module-3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.\tMasukkan iris.csv ke dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal.length</th>\n",
       "      <th>sepal.width</th>\n",
       "      <th>petal.length</th>\n",
       "      <th>petal.width</th>\n",
       "      <th>variety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</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>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal.length  sepal.width  petal.length  petal.width    variety\n",
       "0             5.1          3.5           1.4          0.2     Setosa\n",
       "1             4.9          3.0           1.4          0.2     Setosa\n",
       "2             4.7          3.2           1.3          0.2     Setosa\n",
       "3             4.6          3.1           1.5          0.2     Setosa\n",
       "4             5.0          3.6           1.4          0.2     Setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  Virginica\n",
       "146           6.3          2.5           5.0          1.9  Virginica\n",
       "147           6.5          3.0           5.2          2.0  Virginica\n",
       "148           6.2          3.4           5.4          2.3  Virginica\n",
       "149           5.9          3.0           5.1          1.8  Virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('..\\..\\..\\..\\..\\iris.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.\tNormalisasikan setiap attribut iris ke dataframe menggunakan normalisasi min max sehingga setiap nilai attribut memiliki range 0 – 1. Vbaru = (Vlama - Vmin) / (Vmax - Vmin). Vbaru = nilai data di suatu sel setelah normalisasi, Vlama = nilai data asli sebelum normalisasi, Vmax = nilai max dari kolom data tersebut, Vmin = nilai min dari kolom data tersebut       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>sepal.length</th>\n",
       "      <th>sepal.width</th>\n",
       "      <th>petal.length</th>\n",
       "      <th>petal.width</th>\n",
       "      <th>variety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.458333</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.194444</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>Setosa</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>145</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.711864</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.677966</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.711864</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>0.527778</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.745763</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.694915</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal.length  sepal.width  petal.length  petal.width    variety\n",
       "0        0.222222     0.625000      0.067797     0.041667     Setosa\n",
       "1        0.166667     0.416667      0.067797     0.041667     Setosa\n",
       "2        0.111111     0.500000      0.050847     0.041667     Setosa\n",
       "3        0.083333     0.458333      0.084746     0.041667     Setosa\n",
       "4        0.194444     0.666667      0.067797     0.041667     Setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145      0.666667     0.416667      0.711864     0.916667  Virginica\n",
       "146      0.555556     0.208333      0.677966     0.750000  Virginica\n",
       "147      0.611111     0.416667      0.711864     0.791667  Virginica\n",
       "148      0.527778     0.583333      0.745763     0.916667  Virginica\n",
       "149      0.444444     0.416667      0.694915     0.708333  Virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def normal(data):\n",
    "    return (df[data] - df[data].min()) / (df[data].max() - df[data].min())\n",
    "\n",
    "df[\"sepal.length\"] = normal(\"sepal.length\")\n",
    "df[\"sepal.width\"] = normal(\"sepal.width\")\n",
    "df[\"petal.length\"] = normal(\"petal.length\")\n",
    "df[\"petal.width\"] = normal(\"petal.width\")\n",
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.\tCari centroid dari data yang telah dinormalisasi. Centroid dihitung dari rata2 seluruh data suatu kolom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======Centroid Setosa========\n",
      "sepal.length    0.196111\n",
      "sepal.width     0.595000\n",
      "petal.length    0.078305\n",
      "petal.width     0.060833\n",
      "dtype: float64 \n",
      "========Centroid versicolor========\n",
      "sepal.length    0.454444\n",
      "sepal.width     0.320833\n",
      "petal.length    0.552542\n",
      "petal.width     0.510833\n",
      "dtype: float64 \n",
      "========Centroid virginica========\n",
      "sepal.length    0.635556\n",
      "sepal.width     0.405833\n",
      "petal.length    0.771525\n",
      "petal.width     0.802500\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nasrul Wahabi\\AppData\\Local\\Temp\\ipykernel_18324\\68987014.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  cen_setosa = df.loc[df[\"variety\"] == \"Setosa\"].mean()\n",
      "C:\\Users\\Nasrul Wahabi\\AppData\\Local\\Temp\\ipykernel_18324\\68987014.py:2: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  cen_versicolor = df.loc[df[\"variety\"] == \"Versicolor\"].mean()\n",
