0 0 0 0 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 221 = [ 1 82 0 1 1 60 14 1 0 0 0 0 0 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 222 = [ 0 9 1 1 0 37 31 0 0 1 0 0 0 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 223 = [ 0 19 0 1 1 174 21 0 0 0 1 0 1 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 224 = [1 8 1 0 0 0 1 1 1 0 0 1 0 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 225 = [ 0 31 1 0 1 23 41 1 1 1 1 1 0 0 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 226 = [ 1 40 0 0 1 91 18 0 1 0 1 1 0 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 227 = [ 1 12 1 1 0 164 45 0 0 0 0 1 0 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 228 = [ 1 70 1 0 1 115 22 0 0 0 1 0 1 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 229 = [ 1 51 0 0 1 173 23 0 0 1 1 0 0 0 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 230 = [ 1 29 0 0 1 131 43 0 0 1 0 0 0 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 231 = [ 0 13 0 0 0 265 7 1 1 1 0 0 0 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 232 = [ 0 21 1 1 1 120 22 1 0 1 0 0 1 1 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 233 = [ 1 13 0 1 0 31 47 0 0 0 1 0 1 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 234 = [ 0 5 1 1 1 142 38 0 1 0 1 1 1 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 235 = [ 1 60 1 1 0 48 19 0 0 1 1 0 0 0 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 236 = [ 1 0 1 0 1 138 17 1 0 1 0 1 1 1 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 237 = [ 0 75 0 0 1 169 4 0 1 0 1 1 0 0 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 238 = [ 0 59 1 0 1 107 22 0 1 0 0 0 0 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 239 = [ 1 21 1 0 0 8 43 1 0 0 1 1 0 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 240 = [ 1 1 0 0 0 213 47 1 0 1 1 1 1 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 241 = [ 0 52 0 1 1 108 38 1 0 0 1 0 0 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 242 = [ 0 31 1 0 0 23 39 1 0 1 1 0 1 0 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 243 = [ 0 26 1 1 1 100 25 1 1 0 1 0 0 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 244 = [ 0 27 0 1 1 103 7 1 1 0 1 0 0 1 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 245 = [ 1 68 1 1 0 178 34 0 1 1 1 1 1 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 246 = [ 1 72 0 1 1 186 26 0 0 0 0 1 0 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 247 = [ 1 55 1 1 1 118 20 1 1 1 1 0 0 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 248 = [ 1 19 1 1 1 226 43 1 0 0 1 1 0 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 249 = [ 0 18 0 0 0 262 48 0 1 0 1 0 1 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 250 = [ 1 54 1 0 0 39 16 0 0 0 0 1 1 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 251 = [ 1 77 0 1 0 156 11 0 0 0 1 1 0 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 252 = [ 0 19 1 0 1 61 8 0 1 0 0 0 0 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 253 = [ 1 51 0 1 1 192 17 0 0 1 0 0 0 1 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 254 = [ 0 18 1 1 1 248 9 0 0 0 1 0 0 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 255 = [ 1 68 1 1 1 176 44 0 1 0 1 1 1 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 256 = [ 0 7 0 1 1 30 2 0 1 1 1 0 1 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 257 = [ 1 57 1 1 1 43 12 1 0 1 1 0 0 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 258 = [ 0 53 0 0 0 41 31 1 1 1 1 0 1 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 259 = [ 1 38 0 0 1 116 19 1 1 1 0 0 1 0 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 260 = [ 1 8 1 0 0 138 42 0 0 0 1 1 1 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 261 = [ 1 57 0 1 1 44 0 1 1 0 1 1 1 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 262 = [ 1 65 0 1 0 148 33 0 0 0 1 1 1 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 263 = [ 1 33 0 0 1 14 7 1 1 0 1 1 0 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 264 = [ 0 80 1 0 1 160 44 1 0 1 0 0 1 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 265 = [ 0 53 1 1 0 47 10 0 0 0 1 1 0 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 266 = [ 0 6 1 1 0 50 20 0 1 1 1 0 1 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 267 = [ 0 20 1 1 1 206 8 0 1 1 0 1 0 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 268 = [ 1 21 1 0 1 109 25 0 0 0 1 0 1 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 269 = [ 1 50 1 1 0 42 34 1 0 0 1 1 1 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 270 = [ 1 25 0 1 1 206 8 1 1 1 1 1 1 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 271 = [ 