Datasets
Models
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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Importing the libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import os\n",
"os.chdir(\"F:\\Health-monitoring-system-using-ML-master\\Dataset\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Importing the dataset"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": false
},
"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>SBP</th>\n",
" <th>DBP</th>\n",
" <th>Pulse</th>\n",
" <th>Temperature</th>\n",
" <th>Level</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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"<p>149 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" SBP DBP Pulse Temperature Level\n",
"0 120 80 80 98 0\n",
"1 140 83 75 100 1\n",
"2 155 100 92 104 2\n",
"3 115 82 79 97 0\n",
"4 119 79 85 102 1\n",
".. ... ... ... ... ...\n",
"144 154 99 83 103 2\n",
"145 118 77 73 98 0\n",
"146 125 87 79 102 1\n",
"147 132 88 78 101 1\n",
"148 118 82 74 98 0\n",
"\n",
"[149 rows x 5 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset=pd.read_csv('Health monitoring.csv')\n",
"dataset"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data visualization"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\User\\.conda\\envs\\pandas\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
" warnings.warn(\n"
]
},
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='SBP'>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"sns.boxplot(dataset['SBP'])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\User\\.conda\\envs\\pandas\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
" warnings.warn(\n"
]
},
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='DBP'>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"sns.boxplot(dataset['DBP'])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\User\\.conda\\envs\\pandas\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
" warnings.warn(\n"
]
},
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='Pulse'>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"sns.boxplot(dataset['Pulse'])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\User\\.conda\\envs\\pandas\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
" warnings.warn(\n"
]
},
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='Temperature'>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAANlUlEQVR4nO3db5Bdd1nA8e+T3bamjpRmY4OzpbNl1igjg7VJsWUKFGiZwAwUnI4vdEycYfxTmG2acUT0VV/ojAW0tDuC1sKQDCoIZKA6MTHRaSnWFpPampQUWDFaAoV0i4U2tXWTxxfn7LCNe5Pdm3v32T/fz0xm75577zm/3/757rnn3nsSmYkkaeGtqh6AJK1UBliSihhgSSpigCWpiAGWpCKD87nx2rVrc2RkpE9DkaTlZ+3atezZs2dPZm469bp5BXhkZIT9+/f3bmSStAJExNrZlnsIQpKKGGBJKmKAJamIAZakIgZYkooYYEkqYoAlqYgBlqQiBliSihhgSSpigCWpiAGWpCIGWJKKGGBJKmKAJamIAZakIgZYkooYYEkqYoAlqci8/k84LR/j4+NMTExUD6Mnjh49CsDw8HBftzM6OsrY2Fhft6GVxQCvUBMTEzx86DAnzl9TPZSzNnD8aQCeeL5/P84Dx5/q27q1chngFezE+Wt47qffVj2Ms7b6sV0AfZ3L9DakXvIYsCQVMcCSVMQAS1IRAyxJRQywJBUxwJJUxABLUhEDLElFDLAkFTHAklTEAEtSEQMsSUUMsCQVMcCSVMQAS1IRAyxJRQywJBUxwJJUxABLUhEDLElFDLAkFTHAklTEAEtSEQMsSUUMsCQVMcCSVMQAS1IRAyxJRQywJBUxwJJUxABLUhEDLElFDLAkFTHAklTEAEtSEQMsSUUMsCQVMcCSVMQAS1IRAyxJRQywJBVZkACPj48zPj6+EJuStIIs9bYMLsRGJiYmFmIzklaYpd4WD0FIUhEDLElFDLAkFTHAklTEAEtSEQMsSUUMsCQVMcCSVMQAS1IRAyxJRQywJBUxwJJUxABLUhEDLElFDLAkFTHAklTEAEtSEQMsSUUMsCQVMcCSVMQAS1IRAyxJRQywJBUxwJJUxABLUhEDLElFDLAkFTHAklTEAEtSEQMsSUUMsCQVMcCSVMQAS1IRAyxJRQywJBUxwJJUxABLUhEDLElFDLAkncbk5CQ33XQTk5OTPV+3AZak09i+fTsHDx5kx44dPV+3AZakDiYnJ9m9ezeZye7du3u+FzzY07V1cPToUZ577jm2bt26EJvTHExMTLDqhawexpKx6n++z8TED/wZXmQmJiZYvXp139a/fft2Tp48CcCJEyfYsWMH27Zt69n6z7gHHBG/HhH7I2L/sWPHerZhSVrs9u3bx9TUFABTU1Ps3bu3p+s/4x5wZt4J3AmwcePGrnaZhoeHAbj99tu7ubv6YOvWrRz4xneqh7FknPyRlzD6inX+DC8y/X5Ecu2117Jr1y6mpqYYHBzkuuuu6+n6PQYsSR1s2bKFVauaTA4MDLB58+aert8AS1IHQ0NDbNq0iYhg06ZNDA0N9XT9C/IknCQtVVu2bOHIkSM93/sFAyxJpzU0NMQdd9zRl3V7CEKSihhgSSpigCWpiAGWpCIGWJKKGGBJKmKAJamIAZakIgZYkooYYEkqYoAlqYgBlqQiBliSihhgSSpigCWpiAGWpCIGWJKKGGBJKmKAJamIAZakIgZYkooYYEkqYoAlqYgBlqQiBliSihhgSSpigCWpiAGWpCIGWJKKGGBJKmKAJamIAZakIgZYkooYYEkqYoAlqYgBlqQiBliSihhgSSoyuBAbGR0dXYjNSFphlnpbFiTAY2NjC7EZSSvMUm+LhyAkqYgBlqQiBliSihhgSSpigCWpiAGWpCIGWJKKGGBJKmKAJamIAZakIgZYkooYYEkqYoAlqYgBlqQiBliSihhgSSpigCWpiAGWpCIGWJKKGGBJKmKAJamIAZakIgZYkooYYEkqYoAlqYgBlqQiBliSihhgSSpigCWpiAGWpCIGWJKKGGBJKmKAJamIAZakIgZYkooYYEkqYoAlqYgBlqQiBliSihhgSSoyWD0A1Rk4/hSrH9tVPYyzNnB8EqCvcxk4/hSwrm/r18pkgFeo0dHR6iH0zNGjUwAMD/czkOuW1ddMi4MBXqHGxsaqhyCteB4DlqQiBliSihhgSSpigCWpiAGWpCIGWJKKGGBJKmKAJamIAZakIgZYkooYYEkqYoAlqYgBlqQiBliSihhgSSpigCWpiAGWpCIGWJKKGGBJKmKAJalIZObcbxxxDPjPLre1Fniyy/suNstlLstlHuBcFqvlMpezmceTAJm56dQr5hXgsxER+zNz44JsrM+Wy1yWyzzAuSxWy2Uu/ZqHhyAkqYgBlqQiCxngOxdwW/22XOayXOYBzmWxWi5z6cs8FuwYsCTpxTwEIUlFDLAkFelLgCNia0QciohHI+LmU677rYjIiFjbj233Wqe5RMRYRDzWLv9A4RDnbLa5RMRlEfFARDwcEfsj4jXFw5xVRHw8Ir4bEYdmLFsTEXsj4uvtxwvb5RERd0TERET8W0RcXjfyF5vnPH65Hf/BiLg/In62buT/33zmMuP6KyJiKiJuWPgRdzbfuUTENe3vzKMRcW/XG87Mnv4DXgUcAs4HBoF9wGh73cuBPTRv5ljb620v1FyAN7aXz2tvd1H1WM9iLn8PvLW9zduAe6rH2mH8rwcuBw7NWPYB4P3t5fcDt86Yx98BAVwJPFg9/i7n8VrgwvbyWxfTPOY7l/bzAeAfgV3ADdXjP4vvy0uBrwCXtJ93/fvfjz3gV7Y/KMczcwq4F/iF9rrbgPcBS+WZv05zuRH4w8x8HiAzv1s4xrnqNJcEXtLe5gLgW0XjO63M/CLw1CmLrwe2t5e3A++csXxHNh4AXhoRP7EgAz2D+cwjM+/PzO+1yx8ALl6IMc7VPL8nAGPA54BF9/syz7n8ErAzM/+rvW/X8+lHgA8Br4uIoYg4n2Zv5OURcT1wNDMf6cM2+2XWuQDr2+UPRsS9EXFF6SjnptNcbgY+GBGPAx8CfrduiPO2LjO/3V5+AljXXh4GHp9xu2+2yxarTvOY6d00e/WL3axziYhh4F3AR6sG1oVO35f1wIURcU9EHIiIzd1uYPBsR3iqzDwcEbfSPLR9FngYOA/4PeAtvd5eP3WYywmar9samoe3VwB/HRGvyPbxyGJ0mrncCGzLzM9FxC8CHwOuLRtolzIzI2LRfv3narZ5RMQbaQJ8dc2ounPKXD4M/E5mnoyIwlF155S5DAIbgDcDq4F/jogHMvNr811vX56Ey8yPZeaGzHw98D3gUeBS4JGIOELzUOqhiHhZP7bfS7PM5Ws0e1Q724e4XwZO0pysY1HrMJctwM72Jp8BFuWTcB18Z/rQQvtx+qHgUZq9+2kXt8sWq07zICJeDdwFXJ+Zk0Xjm49Oc9kIfKr9/b8B+EhEvLNkhHPXaS7fBPZk5rOZ+STwRaCrJ0j79SqIi9qPl9AcZ9yemRdl5khmjtBM4PLMfKIf2++lWebyl8DnaZ6IIyLWA+eyBM741GEu3wLe0N7kTcDXa0bXlbtp/oDQfvzCjOWb21dDXAk8PeOh5GI06zza79NO4Fe62bsqMutcMvPSGb//nwXek5mfLxnh3HX6+foCcHVEDLaH834eONzVFvr0jOJ9NM8SPgK8eZbrj7AEXgXRaS40wf0kzXHVh4A3VY/zLOZyNXCgXfYgsKF6nB3G/lfAt4H/pfkD/m5gCPgHmj8a+4A17W0D+BPg34GDwMbq8Xc5j7toHqk83P7bXz3+budyyv0+weJ7FcS85gL8dvu7dAi4udvt+lZkSSriO+EkqYgBlqQiBliSihhgSSpigCWpSM/fCaeVKyKmX7YD8DKad9odaz9/TWa+UDKwWUTENcALmXl/8VC0ghlg9Uw279S6DCAibgGeycwPVY0nIgazOfHQbK4BngHmHOAzrE+aNw9BqK8iYkN7wqIDEbFnxls774mI29pzEB9uzxO7sz336u+3txmJ5pzLf9He5rPtO4/OtN4PR8R+YGtEvL09adK/RsS+iFgXESPAbwLb2nO6vi4iPjHzHLUR8Uz78ZqIuC8i7ga+EhEDEfHBiPiXaM7V+xsL+gXVsmKA1U8BjNO862kD8HHgD2Zc/0JmbgT+lObtne+lOW/xr7aHMwB+CvhIZr4S+D7wnog45wzrPTczN2bmHwFfAq7MzJ8DPgW8LzOPtNu8LTMvy8z7zjCPy4Gtmbme5h1ST2fmFTQnYvq1iLh0/l8ayUMQ6q/zaIK6tz0D1gDN2z2n3d1+PAg8mu35GiLiGzQn0/lv4PHM/Kf2dp8EbgJ2n2G9n55x+WLg0+0e8rnAf3Qxjy9n5vT93gK8esbe8gXAT3a5Xq1wBlj9FDRhvarD9c+3H0/OuDz9+fTP5qnvlc85rPfZGZfHgT/OzLvbJ95u6XCfKdpHhBGxiibWs60vgLHM3NNhPdKceQhC/fQ88OMRcRVARJwTET8zz3VcMn1/mv+J4EvAV+ex3gv44akot8xY/gPgx2Z8foTmHK8A7wDO6bC+PcCN7WEQImJ9RPzo3Kcj/ZABVj+dpDn3660R8QjNGb1eO891fBV4b0QcBi4EPtq+nG2u670F+ExEHODFpwz9G+Bd00/CAX8OvKFd31W8eK93prtozoL1UDT/geOf4SNJdcmzoWnRal+t8LeZ+arqsUj94B6wJBVxD1iSirgHLElFDLAkFTHAklTEAEtSEQMsSUX+D7So/ElUl5xGAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"sns.boxplot(dataset['Temperature'])"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Handling missing values"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"SBP False\n",
