orthopedic_patients_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN Algoritması\n",
    "* import dataset\n",
    "* datasetimi tanımı\n",
    "* dataset görselleştirme\n",
    "* knn algoritması ne demek açıkla\n",
    "* knn with sklearn\n",
    "* ödev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
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       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
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       "      <td>1001.0</td>\n",
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       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
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       "      <td>0.1860</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0  ...          17.33           184.60      2019.0            0.1622   \n",
       "1  ...          23.41           158.80      1956.0            0.1238   \n",
       "2  ...          25.53           152.50      1709.0            0.1444   \n",
       "3  ...          26.50            98.87       567.7            0.2098   \n",
       "4  ...          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
       "       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
       "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
       "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
       "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
       "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
       "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
       "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
       "       'symmetry_worst', 'fractal_dimension_worst', 'Unnamed: 32'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns=df.columns\n",
    "columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
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       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
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       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>926682</td>\n",
       "      <td>M</td>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>...</td>\n",
       "      <td>38.25</td>\n",
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       "      <td>0.2572</td>\n",
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       "      <th>566</th>\n",
       "      <td>926954</td>\n",
       "      <td>M</td>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>...</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>567</th>\n",
       "      <td>927241</td>\n",
       "      <td>M</td>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>...</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>92751</td>\n",
       "      <td>B</td>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
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      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "564  926424         M        21.56         22.39          142.00     1479.0   \n",
       "565  926682         M        20.13         28.25          131.20     1261.0   \n",
       "566  926954         M        16.60         28.08          108.30      858.1   \n",
       "567  927241         M        20.60         29.33          140.10     1265.0   \n",
       "568   92751         B         7.76         24.54           47.92      181.0   \n",
       "\n",
       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "564          0.11100           0.11590         0.24390              0.13890   \n",
       "565          0.09780           0.10340         0.14400              0.09791   \n",
       "566          0.08455           0.10230         0.09251              0.05302   \n",
       "567          0.11780           0.27700         0.35140              0.15200   \n",
       "568          0.05263           0.04362         0.00000              0.00000   \n",
       "\n",
       "     ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "564  ...          26.40           166.10      2027.0           0.14100   \n",
       "565  ...          38.25           155.00      1731.0           0.11660   \n",
       "566  ...          34.12           126.70      1124.0           0.11390   \n",
       "567  ...          39.42           184.60      1821.0           0.16500   \n",
       "568  ...          30.37            59.16       268.6           0.08996   \n",
       "\n",
       "     compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "564            0.21130           0.4107                0.2216          0.2060   \n",
       "565            0.19220           0.3215                0.1628          0.2572   \n",
       "566            0.30940           0.3403                0.1418          0.2218   \n",