      "C:\\Users\\Nasrul Wahabi\\AppData\\Local\\Temp\\ipykernel_18324\\68987014.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  cen_virginica = df.loc[df[\"variety\"] == \"Virginica\"].mean()\n"
     ]
    }
   ],
   "source": [
    "cen_setosa = df.loc[df[\"variety\"] == \"Setosa\"].mean()\n",
    "cen_versicolor = df.loc[df[\"variety\"] == \"Versicolor\"].mean()\n",
    "cen_virginica = df.loc[df[\"variety\"] == \"Virginica\"].mean()\n",
    "\n",
    "print(\"=======Centroid Setosa========\\n{} \\n========Centroid versicolor========\\n{} \\n========Centroid virginica========\\n{}\".format(cen_setosa, cen_versicolor, cen_virginica))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.\tMasukkan iristesting.csv ke dataframe. Normalisasikan iristesting.csv menggunakan vmin dan vmax pada data 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal.length</th>\n",
       "      <th>sepal.width</th>\n",
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       "      <th>petal.width</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.065217</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.28</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.16</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.28</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.065217</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.08</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.44</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.673913</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>Versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.68</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.717391</td>\n",
       "      <td>0.521739</td>\n",
       "      <td>Versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.56</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.586957</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>Versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>Versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.48</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.630435</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>Versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.52</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.630435</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>Versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.52</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.630435</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>Versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>Versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.28</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.369565</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>Versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.52</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.608696</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>Versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.934783</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.826087</td>\n",
       "      <td>0.913043</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.56</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.826087</td>\n",
       "      <td>0.739130</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.96</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.913043</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.847826</td>\n",
       "      <td>0.913043</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.804348</td>\n",
       "      <td>0.739130</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.84</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.847826</td>\n",
       "      <td>0.782609</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.891304</td>\n",
       "      <td>0.913043</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.60</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.826087</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal.length  sepal.width  petal.length  petal.width     variety\n",
       "0           0.24     0.800000      0.000000     0.043478      Setosa\n",
       "1           0.04     0.000000      0.000000     0.043478      Setosa\n",
       "2           0.00     0.600000      0.000000     0.000000      Setosa\n",
       "3           0.24     0.800000      0.065217     0.173913      Setosa\n",
       "4           0.28     1.000000      0.130435     0.086957      Setosa\n",
       "5           0.16     0.466667      0.021739     0.043478      Setosa\n",
       "6           0.28     1.000000      0.065217     0.000000      Setosa\n",
       "7           0.08     0.600000      0.021739     0.000000      Setosa\n",
       "8           0.36     0.933333      0.043478     0.000000      Setosa\n",
       "9           0.24     0.666667      0.021739     0.000000      Setosa\n",
       "10          0.44     0.200000      0.673913     0.434783  Versicolor\n",
       "11          0.68     0.466667      0.717391     0.521739  Versicolor\n",
       "12          0.56     0.200000      0.586957     0.434783  Versicolor\n",