"DBP False\n",
"Pulse False\n",
"Temperature False\n",
"Level False\n",
"dtype: bool"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.isnull().any()"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"No missing value"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Handling textual data"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"No Textual data found in dataset\n",
"So Label Encoding and One hot Encoding is not required"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Splitting the data into Train and Test set"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"x=dataset.iloc[:,0:4].values\n",
"y=dataset.iloc[:,4].values"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[120, 80, 80, 98],\n",
" [140, 83, 75, 100],\n",
" [155, 100, 92, 104],\n",
" [115, 82, 79, 97],\n",
" [119, 79, 85, 102],\n",
" [ 95, 65, 75, 101],\n",
" [116, 75, 70, 99],\n",
" [100, 60, 79, 98],\n",
" [119, 78, 68, 100],\n",
" [110, 69, 65, 102],\n",
" [117, 73, 76, 99],\n",
" [130, 88, 84, 101],\n",
" [145, 90, 82, 104],\n",
" [118, 75, 75, 99],\n",
" [114, 76, 69, 98],\n",
" [105, 65, 60, 102],\n",
" [132, 89, 76, 101],\n",
" [135, 87, 76, 100],\n",
" [140, 91, 80, 102],\n",
" [119, 78, 70, 99],\n",
" [116, 77, 74, 98],\n",
" [106, 68, 79, 103],\n",
" [129, 89, 82, 100],\n",
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" [147, 95, 84, 104],\n",
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" [118, 76, 68, 99],\n",
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" [130, 85, 82, 101],\n",
" [148, 92, 84, 104],\n",
" [100, 65, 62, 95],\n",
" [ 99, 60, 65, 94],\n",
" [119, 78, 74, 98],\n",
" [117, 79, 76, 99],\n",
" [116, 80, 74, 98],\n",
" [115, 78, 68, 99],\n",
" [126, 85, 79, 100],\n",
" [127, 89, 80, 98],\n",
" [128, 88, 79, 101],\n",
" [120, 78, 70, 105],\n",
" [118, 79, 76, 106],\n",
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" [118, 79, 70, 99],\n",
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" [146, 90, 80, 104],\n",
" [127, 85, 79, 100],\n",
" [126, 88, 78, 98],\n",
" [120, 76, 70, 98],\n",
" [118, 79, 76, 100],\n",
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" [128, 88, 77, 98],\n",
" [126, 87, 76, 99],\n",
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" [126, 85, 79, 99],\n",
" [127, 84, 75, 100],\n",
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" [120, 78, 70, 99],\n",
" [119, 80, 75, 98],\n",
" [118, 78, 69, 98],\n",
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" [127, 87, 78, 98],\n",
" [128, 88, 77, 99],\n",
" [120, 79, 75, 98],\n",
" [119, 78, 72, 98],\n",
" [118, 77, 70, 99],\n",
" [101, 67, 78, 100],\n",
" [104, 68, 79, 101],\n",
" [105, 69, 76, 99],\n",
" [100, 63, 80, 103],\n",
" [114, 78, 75, 98],\n",
" [116, 79, 74, 98],\n",
" [117, 80, 73, 99],\n",
" [115, 78, 70, 98],\n",
" [150, 96, 82, 102],\n",
" [151, 95, 80, 100],\n",
" [149, 94, 83, 99],\n",
" [124, 84, 76, 98],\n",