       "567            0.86810           0.9387                0.2650          0.4087   \n",
       "568            0.06444           0.0000                0.0000          0.2871   \n",
       "\n",
       "     fractal_dimension_worst  Unnamed: 32  \n",
       "564                  0.07115          NaN  \n",
       "565                  0.06637          NaN  \n",
       "566                  0.07820          NaN  \n",
       "567                  0.12400          NaN  \n",
       "568                  0.07039          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()\n",
    "#malignant M : kötü huylu tümör\n",
    "#benign B : iyi huylu tümör\n",
    "#diognosis feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"id\",\"Unnamed: 32\"],axis=1,inplace=True)  #unwanted features have dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0         M        17.99         10.38          122.80     1001.0   \n",
       "1         M        20.57         17.77          132.90     1326.0   \n",
       "2         M        19.69         21.25          130.00     1203.0   \n",
       "3         M        11.42         20.38           77.58      386.1   \n",
       "4         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "0         0.2419  ...         25.38          17.33           184.60   \n",
       "1         0.1812  ...         24.99          23.41           158.80   \n",
       "2         0.2069  ...         23.57          25.53           152.50   \n",
       "3         0.2597  ...         14.91          26.50            98.87   \n",
       "4         0.1809  ...         22.54          16.67           152.20   \n",
       "\n",
       "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "M=df[df.diagnosis==\"M\"]  #insert the M values to M variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "B=df[df.diagnosis==\"B\"]  #insert the B values to B variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 357 entries, 19 to 568\n",
      "Data columns (total 31 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   diagnosis                357 non-null    object \n",
      " 1   radius_mean              357 non-null    float64\n",
      " 2   texture_mean             357 non-null    float64\n",
      " 3   perimeter_mean           357 non-null    float64\n",
      " 4   area_mean                357 non-null    float64\n",
      " 5   smoothness_mean          357 non-null    float64\n",
      " 6   compactness_mean         357 non-null    float64\n",
      " 7   concavity_mean           357 non-null    float64\n",
      " 8   concave points_mean      357 non-null    float64\n",
      " 9   symmetry_mean            357 non-null    float64\n",
      " 10  fractal_dimension_mean   357 non-null    float64\n",
      " 11  radius_se                357 non-null    float64\n",
      " 12  texture_se               357 non-null    float64\n",
      " 13  perimeter_se             357 non-null    float64\n",
      " 14  area_se                  357 non-null    float64\n",
      " 15  smoothness_se            357 non-null    float64\n",
      " 16  compactness_se           357 non-null    float64\n",
      " 17  concavity_se             357 non-null    float64\n",
      " 18  concave points_se        357 non-null    float64\n",
      " 19  symmetry_se              357 non-null    float64\n",
      " 20  fractal_dimension_se     357 non-null    float64\n",
      " 21  radius_worst             357 non-null    float64\n",
      " 22  texture_worst            357 non-null    float64\n",
      " 23  perimeter_worst          357 non-null    float64\n",
      " 24  area_worst               357 non-null    float64\n",
      " 25  smoothness_worst         357 non-null    float64\n",
      " 26  compactness_worst        357 non-null    float64\n",
      " 27  concavity_worst          357 non-null    float64\n",
      " 28  concave points_worst     357 non-null    float64\n",
      " 29  symmetry_worst           357 non-null    float64\n",
      " 30  fractal_dimension_worst  357 non-null    float64\n",
      "dtypes: float64(30), object(1)\n",
      "memory usage: 89.2+ KB\n"
     ]
    }
   ],
   "source": [
    "B.info()   #357 iyi huylu tümör"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 212 entries, 0 to 567\n",
      "Data columns (total 31 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   diagnosis                212 non-null    object \n",
      " 1   radius_mean              212 non-null    float64\n",
      " 2   texture_mean             212 non-null    float64\n",
      " 3   perimeter_mean           212 non-null    float64\n",
      " 4   area_mean                212 non-null    float64\n",
      " 5   smoothness_mean          212 non-null    float64\n",
      " 6   compactness_mean         212 non-null    float64\n",