1 27 0 0 0 57 7 1 0 1 0 1 0 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 272 = [ 1 58 0 0 1 214 29 1 1 0 0 1 1 1 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 273 = [ 1 55 1 0 0 97 8 0 0 1 1 0 1 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 274 = [ 0 14 1 1 0 40 14 1 0 1 1 0 1 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 275 = [ 1 78 0 0 1 163 16 1 1 0 0 1 1 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 276 = [ 0 19 1 1 0 20 33 0 1 1 1 1 0 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 277 = [ 0 36 1 0 0 12 18 0 1 0 1 1 0 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 278 = [ 1 64 1 0 1 25 20 1 1 0 1 0 0 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 279 = [ 1 16 0 1 1 225 44 1 0 0 0 0 1 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 280 = [ 1 60 1 1 1 255 31 1 1 0 1 0 0 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 281 = [ 1 33 1 0 0 216 1 1 0 0 1 1 0 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 282 = [ 0 47 1 1 1 41 5 1 0 0 1 0 1 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 283 = [ 0 76 1 0 1 237 19 1 1 1 1 0 1 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 284 = [ 1 35 0 0 0 48 45 1 1 0 1 1 0 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 285 = [ 0 54 0 1 0 176 45 1 0 1 0 0 0 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 286 = [ 0 43 1 1 1 66 9 0 0 0 1 1 1 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 287 = [ 0 35 0 1 0 136 17 0 1 1 0 1 1 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 288 = [ 1 51 0 0 0 2 43 1 1 0 0 0 1 0 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 289 = [ 1 70 0 1 0 67 33 1 0 1 0 0 1 0 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 290 = [ 0 26 0 1 0 218 3 0 1 1 1 0 1 0 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 291 = [ 0 65 0 1 1 248 10 0 0 1 0 0 1 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 292 = [ 1 55 0 1 0 77 29 1 0 0 1 0 0 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 293 = [ 0 26 0 0 0 41 29 1 1 0 0 1 0 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 294 = [ 1 53 1 0 0 67 9 0 1 1 1 1 0 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 295 = [ 0 12 1 0 1 126 15 1 1 0 1 1 0 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 296 = [ 0 15 1 1 0 65 24 0 0 0 0 0 1 0 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 297 = [ 1 24 0 0 0 260 48 1 0 0 1 0 0 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 298 = [ 1 43 1 0 0 4 36 0 0 1 0 0 0 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 299 = [ 1 40 1 0 0 269 3 0 1 1 0 1 1 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 300 = [ 1 16 1 1 0 270 15 0 0 1 0 1 1 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 301 = [ 1 1 1 0 1 268 30 0 1 0 1 0 1 0 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 302 = [ 1 1 1 1 1 214 46 1 1 1 1 0 1 1 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 303 = [ 0 4 0 0 1 250 19 0 0 0 1 1 1 0 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 304 = [ 1 3 0 0 0 168 5 1 0 1 1 1 0 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 305 = [ 0 59 0 0 1 243 43 1 1 1 1 1 0 1 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 306 = [ 1 65 0 1 0 88 47 0 0 0 1 0 1 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 307 = [ 0 70 0 0 0 124 25 1 0 0 1 0 0 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 308 = [ 0 46 1 1 0 119 13 1 0 0 1 1 0 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 309 = [ 0 13 0 0 1 65 1 0 0 0 1 0 0 1 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 310 = [ 1 22 0 1 1 25 18 0 1 1 0 0 0 0 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 311 = [ 1 0 1 1 1 249 37 1 1 0 1 1 1 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 312 = [ 1 40 0 0 0 105 19 1 1 0 1 0 0 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 313 = [ 1 12 0 1 1 137 48 0 0 1 1 0 0 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 314 = [ 0 34 1 1 1 184 36 0 0 1 0 1 1 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 315 = [ 0 79 0 0 1 11 1 0 1 1 0 0 0 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 316 = [ 1 60 1 1 1 205 46 1 1 0 0 1 1 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 317 = [ 0 39 1 0 1 113 23 0 0 1 1 0 0 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 318 = [ 0 30 1 1 1 128 3 1 1 0 0 1 1 0 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 319 = [ 1 15 1 1 0 47 19 1 1 0 0 1 0 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 320 = [ 1 75 1 1 1 256 0 1 0 1 0 0 0 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 321 = [ 0 4 0 1 0 161 7 1 1 1 1 1 1 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 