" [120, 80, 76, 98],\n",
" [119, 78, 70, 98],\n",
" [118, 76, 74, 98],\n",
" [117, 79, 73, 98],\n",
" [118, 79, 76, 105],\n",
" [120, 80, 75, 105],\n",
" [117, 60, 85, 100],\n",
" [107, 65, 80, 102],\n",
" [ 99, 66, 79, 102],\n",
" [118, 79, 73, 99],\n",
" [131, 86, 76, 98],\n",
" [132, 87, 77, 98],\n",
" [132, 86, 75, 102],\n",
" [133, 92, 79, 100],\n",
" [134, 87, 78, 99],\n",
" [135, 89, 80, 100],\n",
" [136, 90, 79, 101],\n",
" [101, 68, 70, 99],\n",
" [102, 67, 65, 101],\n",
" [ 99, 68, 66, 102],\n",
" [145, 100, 75, 100],\n",
" [146, 101, 82, 101],\n",
" [146, 95, 81, 100],\n",
" [152, 98, 75, 99],\n",
" [154, 99, 79, 100],\n",
" [118, 79, 71, 98],\n",
" [119, 76, 70, 99],\n",
" [120, 78, 69, 98],\n",
" [117, 79, 68, 98],\n",
" [116, 80, 70, 98],\n",
" [119, 80, 73, 99],\n",
" [119, 79, 70, 98],\n",
" [125, 85, 75, 100],\n",
" [126, 87, 77, 101],\n",
" [128, 88, 79, 101],\n",
" [130, 85, 76, 100],\n",
" [148, 90, 76, 101],\n",
" [147, 92, 80, 99],\n",
" [153, 101, 81, 102],\n",
" [151, 99, 82, 103],\n",
" [118, 78, 68, 98],\n",
" [119, 79, 70, 98],\n",
" [120, 78, 71, 99],\n",
" [119, 78, 74, 98],\n",
" [ 98, 64, 76, 98],\n",
" [104, 67, 84, 103],\n",
" [106, 62, 78, 102],\n",
" [130, 87, 76, 100],\n",
" [116, 78, 73, 98],\n",
" [118, 79, 69, 98],\n",
" [117, 77, 74, 98],\n",
" [134, 86, 78, 100],\n",
" [136, 87, 80, 101],\n",
" [139, 88, 81, 100],\n",
" [147, 94, 79, 100],\n",
" [154, 99, 83, 103],\n",
" [118, 77, 73, 98],\n",
" [125, 87, 79, 102],\n",
" [132, 88, 78, 101],\n",
" [118, 82, 74, 98]], dtype=int64)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 2, 0, 1, 2, 0, 2, 0, 2, 0, 1, 2, 0, 0, 2, 1, 1, 2, 0, 0, 2,\n",
" 1, 1, 2, 0, 0, 1, 1, 2, 2, 2, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 0, 0,\n",
" 0, 2, 2, 1, 1, 0, 0, 2, 1, 1, 0, 0, 2, 2, 2, 1, 1, 0, 0, 0, 0, 1,\n",
" 1, 2, 2, 2, 2, 1, 1, 1, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2,\n",
" 1, 0, 0, 0, 0, 2, 2, 1, 2, 2, 0, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 0, 0, 0,\n",
" 0, 2, 2, 2, 1, 0, 0, 0, 1, 1, 1, 2, 2, 0, 1, 1, 0], dtype=int64)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train,X_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=0)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[128, 87, 80, 100],\n",
" [ 99, 66, 79, 102],\n",
" [107, 65, 80, 102],\n",
" [ 98, 60, 64, 101],\n",
" [140, 91, 80, 102],\n",
" [136, 90, 79, 101],\n",
" [130, 87, 76, 100],\n",
" [118, 81, 72, 98],\n",
" [140, 99, 85, 102],\n",
" [155, 100, 92, 104],\n",
" [125, 87, 79, 102],\n",
" [117, 73, 76, 99],\n",
" [104, 68, 79, 101],\n",
" [154, 99, 79, 100],\n",
" [147, 94, 79, 100],\n",
" [120, 78, 69, 98],\n",
" [128, 86, 64, 101],\n",
" [104, 68, 79, 101],\n",
" [126, 85, 76, 100],\n",
" [120, 78, 71, 99],\n",
" [119, 79, 70, 98],\n",
" [120, 80, 76, 98],\n",
" [118, 77, 73, 98],\n",
" [132, 87, 77, 98],\n",
" [118, 79, 76, 100],\n",
" [147, 92, 80, 99],\n",
" [127, 84, 75, 100],\n",
" [100, 65, 62, 95],\n",
" [119, 80, 75, 98],\n",
" [118, 76, 74, 98],\n",
" [117, 80, 73, 99],\n",
" [151, 95, 80, 100],\n",
" [128, 88, 79, 101],\n",
" [118, 79, 73, 99],\n",
" [126, 87, 77, 101],\n",
" [126, 88, 78, 98],\n",
" [118, 75, 75, 99],\n",
" [ 99, 68, 66, 102],\n",
" [117, 79, 73, 98],\n",
" [116, 77, 74, 98],\n",
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" [115, 82, 79, 97],\n",
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" [116, 75, 70, 99],\n",