      " 7   concavity_mean           212 non-null    float64\n",
      " 8   concave points_mean      212 non-null    float64\n",
      " 9   symmetry_mean            212 non-null    float64\n",
      " 10  fractal_dimension_mean   212 non-null    float64\n",
      " 11  radius_se                212 non-null    float64\n",
      " 12  texture_se               212 non-null    float64\n",
      " 13  perimeter_se             212 non-null    float64\n",
      " 14  area_se                  212 non-null    float64\n",
      " 15  smoothness_se            212 non-null    float64\n",
      " 16  compactness_se           212 non-null    float64\n",
      " 17  concavity_se             212 non-null    float64\n",
      " 18  concave points_se        212 non-null    float64\n",
      " 19  symmetry_se              212 non-null    float64\n",
      " 20  fractal_dimension_se     212 non-null    float64\n",
      " 21  radius_worst             212 non-null    float64\n",
      " 22  texture_worst            212 non-null    float64\n",
      " 23  perimeter_worst          212 non-null    float64\n",
      " 24  area_worst               212 non-null    float64\n",
      " 25  smoothness_worst         212 non-null    float64\n",
      " 26  compactness_worst        212 non-null    float64\n",
      " 27  concavity_worst          212 non-null    float64\n",
      " 28  concave points_worst     212 non-null    float64\n",
      " 29  symmetry_worst           212 non-null    float64\n",
      " 30  fractal_dimension_worst  212 non-null    float64\n",
      "dtypes: float64(30), object(1)\n",
      "memory usage: 53.0+ KB\n"
     ]
    }
   ],
   "source": [
    "M.info()   #212 tane kötü huylu tümör"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2a58f7ffcd0>"
      ]
     },
     "execution_count": 41,
     "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": [
    "#scatterplot\n",
    "plt.scatter(M.radius_mean,M.area_mean,color='red',label=\"kötü\",alpha=0.3)   #alpha saydamlık veriyor\n",
    "plt.scatter(B.radius_mean,B.area_mean,color='green',label=\"iyi\",alpha=0.3)\n",
    "plt.xlabel(\"radius mean\")\n",
    "plt.ylabel(\"area mean\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "it is clear that the bigger radius and area mean the higher probability of having tumor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2a58f65a5e0>"
      ]
     },
     "execution_count": 39,
     "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": [
    "#scatterplot\n",
    "plt.scatter(M.radius_mean,M.texture_mean,color='red',label=\"kötü\")\n",
    "plt.scatter(B.radius_mean,B.texture_mean,color='green',label=\"iyi\")\n",
    "plt.xlabel(\"radius mean\")\n",
    "plt.ylabel(\"texture mean\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bigger radius and texture mean the higher probability of having melignant tumor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.diagnosis= [1 if each==\"M\" else 0 for each in df.diagnosis]   #1 kötü huylu, 0 iyi huylu, converted numerical values\n",
    "y=df.diagnosis.values\n",
    "x_data=df.drop('diagnosis',axis=1)   #dropped diagnosis (x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 30 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   radius_mean              569 non-null    float64\n",
      " 1   texture_mean             569 non-null    float64\n",
      " 2   perimeter_mean           569 non-null    float64\n",
      " 3   area_mean                569 non-null    float64\n",
      " 4   smoothness_mean          569 non-null    float64\n",
      " 5   compactness_mean         569 non-null    float64\n",
      " 6   concavity_mean           569 non-null    float64\n",
      " 7   concave points_mean      569 non-null    float64\n",
      " 8   symmetry_mean            569 non-null    float64\n",
      " 9   fractal_dimension_mean   569 non-null    float64\n",
      " 10  radius_se                569 non-null    float64\n",
      " 11  texture_se               569 non-null    float64\n",
      " 12  perimeter_se             569 non-null    float64\n",
      " 13  area_se                  569 non-null    float64\n",
      " 14  smoothness_se            569 non-null    float64\n",
      " 15  compactness_se           569 non-null    float64\n",
      " 16  concavity_se             569 non-null    float64\n",
      " 17  concave points_se        569 non-null    float64\n",
      " 18  symmetry_se              569 non-null    float64\n",
      " 19  fractal_dimension_se     569 non-null    float64\n",
      " 20  radius_worst             569 non-null    float64\n",
      " 21  texture_worst            569 non-null    float64\n",
      " 22  perimeter_worst          569 non-null    float64\n",
      " 23  area_worst               569 non-null    float64\n",
