Datasets
Models
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{
"cells": [
{
"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"
]
},
{
"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",
" <td>120</td>\n",
" <td>80</td>\n",
" <td>80</td>\n",
" <td>98</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>140</td>\n",
" <td>83</td>\n",
" <td>75</td>\n",
" <td>100</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>155</td>\n",
" <td>100</td>\n",
" <td>92</td>\n",
" <td>104</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>115</td>\n",
" <td>82</td>\n",
" <td>79</td>\n",
" <td>97</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>119</td>\n",
" <td>79</td>\n",
" <td>85</td>\n",
" <td>102</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>144</th>\n",
" <td>154</td>\n",
" <td>99</td>\n",
" <td>83</td>\n",
" <td>103</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145</th>\n",
" <td>118</td>\n",
" <td>77</td>\n",
" <td>73</td>\n",
" <td>98</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
" <td>125</td>\n",
" <td>87</td>\n",
" <td>79</td>\n",
" <td>102</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147</th>\n",
" <td>132</td>\n",
" <td>88</td>\n",
" <td>78</td>\n",
" <td>101</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>148</th>\n",
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" <td>82</td>\n",
" <td>74</td>\n",
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" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<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"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data visualization"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x2265cf5ec48>"
]
},
"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": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x2265d6c6608>"
]
},
"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": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x2265d743c08>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"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": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x2265d79ef48>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"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'])"
]
},
{
"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"
]
},
{
"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"
]
},
{
"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",
" [126, 90, 80, 101],\n",
" [147, 95, 84, 104],\n",
" [116, 78, 70, 98],\n",
" [118, 76, 68, 99],\n",
" [128, 87, 80, 100],\n",
" [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",
" [100, 65, 79, 100],\n",
" [119, 76, 72, 98],\n",
" [118, 79, 70, 99],\n",
" [117, 78, 75, 98],\n",
" [145, 92, 82, 101],\n",
" [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",
" [140, 99, 85, 102],\n",
" [128, 88, 77, 98],\n",
" [126, 87, 76, 99],\n",
" [119, 78, 70, 98],\n",
" [118, 77, 75, 99],\n",
" [104, 68, 79, 101],\n",
" [100, 64, 69, 104],\n",
" [142, 93, 80, 102],\n",
" [126, 85, 79, 99],\n",
" [127, 84, 75, 100],\n",
" [118, 81, 72, 98],\n",
" [120, 78, 70, 99],\n",
" [119, 80, 75, 98],\n",
" [118, 78, 69, 98],\n",
" [127, 83, 79, 100],\n",
" [128, 86, 64, 101],\n",
" [149, 96, 82, 103],\n",
" [148, 95, 80, 102],\n",
" [ 98, 60, 64, 101],\n",
" [101, 68, 67, 104],\n",
" [126, 85, 76, 100],\n",
" [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",
" [105, 65, 60, 102],\n",
" [128, 88, 77, 98],\n",
" [115, 82, 79, 97],\n",
" [153, 101, 81, 102],\n",
" [118, 77, 70, 99],\n",
" [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",
" [118, 79, 71, 98],\n",
" [140, 83, 75, 100],\n",
" [146, 101, 82, 101],\n",
" [118, 79, 69, 98],\n",
" [119, 76, 72, 98],\n",
" [119, 79, 85, 102],\n",
" [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"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Feature scaling"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"No feature scaling is required."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Training and Testing the model"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.neighbors import KNeighborsClassifier\n",
"knn = KNeighborsClassifier(n_neighbors=5,metric='minkowski',p=2)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
" metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
" weights='uniform')"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"knn.fit(X_train,y_train)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from joblib import dump\n",
"dump(knn,'hms.save')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"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, 0, 0,\n",
" 2, 1, 0, 0, 0, 0, 0, 2], dtype=int64)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_pred=knn.predict(X_test)\n",
"y_pred"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Evaluation"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import accuracy_score\n",
"accuracy_score(y_test,y_pred)"
]
}
],
"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": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}