" [148, 95, 80, 102],\n",
" [101, 68, 70, 99],\n",
" [118, 79, 76, 105],\n",
" [145, 90, 82, 104],\n",
" [130, 85, 76, 100],\n",
" [132, 88, 78, 101],\n",
" [132, 86, 75, 102],\n",
" [148, 90, 76, 101],\n",
" [146, 90, 80, 104],\n",
" [130, 88, 84, 101],\n",
" [102, 67, 65, 101],\n",
" [152, 98, 75, 99],\n",
" [100, 65, 79, 100],\n",
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" [140, 83, 75, 100],\n",
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" [151, 99, 82, 103],\n",
" [135, 87, 76, 100],\n",
" [128, 88, 79, 101],\n",
" [ 95, 65, 75, 101],\n",
" [126, 87, 76, 99],\n",
" [139, 88, 81, 100],\n",
" [133, 92, 79, 100],\n",
" [120, 80, 80, 98],\n",
" [116, 80, 74, 98],\n",
" [130, 85, 82, 101],\n",
" [118, 77, 75, 99],\n",
" [119, 78, 72, 98],\n",
" [115, 78, 68, 99],\n",
" [126, 90, 80, 101],\n",
" [120, 79, 75, 98],\n",
" [ 99, 60, 65, 94],\n",
" [125, 85, 75, 100],\n",
" [100, 64, 69, 104],\n",
" [119, 79, 70, 98],\n",
" [127, 83, 79, 100],\n",
" [119, 78, 74, 98],\n",
" [116, 78, 73, 98],\n",
" [114, 76, 69, 98],\n",
" [119, 80, 73, 99],\n",
" [119, 78, 70, 99],\n",
" [148, 92, 84, 104],\n",
" [118, 78, 68, 98],\n",
" [120, 76, 70, 98],\n",
" [106, 62, 78, 102],\n",
" [131, 86, 76, 98],\n",
" [116, 79, 74, 98],\n",
" [118, 78, 69, 98],\n",
" [118, 82, 74, 98],\n",
" [105, 69, 76, 99],\n",
" [119, 76, 70, 99],\n",
" [154, 99, 83, 103],\n",
" [127, 87, 78, 98],\n",
" [101, 67, 78, 100],\n",
" [116, 78, 70, 98],\n",
" [114, 78, 75, 98],\n",
" [117, 77, 74, 98],\n",
" [136, 87, 80, 101],\n",
" [120, 78, 70, 105],\n",
" [142, 93, 80, 102],\n",
" [124, 84, 76, 98],\n",
" [101, 68, 67, 104],\n",
" [149, 94, 83, 99],\n",
" [126, 85, 79, 100],\n",
" [106, 68, 79, 103],\n",
" [110, 69, 65, 102],\n",
" [134, 87, 78, 99],\n",
" [149, 96, 82, 103],\n",
" [117, 79, 68, 98],\n",
" [127, 85, 79, 100]], dtype=int64)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 98, 64, 76, 98],\n",
" [145, 100, 75, 100],\n",
" [126, 85, 79, 99],\n",
" [100, 63, 80, 103],\n",
" [100, 60, 79, 98],\n",
" [135, 89, 80, 100],\n",
" [134, 86, 78, 100],\n",
" [117, 60, 85, 100],\n",
" [116, 80, 70, 98],\n",
" [115, 78, 70, 98],\n",
" [117, 79, 76, 99],\n",
" [117, 78, 75, 98],\n",
" [119, 78, 70, 98],\n",
" [147, 95, 84, 104],\n",
" [127, 89, 80, 98],\n",
" [119, 78, 74, 98],\n",
" [146, 95, 81, 100],\n",
" [128, 88, 77, 99],\n",
" [132, 89, 76, 101],\n",
" [145, 92, 82, 101],\n",
" [118, 79, 76, 106],\n",
" [119, 78, 68, 100],\n",
" [150, 96, 82, 102],\n",
" [129, 89, 82, 100],\n",
" [120, 78, 70, 99],\n",
" [120, 80, 75, 105],\n",
" [119, 78, 70, 98],\n",
" [118, 76, 68, 99],\n",
" [118, 79, 70, 99],\n",
" [104, 67, 84, 103]], dtype=int64)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 2, 2, 2, 1, 1, 0, 2, 2, 1, 0, 2, 2, 2, 0, 1, 2, 1, 0, 0, 0,\n",
" 0, 1, 0, 2, 1, 2, 0, 0, 0, 2, 1, 0, 1, 1, 0, 2, 0, 0, 2, 1, 0, 2,\n",
" 0, 0, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 2, 0, 1, 2, 0, 0, 1, 2,\n",
" 1, 1, 2, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 2, 1, 2, 0, 1, 0, 0, 0,\n",
" 0, 0, 2, 0, 0, 2, 1, 0, 0, 0, 2, 0, 2, 1, 2, 0, 0, 0, 1, 2, 2, 1,\n",
" 2, 2, 1, 2, 2, 1, 2, 0, 1], dtype=int64)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_train"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([2, 2, 1, 2, 2, 1, 1, 1, 0, 0, 0, 0, 0, 2, 1, 0, 2, 1, 1, 2, 2, 0,\n",