      " 24  smoothness_worst         569 non-null    float64\n",
      " 25  compactness_worst        569 non-null    float64\n",
      " 26  concavity_worst          569 non-null    float64\n",
      " 27  concave points_worst     569 non-null    float64\n",
      " 28  symmetry_worst           569 non-null    float64\n",
      " 29  fractal_dimension_worst  569 non-null    float64\n",
      "dtypes: float64(30)\n",
      "memory usage: 133.5 KB\n"
     ]
    }
   ],
   "source": [
    "x_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1,\n",
       "       0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1,\n",
       "       1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0,\n",
       "       0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1,\n",
       "       1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "x= (x_data-np.min(x_data))/(np.max(x_data)-np.min(x_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.521037</td>\n",
       "      <td>0.022658</td>\n",
       "      <td>0.545989</td>\n",
       "      <td>0.363733</td>\n",
       "      <td>0.593753</td>\n",
       "      <td>0.792037</td>\n",
       "      <td>0.703140</td>\n",
       "      <td>0.731113</td>\n",
       "      <td>0.686364</td>\n",
       "      <td>0.605518</td>\n",
       "      <td>...</td>\n",
       "      <td>0.620776</td>\n",
       "      <td>0.141525</td>\n",
       "      <td>0.668310</td>\n",
       "      <td>0.450698</td>\n",
       "      <td>0.601136</td>\n",
       "      <td>0.619292</td>\n",
       "      <td>0.568610</td>\n",
       "      <td>0.912027</td>\n",
       "      <td>0.598462</td>\n",
       "      <td>0.418864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.643144</td>\n",
       "      <td>0.272574</td>\n",
       "      <td>0.615783</td>\n",
       "      <td>0.501591</td>\n",
       "      <td>0.289880</td>\n",
       "      <td>0.181768</td>\n",
       "      <td>0.203608</td>\n",
       "      <td>0.348757</td>\n",
       "      <td>0.379798</td>\n",
       "      <td>0.141323</td>\n",
       "      <td>...</td>\n",
       "      <td>0.606901</td>\n",
       "      <td>0.303571</td>\n",
       "      <td>0.539818</td>\n",
       "      <td>0.435214</td>\n",
       "      <td>0.347553</td>\n",
       "      <td>0.154563</td>\n",
       "      <td>0.192971</td>\n",
       "      <td>0.639175</td>\n",
       "      <td>0.233590</td>\n",
       "      <td>0.222878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.601496</td>\n",
       "      <td>0.390260</td>\n",
       "      <td>0.595743</td>\n",
       "      <td>0.449417</td>\n",
       "      <td>0.514309</td>\n",
       "      <td>0.431017</td>\n",
       "      <td>0.462512</td>\n",
       "      <td>0.635686</td>\n",
       "      <td>0.509596</td>\n",
       "      <td>0.211247</td>\n",
       "      <td>...</td>\n",
       "      <td>0.556386</td>\n",
       "      <td>0.360075</td>\n",
       "      <td>0.508442</td>\n",
       "      <td>0.374508</td>\n",
       "      <td>0.483590</td>\n",
       "      <td>0.385375</td>\n",
       "      <td>0.359744</td>\n",
       "      <td>0.835052</td>\n",
       "      <td>0.403706</td>\n",
       "      <td>0.213433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.210090</td>\n",
       "      <td>0.360839</td>\n",
       "      <td>0.233501</td>\n",
       "      <td>0.102906</td>\n",
       "      <td>0.811321</td>\n",
       "      <td>0.811361</td>\n",
       "      <td>0.565604</td>\n",
       "      <td>0.522863</td>\n",
       "      <td>0.776263</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.248310</td>\n",
       "      <td>0.385928</td>\n",
       "      <td>0.241347</td>\n",
       "      <td>0.094008</td>\n",
       "      <td>0.915472</td>\n",
       "      <td>0.814012</td>\n",
       "      <td>0.548642</td>\n",
       "      <td>0.884880</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.773711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.629893</td>\n",
       "      <td>0.156578</td>\n",
       "      <td>0.630986</td>\n",
       "      <td>0.489290</td>\n",
       "      <td>0.430351</td>\n",
       "      <td>0.347893</td>\n",
       "      <td>0.463918</td>\n",
       "      <td>0.518390</td>\n",
       "      <td>0.378283</td>\n",
       "      <td>0.186816</td>\n",
       "      <td>...</td>\n",
       "      <td>0.519744</td>\n",
       "      <td>0.123934</td>\n",
       "      <td>0.506948</td>\n",
       "      <td>0.341575</td>\n",
       "      <td>0.437364</td>\n",
       "      <td>0.172415</td>\n",
       "      <td>0.319489</td>\n",
       "      <td>0.558419</td>\n",
       "      <td>0.157500</td>\n",
       "      <td>0.142595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