322 = [ 0 1 1 0 0 72 41 0 1 0 0 1 0 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 323 = [ 0 9 0 0 0 106 33 1 1 0 0 1 1 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 324 = [ 1 29 0 1 1 228 18 0 1 1 1 1 1 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 325 = [ 0 65 0 1 0 77 44 0 1 1 1 0 0 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 326 = [ 1 39 1 0 0 225 30 0 0 1 0 1 0 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 327 = [ 1 23 1 1 1 44 4 1 0 1 0 0 0 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 328 = [ 1 37 0 0 1 118 45 0 0 1 1 0 1 0 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 329 = [ 1 4 1 0 1 246 47 0 0 1 1 0 1 0 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 330 = [ 0 40 1 1 0 174 35 0 1 1 0 1 1 0 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 331 = [ 1 76 0 0 0 206 23 1 1 1 1 1 0 0 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 332 = [ 1 76 1 1 1 262 43 0 1 1 1 0 1 0 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 333 = [ 1 76 1 1 1 153 12 1 1 0 0 0 1 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 334 = [ 0 14 1 1 1 44 39 1 0 0 1 1 0 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 335 = [ 1 30 0 1 0 81 7 1 1 0 1 0 0 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 336 = [ 1 53 1 0 0 0 29 0 0 0 1 1 0 0 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 337 = [ 1 47 0 1 0 203 17 1 0 0 0 0 0 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 338 = [ 1 80 0 0 1 135 34 1 0 1 0 1 0 0 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 339 = [ 0 9 0 1 1 21 29 1 0 0 0 0 1 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 340 = [ 1 18 1 1 1 238 46 0 1 0 1 1 1 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 341 = [ 1 37 0 1 1 215 21 0 1 0 1 1 0 0 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 342 = [ 1 64 0 0 0 198 24 1 0 0 0 1 1 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 343 = [ 1 16 0 0 1 213 33 0 0 1 1 1 1 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 344 = [ 1 75 1 1 1 126 18 1 1 1 1 0 1 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 345 = [ 1 2 1 1 1 249 15 1 0 0 1 1 0 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 346 = [ 0 29 0 1 0 256 39 1 0 0 1 1 1 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 347 = [ 0 8 1 0 1 249 20 1 1 0 1 1 1 0 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 348 = [ 0 62 0 1 1 202 0 0 0 0 0 1 1 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 349 = [ 0 82 1 0 0 226 30 1 1 0 0 1 0 0 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 350 = [ 1 75 0 1 0 239 32 1 0 0 0 0 0 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 351 = [ 0 28 1 1 1 97 18 1 0 1 1 0 1 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 352 = [ 1 9 0 1 0 216 29 1 1 1 0 0 1 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 353 = [ 1 10 0 0 0 271 46 0 0 1 0 1 0 0 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 354 = [ 0 25 1 0 0 40 35 0 1 1 0 1 1 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 355 = [ 1 31 1 1 1 232 6 0 0 0 0 1 1 0 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 356 = [ 1 51 0 1 0 6 8 1 1 1 0 1 0 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 357 = [ 0 69 1 0 0 243 31 0 1 1 1 1 1 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 358 = [ 1 20 1 0 1 36 7 1 0 0 0 1 1 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 359 = [ 1 42 0 1 0 256 19 1 1 1 1 0 1 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 360 = [ 0 25 0 1 1 85 4 1 1 1 1 0 1 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 361 = [ 1 58 0 0 1 44 25 0 0 0 0 0 1 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 362 = [ 1 0 1 1 0 182 31 1 0 0 1 0 0 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 363 = [ 0 39 0 1 0 132 26 1 1 1 1 0 0 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 364 = [ 1 50 0 0 0 9 33 0 0 1 0 0 0 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 365 = [ 0 47 0 0 0 98 36 0 1 1 1 1 0 0 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 366 = [ 0 79 1 1 1 83 2 1 1 1 1 1 0 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 367 = [ 0 3 1 1 1 110 39 0 1 0 0 1 1 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 368 = [ 1 2 0 1 1 31 17 1 1 1 1 1 0 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 369 = [ 1 69 1 1 0 218 20 0 1 0 0 0 0 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 370 = [ 1 68 1 1 1 237 28 0 0 0 1 1 0 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 371 = [ 1 7 1 1 1 243 46 0 0 1 0 1 0 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 372 = [ 0 28 1 1 0 185 43 1 1 0 1 1 0 