" 2, 1, 0, 2, 0, 0, 0, 2], dtype=int64)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_test"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Feature scaling"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"sc1=StandardScaler()\n",
"X_train = sc1.fit_transform(X_train)\n",
"X_test = sc1.transform(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.30251906, 0.59096996, 0.86737175, 0.05006436],\n",
" [-1.68225901, -1.60997479, 0.68139475, 1.0430075 ],\n",
" [-1.13473402, -1.71478168, 0.86737175, 1.0430075 ],\n",
" [-1.75069963, -2.23881614, -2.10826035, 0.54653593],\n",
" [ 1.12380653, 1.01019753, 0.86737175, 1.0430075 ],\n",
" [ 0.85004404, 0.90539064, 0.68139475, 0.54653593],\n",
" [ 0.4394003 , 0.59096996, 0.12346373, 0.05006436],\n",
" [-0.38188717, -0.0378714 , -0.6204443 , -0.94287878],\n",
" [ 1.12380653, 1.84865267, 1.79725678, 1.0430075 ],\n",
" [ 2.15041587, 1.95345956, 3.09909583, 2.03595065],\n",
" [ 0.09719719, 0.59096996, 0.68139475, 1.0430075 ],\n",
" [-0.4503278 , -0.87632654, 0.12346373, -0.44640721],\n",
" [-1.34005589, -1.400361 , 0.68139475, 0.54653593],\n",
" [ 2.08197525, 1.84865267, 0.68139475, 0.05006436],\n",
" [ 1.60289089, 1.32461821, 0.68139475, 0.05006436],\n",
" [-0.24500593, -0.35229208, -1.17837532, -0.94287878],\n",
" [ 0.30251906, 0.48616306, -2.10826035, 0.54653593],\n",
" [-1.34005589, -1.400361 , 0.68139475, 0.54653593],\n",
" [ 0.16563781, 0.38135617, 0.12346373, 0.05006436],\n",
" [-0.24500593, -0.35229208, -0.8064213 , -0.44640721],\n",
" [-0.31344655, -0.24748518, -0.99239831, -0.94287878],\n",
" [-0.24500593, -0.14267829, 0.12346373, -0.94287878],\n",
" [-0.38188717, -0.45709897, -0.43446729, -0.94287878],\n",
" [ 0.57628155, 0.59096996, 0.30944073, -0.94287878],\n",
" [-0.38188717, -0.24748518, 0.12346373, 0.05006436],\n",
" [ 1.60289089, 1.11500442, 0.86737175, -0.44640721],\n",
" [ 0.23407843, 0.27654928, -0.06251328, 0.05006436],\n",
" [-1.61381838, -1.71478168, -2.48021436, -2.4322935 ],\n",
" [-0.31344655, -0.14267829, -0.06251328, -0.94287878],\n",
" [-0.38188717, -0.56190586, -0.24849029, -0.94287878],\n",
" [-0.4503278 , -0.14267829, -0.43446729, -0.44640721],\n",
" [ 1.87665338, 1.4294251 , 0.86737175, 0.05006436],\n",
" [ 0.30251906, 0.69577685, 0.68139475, 0.54653593],\n",
" [-0.38188717, -0.24748518, -0.43446729, -0.44640721],\n",
" [ 0.16563781, 0.59096996, 0.30944073, 0.54653593],\n",
" [ 0.16563781, 0.69577685, 0.49541774, -0.94287878],\n",
" [-0.38188717, -0.66671275, -0.06251328, -0.44640721],\n",
" [-1.68225901, -1.400361 , -1.73630633, 1.0430075 ],\n",
" [-0.4503278 , -0.24748518, -0.43446729, -0.94287878],\n",
" [-0.51876842, -0.45709897, -0.24849029, -0.94287878],\n",
" [-1.27161527, -1.71478168, -2.85216837, 1.0430075 ],\n",
" [ 0.30251906, 0.69577685, 0.30944073, -0.94287878],\n",
" [-0.58720904, 0.06693549, 0.68139475, -1.43935035],\n",
" [ 2.01353463, 2.05826645, 1.05334876, 1.0430075 ],\n",
" [-0.38188717, -0.45709897, -0.99239831, -0.44640721],\n",
" [-0.51876842, -0.66671275, -0.99239831, -0.44640721],\n",
" [ 1.67133151, 1.4294251 , 0.86737175, 1.0430075 ],\n",
" [-1.54537776, -1.400361 , -0.99239831, -0.44640721],\n",
" [-0.38188717, -0.24748518, 0.12346373, 2.53242222],\n",
" [ 1.46600964, 0.90539064, 1.23932576, 2.03595065],\n",
" [ 0.4394003 , 0.38135617, 0.12346373, 0.05006436],\n",
" [ 0.57628155, 0.69577685, 0.49541774, 0.54653593],\n",
" [ 0.57628155, 0.48616306, -0.06251328, 1.0430075 ],\n",