       "0     0.521037      0.022658        0.545989   0.363733         0.593753   \n",
       "1     0.643144      0.272574        0.615783   0.501591         0.289880   \n",
       "2     0.601496      0.390260        0.595743   0.449417         0.514309   \n",
       "3     0.210090      0.360839        0.233501   0.102906         0.811321   \n",
       "4     0.629893      0.156578        0.630986   0.489290         0.430351   \n",
       "\n",
       "   compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n",
       "0          0.792037        0.703140             0.731113       0.686364   \n",
       "1          0.181768        0.203608             0.348757       0.379798   \n",
       "2          0.431017        0.462512             0.635686       0.509596   \n",
       "3          0.811361        0.565604             0.522863       0.776263   \n",
       "4          0.347893        0.463918             0.518390       0.378283   \n",
       "\n",
       "   fractal_dimension_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "0                0.605518  ...      0.620776       0.141525         0.668310   \n",
       "1                0.141323  ...      0.606901       0.303571         0.539818   \n",
       "2                0.211247  ...      0.556386       0.360075         0.508442   \n",
       "3                1.000000  ...      0.248310       0.385928         0.241347   \n",
       "4                0.186816  ...      0.519744       0.123934         0.506948   \n",
       "\n",
       "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0    0.450698          0.601136           0.619292         0.568610   \n",
       "1    0.435214          0.347553           0.154563         0.192971   \n",
       "2    0.374508          0.483590           0.385375         0.359744   \n",
       "3    0.094008          0.915472           0.814012         0.548642   \n",
       "4    0.341575          0.437364           0.172415         0.319489   \n",
       "\n",
       "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0              0.912027        0.598462                 0.418864  \n",
       "1              0.639175        0.233590                 0.222878  \n",
       "2              0.835052        0.403706                 0.213433  \n",
       "3              0.884880        1.000000                 0.773711  \n",
       "4              0.558419        0.157500                 0.142595  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train Test split:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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=0.3,random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "KNN Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "n_neighbor=3\n",
    "knn = KNeighborsClassifier(n_neighbors=n_neighbor)               #n is number of neighbor, k value, hyperparameter\n",
    "knn.fit(x_train,y_train)  #train the model\n",
    "prediction=knn.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0,\n",
       "       1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0,\n",
       "       0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 nn score: 0.9532163742690059\n"
     ]
    }
   ],
   "source": [
    "print(\"{} nn score: {}\".format(n_neighbor,knn.score(x_test,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find k value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_list=[]\n",
    "for each in range(1,15):\n",
    "    n_neighbor=each\n",
    "    knn = KNeighborsClassifier(n_neighbors=n_neighbor)\n",
    "    knn.fit(x_train,y_train)\n",
    "    score=knn.score(x_test,y_test)\n",
    "    score_list.append(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'accuracy')"
      ]
     },
     "execution_count": 153,
     "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": [
    "plt.plot(range(1,15),score_list)\n",
    "plt.xlabel(\"k values\")\n",
    "plt.ylabel(\"accuracy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, 6,7,8,9,10 and 12 k values seems better"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "*Eğer k değerini 8 seçersem accuracy en yüksek oluyor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Homework"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe=pd.read_csv(r\"column_2C_weka.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 310 entries, 0 to 309\n",
      "Data columns (total 7 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   pelvic_incidence          310 non-null    float64\n",
      " 1   pelvic_tilt numeric       310 non-null    float64\n",
      " 2   lumbar_lordosis_angle     310 non-null    float64\n",
      " 3   sacral_slope              310 non-null    float64\n",
      " 4   pelvic_radius             310 non-null    float64\n",