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 373 = [ 0 30 1 1 1 191 37 1 0 0 1 1 1 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 374 = [ 1 65 0 0 0 227 47 0 1 0 1 0 0 0 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 375 = [ 1 37 0 0 1 219 12 1 1 0 1 1 0 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 376 = [ 0 43 1 0 1 29 28 0 0 0 1 0 1 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 377 = [ 1 31 1 1 0 55 48 1 0 0 1 0 0 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 378 = [ 1 46 0 0 0 7 25 0 0 0 0 1 0 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 379 = [ 1 61 1 0 0 183 34 1 0 1 1 0 1 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 380 = [ 1 27 1 1 0 123 1 1 1 0 1 0 0 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 381 = [ 0 20 1 1 0 46 32 0 0 1 1 1 1 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 382 = [ 0 32 1 1 0 221 28 1 0 1 1 0 0 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 383 = [ 1 3 0 1 1 42 18 0 0 0 0 1 1 0 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 384 = [ 0 21 0 1 1 221 26 0 0 0 0 0 0 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 385 = [ 1 60 1 1 0 121 42 0 0 1 1 1 1 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 386 = [ 0 14 1 0 0 90 36 0 1 0 1 0 0 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 387 = [ 0 11 1 1 1 147 27 1 0 1 0 0 1 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 388 = [ 0 37 0 1 0 49 26 1 0 1 0 0 0 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 389 = [ 1 27 1 0 1 116 31 1 0 0 1 0 1 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 390 = [ 0 61 0 0 0 128 43 1 0 0 0 1 1 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 391 = [ 0 9 0 0 1 121 39 0 0 1 1 0 0 0 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 392 = [ 0 36 0 1 1 179 3 1 1 1 1 0 0 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 393 = [ 0 81 0 0 0 211 2 1 1 0 1 1 1 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 394 = [ 0 47 1 0 1 236 24 0 0 0 1 1 0 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 395 = [ 1 8 1 0 0 269 29 0 0 1 1 1 1 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 396 = [ 1 40 0 0 1 177 13 0 0 1 0 1 1 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 397 = [ 0 38 1 0 0 116 36 0 0 1 0 0 0 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 398 = [ 1 8 0 1 0 169 45 1 0 0 0 1 0 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 399 = [ 1 55 0 1 1 125 46 1 0 1 1 1 0 0 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 400 = [ 1 64 0 0 0 226 47 0 0 1 0 0 1 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 401 = [ 0 49 0 1 1 130 44 0 1 1 0 1 1 0 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 402 = [ 1 42 0 1 1 143 3 0 1 1 0 0 1 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 403 = [ 1 60 0 0 0 29 30 1 0 1 0 0 0 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 404 = [ 1 51 1 0 0 261 38 0 1 1 1 0 0 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 405 = [ 0 65 1 1 0 105 40 0 0 1 1 0 1 0 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 406 = [ 1 50 1 0 0 106 42 1 1 1 0 1 0 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 407 = [ 0 76 0 1 0 219 5 1 0 1 1 0 0 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 408 = [ 1 33 1 1 1 254 45 0 0 1 0 1 1 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 409 = [ 1 1 1 1 0 142 5 0 1 1 1 1 1 0 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 410 = [ 1 13 0 1 0 86 24 0 0 1 0 0 0 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 411 = [ 0 18 1 0 0 107 7 1 1 0 1 0 0 1 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 412 = [ 1 77 0 1 0 268 0 0 0 0 1 0 0 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 413 = [ 0 52 0 0 0 51 32 1 1 1 1 1 0 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 414 = [ 0 42 1 0 0 205 30 0 0 0 1 1 1 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 415 = [ 0 73 0 0 1 220 16 1 1 0 0 1 0 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 416 = [ 1 36 0 1 0 231 47 1 0 1 0 1 0 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 417 = [ 0 74 1 1 1 66 43 0 0 0 1 0 0 0 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 418 = [ 0 80 0 0 0 163 30 1 1 0 0 0 0 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 419 = [ 1 52 1 1 0 167 30 0 0 0 1 1 0 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 420 = [ 0 44 0 1 0 122 24 1 1 0 0 0 0 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 421 = [ 0 3 0 1 0 149 7 0 0 1 1 0 0 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 422 = [ 0 10 1 1 0 221 5 1 1 0 1 0 0 0 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 423 = [ 