" [ 1.67133151, 0.90539064, 0.12346373, 0.54653593],\n",
" [ 1.53445027, 0.90539064, 0.86737175, 2.03595065],\n",
" [ 0.4394003 , 0.69577685, 1.61127978, 0.54653593],\n",
" [-1.47693714, -1.50516789, -1.92228334, 0.54653593],\n",
" [ 1.945094 , 1.74384578, -0.06251328, -0.44640721],\n",
" [-1.61381838, -1.71478168, 0.68139475, 0.05006436],\n",
" [-0.38188717, -0.24748518, -0.8064213 , -0.94287878],\n",
" [ 1.12380653, 0.17174239, -0.06251328, 0.05006436],\n",
" [ 1.53445027, 2.05826645, 1.23932576, 0.54653593],\n",
" [-0.38188717, -0.24748518, -1.17837532, -0.94287878],\n",
" [-0.31344655, -0.56190586, -0.6204443 , -0.94287878],\n",
" [-0.31344655, -0.24748518, 1.79725678, 1.0430075 ],\n",
" [ 1.87665338, 1.84865267, 1.23932576, 1.53947908],\n",
" [ 0.78160341, 0.59096996, 0.12346373, 0.05006436],\n",
" [ 0.30251906, 0.69577685, 0.68139475, 0.54653593],\n",
" [-1.9560215 , -1.71478168, -0.06251328, 0.54653593],\n",
" [ 0.16563781, 0.59096996, 0.12346373, -0.44640721],\n",
" [ 1.05536591, 0.69577685, 1.05334876, 0.05006436],\n",
" [ 0.64472217, 1.11500442, 0.68139475, 0.05006436],\n",
" [-0.24500593, -0.14267829, 0.86737175, -0.94287878],\n",
" [-0.51876842, -0.14267829, -0.24849029, -0.94287878],\n",
" [ 0.4394003 , 0.38135617, 1.23932576, 0.54653593],\n",
" [-0.38188717, -0.45709897, -0.06251328, -0.44640721],\n",
" [-0.31344655, -0.35229208, -0.6204443 , -0.94287878],\n",
" [-0.58720904, -0.35229208, -1.36435232, -0.44640721],\n",
" [ 0.16563781, 0.90539064, 0.86737175, 0.54653593],\n",
" [-0.24500593, -0.24748518, -0.06251328, -0.94287878],\n",
" [-1.68225901, -2.23881614, -1.92228334, -2.92876507],\n",
" [ 0.09719719, 0.38135617, -0.06251328, 0.05006436],\n",
" [-1.61381838, -1.81958857, -1.17837532, 2.03595065],\n",
" [-0.31344655, -0.24748518, -0.99239831, -0.94287878],\n",
" [ 0.23407843, 0.17174239, 0.68139475, 0.05006436],\n",
" [-0.31344655, -0.35229208, -0.24849029, -0.94287878],\n",
" [-0.51876842, -0.35229208, -0.43446729, -0.94287878],\n",
" [-0.65564966, -0.56190586, -1.17837532, -0.94287878],\n",
" [-0.31344655, -0.14267829, -0.43446729, -0.44640721],\n",
" [-0.31344655, -0.35229208, -0.99239831, -0.44640721],\n",
" [ 1.67133151, 1.11500442, 1.61127978, 2.03595065],\n",
" [-0.38188717, -0.35229208, -1.36435232, -0.94287878],\n",
" [-0.24500593, -0.56190586, -0.99239831, -0.94287878],\n",
" [-1.20317465, -2.02920236, 0.49541774, 1.0430075 ],\n",
" [ 0.50784092, 0.48616306, 0.12346373, -0.94287878],\n",
" [-0.51876842, -0.24748518, -0.24849029, -0.94287878],\n",
" [-0.38188717, -0.35229208, -1.17837532, -0.94287878],\n",
" [-0.38188717, 0.06693549, -0.24849029, -0.94287878],\n",
" [-1.27161527, -1.29555411, 0.12346373, -0.44640721],\n",
" [-0.31344655, -0.56190586, -0.99239831, -0.44640721],\n",
" [ 2.08197525, 1.84865267, 1.42530277, 1.53947908],\n",
" [ 0.23407843, 0.59096996, 0.49541774, -0.94287878],\n",
" [-1.54537776, -1.50516789, 0.49541774, 0.05006436],\n",
" [-0.51876842, -0.35229208, -0.99239831, -0.94287878],\n",
" [-0.65564966, -0.35229208, -0.06251328, -0.94287878],\n",
" [-0.4503278 , -0.45709897, -0.24849029, -0.94287878],\n",
" [ 0.85004404, 0.59096996, 0.86737175, 0.54653593],\n",
" [-0.24500593, -0.35229208, -0.99239831, 2.53242222],\n",
" [ 1.26068777, 1.21981131, 0.86737175, 1.0430075 ],\n",
" [ 0.02875656, 0.27654928, 0.12346373, -0.94287878],\n",
" [-1.54537776, -1.400361 , -1.55032933, 2.03595065],\n",