      " 5   degree_spondylolisthesis  310 non-null    float64\n",
      " 6   class                     310 non-null    object \n",
      "dtypes: float64(6), object(1)\n",
      "memory usage: 17.1+ KB\n"
     ]
    }
   ],
   "source": [
    "dataframe.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "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>pelvic_incidence</th>\n",
       "      <th>pelvic_tilt numeric</th>\n",
       "      <th>lumbar_lordosis_angle</th>\n",
       "      <th>sacral_slope</th>\n",
       "      <th>pelvic_radius</th>\n",
       "      <th>degree_spondylolisthesis</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.027818</td>\n",
       "      <td>22.552586</td>\n",
       "      <td>39.609117</td>\n",
       "      <td>40.475232</td>\n",
       "      <td>98.672917</td>\n",
       "      <td>-0.254400</td>\n",
       "      <td>Abnormal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.056951</td>\n",
       "      <td>10.060991</td>\n",
       "      <td>25.015378</td>\n",
       "      <td>28.995960</td>\n",
       "      <td>114.405425</td>\n",
       "      <td>4.564259</td>\n",
       "      <td>Abnormal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.832021</td>\n",
       "      <td>22.218482</td>\n",
       "      <td>50.092194</td>\n",
       "      <td>46.613539</td>\n",
       "      <td>105.985135</td>\n",
       "      <td>-3.530317</td>\n",
       "      <td>Abnormal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69.297008</td>\n",
       "      <td>24.652878</td>\n",
       "      <td>44.311238</td>\n",
       "      <td>44.644130</td>\n",
       "      <td>101.868495</td>\n",
       "      <td>11.211523</td>\n",
       "      <td>Abnormal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49.712859</td>\n",
       "      <td>9.652075</td>\n",
       "      <td>28.317406</td>\n",
       "      <td>40.060784</td>\n",
       "      <td>108.168725</td>\n",
       "      <td>7.918501</td>\n",
       "      <td>Abnormal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>47.903565</td>\n",
       "      <td>13.616688</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>34.286877</td>\n",
       "      <td>117.449062</td>\n",
       "      <td>-4.245395</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>53.936748</td>\n",
       "      <td>20.721496</td>\n",
       "      <td>29.220534</td>\n",
       "      <td>33.215251</td>\n",
       "      <td>114.365845</td>\n",
       "      <td>-0.421010</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>61.446597</td>\n",
       "      <td>22.694968</td>\n",
       "      <td>46.170347</td>\n",
       "      <td>38.751628</td>\n",
       "      <td>125.670725</td>\n",
       "      <td>-2.707880</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>45.252792</td>\n",
       "      <td>8.693157</td>\n",
       "      <td>41.583126</td>\n",
       "      <td>36.559635</td>\n",
       "      <td>118.545842</td>\n",
       "      <td>0.214750</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>33.841641</td>\n",
       "      <td>5.073991</td>\n",
       "      <td>36.641233</td>\n",
       "      <td>28.767649</td>\n",
       "      <td>123.945244</td>\n",
       "      <td>-0.199249</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>310 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pelvic_incidence  pelvic_tilt numeric  lumbar_lordosis_angle  \\\n",
       "0           63.027818            22.552586              39.609117   \n",
       "1           39.056951            10.060991              25.015378   \n",
       "2           68.832021            22.218482              50.092194   \n",
       "3           69.297008            24.652878              44.311238   \n",
       "4           49.712859             9.652075              28.317406   \n",
       "..                ...                  ...                    ...   \n",
       "305         47.903565            13.616688              36.000000   \n",
       "306         53.936748            20.721496              29.220534   \n",
       "307         61.446597            22.694968              46.170347   \n",
       "308         45.252792             8.693157              41.583126   \n",
       "309         33.841641             5.073991              36.641233   \n",
       "\n",
       "     sacral_slope  pelvic_radius  degree_spondylolisthesis     class  \n",
       "0       40.475232      98.672917                 -0.254400  Abnormal  \n",
       "1       28.995960     114.405425                  4.564259  Abnormal  \n",
       "2       46.613539     105.985135                 -3.530317  Abnormal  \n",
       "3       44.644130     101.868495                 11.211523  Abnormal  \n",