1 76 0 0 0 103 27 1 0 1 1 0 1 0 0 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 424 = [ 1 38 1 1 1 216 40 0 0 1 1 1 0 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 425 = [ 0 8 1 1 0 175 13 0 1 1 0 1 0 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 426 = [ 1 49 0 0 0 128 14 1 1 0 1 1 0 1 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 427 = [ 0 68 0 0 1 123 42 1 0 0 1 0 1 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 428 = [ 1 47 0 1 0 50 22 1 0 1 1 1 0 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 429 = [ 1 33 1 1 1 192 5 1 1 0 1 1 1 0 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 430 = [ 1 76 1 0 1 82 19 0 1 1 0 1 0 1 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 431 = [ 1 12 0 0 1 176 3 1 0 1 0 1 0 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 432 = [ 1 63 0 1 1 160 38 0 1 0 1 1 0 1 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 433 = [ 1 13 1 1 1 81 22 0 0 1 1 0 0 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 434 = [ 1 4 0 0 0 239 6 0 0 0 1 0 0 0 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 435 = [ 0 11 1 0 1 22 40 0 1 0 0 0 0 0 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 436 = [ 1 1 1 0 0 161 21 0 0 0 0 0 0 0 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 437 = [ 1 67 0 0 0 89 14 0 0 1 1 1 1 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 438 = [ 1 1 0 1 0 117 18 0 1 1 0 1 1 1 1 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 439 = [ 0 51 1 1 1 236 6 0 0 0 0 1 0 0 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 440 = [ 1 18 1 1 0 178 4 1 1 0 1 1 1 0 1 1 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 441 = [ 0 81 1 1 0 248 27 0 1 1 0 0 0 0 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 442 = [ 1 13 0 1 1 261 1 0 0 1 1 1 1 0 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 443 = [ 1 58 1 0 1 92 9 1 0 0 1 1 0 0 0 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 444 = [ 0 54 0 1 0 167 12 0 1 0 0 0 0 0 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 445 = [ 1 19 0 1 0 130 8 1 0 1 1 1 0 0 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 446 = [ 1 75 1 0 0 246 40 0 0 0 0 0 0 1 0 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 447 = [ 0 25 0 0 0 94 19 1 0 1 0 1 0 1 1 1 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 448 = [ 0 39 1 1 1 225 15 1 1 1 0 0 1 1 0 0 1]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 449 = [ 1 23 1 1 0 105 19 0 1 0 0 1 1 0 1 0 0]\n",
|
|
"Model predicts NO STROKE = [0]\n",
|
|
"###################################################################################################################################\n",
|
|
"\n",
|
|
"Array 450 = [ 0 77 0 0 1 271 18 0 1 1 1 1 1 1 0 1 0]\n",
|
|
"Model predicts STROKE = [1]\n",
|
|
"######################################################### PROGRAM FINISHED #########################################################\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import random\n",
|
|
"\n",
|
|
"count=0\n",
|
|
"array=[]\n",
|
|
"predictionOutcome=[0]\n",
|
|
"\n",
|
|
"print(\"######################################################### PROGRAM STARTED #########################################################\")\n",
|
|
"\n",
|
|
"while predictionOutcome == [0]:\n",
|
|
" arr1=np.random.randint(2, size=1)\n",
|
|
" arr2=np.random.randint(83, size=1)\n",
|
|
" arr3=np.random.randint(2, size=3)\n",
|
|
" arr4=np.random.randint(272, size=1)\n",
|
|
" arr5=np.random.randint(49, size=1)\n",
|
|
" arr6=np.random.randint(2, size=10)\n",
|
|
" \n",
|
|
" array = np.concatenate((arr1, arr2, arr3, arr4, arr5, arr6), axis=0)\n",
|
|
" \n",
|
|
" print(\"\\nArray %d =\" %count, array)\n",
|
|
" \n",
|
|
" predictionOutcome = model.predict([array])\n",
|
|
" \n",
|
|
" if predictionOutcome == 0:\n",
|
|
" print(\"Model predicts NO STROKE = \", predictionOutcome)\n",
|
|
" print(\"###################################################################################################################################\")\n",
|
|
" else:\n",
|
|
" print(\"Model predicts STROKE = \", predictionOutcome)\n",
|
|
" \n",
|
|
" count+=1\n",
|
|
"else:\n",
|
|
" print(\"######################################################### PROGRAM FINISHED #########################################################\")\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3.9.13 ('BrainStrokePredictionMLEnv')",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.13"
|
|
},
|
|
"orig_nbformat": 4,
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "54c59028773620d1ec7cf564885279046a3969c7da2b497982c8156e7da39d8c"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|