       "13          0.24     0.000000      0.434783     0.347826  Versicolor\n",
       "14          0.48     0.266667      0.630435     0.478261  Versicolor\n",
       "15          0.52     0.466667      0.630435     0.434783  Versicolor\n",
       "16          0.52     0.400000      0.630435     0.478261  Versicolor\n",
       "17          0.72     0.400000      0.652174     0.478261  Versicolor\n",
       "18          0.28     0.133333      0.369565     0.391304  Versicolor\n",
       "19          0.52     0.333333      0.608696     0.478261  Versicolor\n",
       "20          0.92     0.533333      0.934783     0.956522   Virginica\n",
       "21          1.00     0.533333      0.826087     0.913043   Virginica\n",
       "22          0.56     0.266667      0.826087     0.739130   Virginica\n",
       "23          0.96     0.600000      1.000000     0.913043   Virginica\n",
       "24          0.92     0.666667      0.956522     1.000000   Virginica\n",
       "25          0.92     0.466667      0.847826     0.913043   Virginica\n",
       "26          0.76     0.133333      0.804348     0.739130   Virginica\n",
       "27          0.84     0.466667      0.847826     0.782609   Virginica\n",
       "28          0.72     0.733333      0.891304     0.913043   Virginica\n",
       "29          0.60     0.466667      0.826087     0.695652   Virginica"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftest = pd.read_csv('..\\..\\..\\..\\..\\iristesting.csv')\n",
    "dftest\n",
    "\n",
    "def normal(value):\n",
    "    return (dftest[value] - dftest[value].min()) / (dftest[value].max() - dftest[value].min())\n",
    "\n",
    "dftest['sepal.length'] = normal('sepal.length')\n",
    "dftest['sepal.width'] = normal('sepal.width')\n",
    "dftest['petal.length'] = normal('petal.length')\n",
    "dftest['petal.width'] = normal('petal.width')\n",
    "dftest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5.\tPrediksilah kelas data di iristesting.csv menggunakan kriteria kedekatan distance antara setiap data di iristesting.csv ke centroid masing2 class di iris. Gunakan euclidean distance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nasrul Wahabi\\AppData\\Local\\Temp\\ipykernel_18324\\3173138841.py:4: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  cen_class = dftest[dftest['variety'] == i].mean()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     True\n",
      "1     True\n",
      "2     True\n",
      "3     True\n",
      "4     True\n",
      "5     True\n",
      "6     True\n",
      "7     True\n",
      "8     True\n",
      "9     True\n",
      "10    True\n",
      "11    True\n",
      "12    True\n",
      "13    True\n",
      "14    True\n",
      "15    True\n",
      "16    True\n",
      "17    True\n",
      "18    True\n",
      "19    True\n",
      "20    True\n",
      "21    True\n",
      "22    True\n",
      "23    True\n",
      "24    True\n",
      "25    True\n",
      "26    True\n",
      "27    True\n",
      "28    True\n",
      "29    True\n",
      "Name: variety, dtype: bool\n",
      "0         Setosa\n",
      "1         Setosa\n",
      "2         Setosa\n",
      "3         Setosa\n",
      "4         Setosa\n",
      "5         Setosa\n",
      "6         Setosa\n",
      "7         Setosa\n",
      "8         Setosa\n",
      "9         Setosa\n",
      "10    Versicolor\n",
      "11    Versicolor\n",
      "12    Versicolor\n",
      "13    Versicolor\n",
      "14    Versicolor\n",
      "15    Versicolor\n",
      "16    Versicolor\n",
      "17    Versicolor\n",
      "18    Versicolor\n",
      "19    Versicolor\n",
      "20     Virginica\n",
      "21     Virginica\n",
      "22     Virginica\n",
      "23     Virginica\n",
      "24     Virginica\n",
      "25     Virginica\n",
      "26     Virginica\n",
      "27     Virginica\n",
      "28     Virginica\n",
      "29     Virginica\n",
      "Name: variety, dtype: object\n"
     ]
    }
   ],
   "source": [
    "def euclid(indeks, value):\n",
    "  dist = []\n",
    "  for i in ['Setosa', 'Versicolor', 'Virginica']:\n",
    "    cen_class = dftest[dftest['variety'] == i].mean()\n",
    "    test = value.iloc[indeks, 0:4]\n",
    "    a = ((test[0]-cen_class[0])**2 + (test[1]-cen_class[1])**2 + (test[2]-cen_class[2])**2 + (test[3]-cen_class[3])**2)**1/2\n",
    "    dist.append(a)\n",
    "  if (min(dist)== dist[0]):\n",
    "    return 'Setosa'\n",
    "  elif (min(dist)== dist[1]):\n",
    "    return \"Versicolor\"\n",
    "  elif (min(dist)== dist[2]):\n",
    "    return \"Virginica\"\n",
    "  else :\n",
    "    return None\n",
    "\n",
    "temp = []\n",
    "for x in dftest.index:\n",
    "  temp.append(euclid (x,dftest))\n",
    "print(dftest['variety'] == temp)\n",
    "print(dftest['variety'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 100.0%\n"
     ]
    }
   ],
   "source": [
    "print(f\"Accuracy : {(dftest['variety'] == temp).sum()/len(dftest)*100}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6.\tHitunglah berapa persen klasifikasi yang benar (akurasi) saat memprediksi class "
   ]
  }
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