       "4       40.060784     108.168725                  7.918501  Abnormal  \n",
       "..            ...            ...                       ...       ...  \n",
       "305     34.286877     117.449062                 -4.245395    Normal  \n",
       "306     33.215251     114.365845                 -0.421010    Normal  \n",
       "307     38.751628     125.670725                 -2.707880    Normal  \n",
       "308     36.559635     118.545842                  0.214750    Normal  \n",
       "309     28.767649     123.945244                 -0.199249    Normal  \n",
       "\n",
       "[310 rows x 7 columns]"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe.rename({'class':'status'},axis=1,inplace=True)  #class feature was an obstacle to write code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pelvic_incidence', 'pelvic_tilt numeric', 'lumbar_lordosis_angle',\n",
       "       'sacral_slope', 'pelvic_radius', 'degree_spondylolisthesis', 'status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.columns           #to see if the feature name has changed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe.status=[ 1 if each=='Abnormal' else 0  for each in dataframe.status]   #convert objects to numerical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: status, dtype: int64"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.status.head()  #it seems the code works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "An=dataframe[dataframe.status==1]\n",
    "N=dataframe[dataframe.status==0]          "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variables are to use them in the graphics below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "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>pelvic_incidence</th>\n",
       "      <th>pelvic_tilt numeric</th>\n",
       "      <th>lumbar_lordosis_angle</th>\n",
       "      <th>sacral_slope</th>\n",
       "      <th>pelvic_radius</th>\n",
       "      <th>degree_spondylolisthesis</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.027818</td>\n",
       "      <td>22.552586</td>\n",
       "      <td>39.609117</td>\n",
       "      <td>40.475232</td>\n",
       "      <td>98.672917</td>\n",
       "      <td>-0.254400</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.056951</td>\n",
       "      <td>10.060991</td>\n",
       "      <td>25.015378</td>\n",
       "      <td>28.995960</td>\n",
       "      <td>114.405425</td>\n",
       "      <td>4.564259</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.832021</td>\n",
       "      <td>22.218482</td>\n",
       "      <td>50.092194</td>\n",
       "      <td>46.613539</td>\n",
       "      <td>105.985135</td>\n",
       "      <td>-3.530317</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69.297008</td>\n",
       "      <td>24.652878</td>\n",
       "      <td>44.311238</td>\n",
       "      <td>44.644130</td>\n",
       "      <td>101.868495</td>\n",
       "      <td>11.211523</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49.712859</td>\n",
       "      <td>9.652075</td>\n",
       "      <td>28.317406</td>\n",
       "      <td>40.060784</td>\n",
       "      <td>108.168725</td>\n",
       "      <td>7.918501</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pelvic_incidence  pelvic_tilt numeric  lumbar_lordosis_angle  sacral_slope  \\\n",
       "0         63.027818            22.552586              39.609117     40.475232   \n",
       "1         39.056951            10.060991              25.015378     28.995960   \n",
       "2         68.832021            22.218482              50.092194     46.613539   \n",
       "3         69.297008            24.652878              44.311238     44.644130   \n",
       "4         49.712859             9.652075              28.317406     40.060784   \n",
       "\n",
       "   pelvic_radius  degree_spondylolisthesis  status  \n",
       "0      98.672917                 -0.254400       1  \n",
       "1     114.405425                  4.564259       1  \n",
       "2     105.985135                 -3.530317       1  \n",
       "3     101.868495                 11.211523       1  \n",
       "4     108.168725                  7.918501       1  "
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "An.head()   #normal results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "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>pelvic_incidence</th>\n",
       "      <th>pelvic_tilt numeric</th>\n",
       "      <th>lumbar_lordosis_angle</th>\n",
       "      <th>sacral_slope</th>\n",
       "      <th>pelvic_radius</th>\n",
       "      <th>degree_spondylolisthesis</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>38.505273</td>\n",
       "      <td>16.964297</td>\n",