" [ 1.73977213, 1.32461821, 1.42530277, -0.44640721],\n",
" [ 0.16563781, 0.38135617, 0.68139475, 0.05006436],\n",
" [-1.20317465, -1.400361 , 0.68139475, 1.53947908],\n",
" [-0.92941215, -1.29555411, -1.92228334, 1.0430075 ],\n",
" [ 0.71316279, 0.59096996, 0.49541774, -0.44640721],\n",
" [ 1.73977213, 1.53423199, 1.23932576, 1.53947908],\n",
" [-0.4503278 , -0.24748518, -1.36435232, -0.94287878],\n",
" [ 0.23407843, 0.38135617, 0.68139475, 0.05006436]])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[-1.75069963, -1.81958857, 0.12346373, -0.94287878],\n",
" [ 1.46600964, 1.95345956, -0.06251328, 0.05006436],\n",
" [ 0.16563781, 0.38135617, 0.68139475, -0.44640721],\n",
" [-1.61381838, -1.92439546, 0.86737175, 1.53947908],\n",
" [-1.61381838, -2.23881614, 0.68139475, -0.94287878],\n",
" [ 0.78160341, 0.80058374, 0.86737175, 0.05006436],\n",
" [ 0.71316279, 0.48616306, 0.49541774, 0.05006436],\n",
" [-0.4503278 , -2.23881614, 1.79725678, 0.05006436],\n",
" [-0.51876842, -0.14267829, -0.99239831, -0.94287878],\n",
" [-0.58720904, -0.35229208, -0.99239831, -0.94287878],\n",
" [-0.4503278 , -0.24748518, 0.12346373, -0.44640721],\n",
" [-0.4503278 , -0.35229208, -0.06251328, -0.94287878],\n",
" [-0.31344655, -0.35229208, -0.99239831, -0.94287878],\n",
" [ 1.60289089, 1.4294251 , 1.61127978, 2.03595065],\n",
" [ 0.23407843, 0.80058374, 0.86737175, -0.94287878],\n",
" [-0.31344655, -0.35229208, -0.24849029, -0.94287878],\n",
" [ 1.53445027, 1.4294251 , 1.05334876, 0.05006436],\n",
" [ 0.30251906, 0.69577685, 0.30944073, -0.44640721],\n",
" [ 0.57628155, 0.80058374, 0.12346373, 0.54653593],\n",
" [ 1.46600964, 1.11500442, 1.23932576, 0.54653593],\n",
" [-0.38188717, -0.24748518, 0.12346373, 3.02889379],\n",
" [-0.31344655, -0.35229208, -1.36435232, 0.05006436],\n",
" [ 1.80821276, 1.53423199, 1.23932576, 1.0430075 ],\n",
" [ 0.37095968, 0.80058374, 1.23932576, 0.05006436],\n",
" [-0.24500593, -0.35229208, -0.99239831, -0.44640721],\n",
" [-0.24500593, -0.14267829, -0.06251328, 2.53242222],\n",
" [-0.31344655, -0.35229208, -0.99239831, -0.94287878],\n",
" [-0.38188717, -0.56190586, -1.36435232, -0.44640721],\n",
" [-0.38188717, -0.24748518, -0.99239831, -0.44640721],\n",
" [-1.34005589, -1.50516789, 1.61127978, 1.53947908]])"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Training and Testing the model"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.neighbors import KNeighborsClassifier\n",
"knn = KNeighborsClassifier(n_neighbors=5,metric='minkowski',p=2)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
],
"text/plain": [
"KNeighborsClassifier()"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"knn.fit(X_train,y_train)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['hms.save']"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from joblib import dump\n",
"dump(knn,'hms.save')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([2, 2, 1, 2, 2, 1, 1, 2, 0, 0, 0, 0, 0, 2, 1, 0, 2, 1, 1, 2, 2, 0,\n",
" 2, 1, 0, 2, 0, 0, 0, 2], dtype=int64)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_pred=knn.predict(X_test)\n",
"y_pred"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Evaluation"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9666666666666667"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import accuracy_score\n",
"accuracy_score(y_test,y_pred)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 4
}