       "      <td>35.112814</td>\n",
       "      <td>21.540976</td>\n",
       "      <td>127.632875</td>\n",
       "      <td>7.986683</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>54.920858</td>\n",
       "      <td>18.968430</td>\n",
       "      <td>51.601455</td>\n",
       "      <td>35.952428</td>\n",
       "      <td>125.846646</td>\n",
       "      <td>2.001642</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>44.362490</td>\n",
       "      <td>8.945435</td>\n",
       "      <td>46.902096</td>\n",
       "      <td>35.417055</td>\n",
       "      <td>129.220682</td>\n",
       "      <td>4.994195</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>48.318931</td>\n",
       "      <td>17.452121</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>30.866809</td>\n",
       "      <td>128.980308</td>\n",
       "      <td>-0.910941</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>45.701789</td>\n",
       "      <td>10.659859</td>\n",
       "      <td>42.577846</td>\n",
       "      <td>35.041929</td>\n",
       "      <td>130.178314</td>\n",
       "      <td>-3.388910</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pelvic_incidence  pelvic_tilt numeric  lumbar_lordosis_angle  \\\n",
       "210         38.505273            16.964297              35.112814   \n",
       "211         54.920858            18.968430              51.601455   \n",
       "212         44.362490             8.945435              46.902096   \n",
       "213         48.318931            17.452121              48.000000   \n",
       "214         45.701789            10.659859              42.577846   \n",
       "\n",
       "     sacral_slope  pelvic_radius  degree_spondylolisthesis  status  \n",
       "210     21.540976     127.632875                  7.986683       0  \n",
       "211     35.952428     125.846646                  2.001642       0  \n",
       "212     35.417055     129.220682                  4.994195       0  \n",
       "213     30.866809     128.980308                 -0.910941       0  \n",
       "214     35.041929     130.178314                 -3.388910       0  "
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N.head()  #anormal results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2a5945876a0>"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.pairplot(dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(dataframe.corr(),annot=True,linewidth=3)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sacral slope-pelvic incidence has 0.81 correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lumbar lordosis angle-pelvic incidence has 0.72 correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the graphic :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2a59a0a18e0>"
      ]
     },
     "execution_count": 224,
     "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": [
    "#scatterplot\n",
    "plt.scatter(An.pelvic_incidence,An.sacral_slope,color='red')   #anormal \n",
    "plt.scatter(N.pelvic_incidence,N.sacral_slope,color='blue')\n",
    "plt.xlabel(\"Pelvic Incidence\")\n",
    "plt.ylabel(\"Sacral Slope\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although there is a relation between pelvic incidence and sacral slope, we can't say it is connected to be normal or anormal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2a59a0ddca0>"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dKVwA7Af+Gvg0sBd4UVG9QRSJgqhCo3a5z/Pgxv/zncQUigzFFBmXiWNKxLJbUbgZ+IHE++8Hbi6qN4giURCVMPMGF3udvW6seZ29kSBAsZHvVUoKIUaAPFEolTrb3c8rOjYMlDpbtJG3P0Io/fWkppMuu1eEmDq6TZ29amZXm9kFcXkv0ehBiNGiaPV13qrlUafqCuRerEQXU0mZkcLJwGuBFxCtQP408G53f7T/zctHIwWxgTIjgXHsPWdttDM/n58Ke9pGRaISXe28NspIFMQGQltm9nqLzKrkCVEZkerEwI/qdyFGgjxROCmn0m1AUDFGIaYgxAYWFrKN5zAT76V7+U03TpPQZ0lhKJOQL03ou5iZido06qMjMTTyYgovBX4G+Iu4LMXl48CH+t80ISoyzJhByOe/c2d21tOdO/M/SxIStWZG1qwYQ2j70LU1xRZEPqFpSc0C/E2ZY8MompI6hRRN9RzGVNC8dQl5K6LLplWomIV1Q9I/9p2Ydjvhq3RFeehBltQXJN4/D7ilqN4gikRhyuhmUVhaLJaXw+JRVVg63aCnSlqFkiumc5P+TXg+H1GebkXhfODLwD6i1cy3AOcX1RtEmTpRmOYFUY1G2BgWpY+u0tPuRHg63cqzU5HLuV9h0r+ikcI0/8amiI5FAZgF3hi/fjzwXXnnD7pMlShMc+qEMkY973uoksU0dG6tFjaWZTbh6dWopOB+hUn/8n430/wbmzK6HSn8VdE5wypTJQpTlMGxjTJGPe97KLPfQbNnX/bcpLGsYkx70RPPuV/wZzJ7sPie0/wbmzK6FYVdwP8EXhi7ks6X+2gITFGu9zbiZw/mLUp/D2nDW6uVM/R5I4UiY1nG2PeyJx64X1e3mObf2JTRrSh8KqN8sqjeIMpUicI09+LqdW9wcTjDadpVk7aKc3P5+xUkLWeVDXCqGstO/g07GFl0PBiZ5t/YlNGVKIxymSpRmGZ/b6PhdfZl2yv2ltvqMh0TqDL7KDTSqGosq/bEB/1vPs2/sSmj25HCdwHvAFbj8t9HJeA8VaLgPp4zQ8q2OXFeo3a512tHN1Qx1rLtKWsbr9kPF0iWsdy0KXsjnLznrdoTH0bPfRx/Y6Iy3YrCnwBvAb4nLm8GPlxUbxBl6kRh3Cjb80ycl+kmmnevcTg8UkjSL0Oa3hltbq79uZaX85+3ak9cPn7RJ7pevFbm2DCKRGHEKWugE+fV2Zvt/eFwm1gYa768+ZqN16rSq2+eX3WFdOi5QkY8Xa+53qKoJy4fv+gT3YrC36ZWND8f+NuieoMoEoXRY4MNTc8QCvV0E8Y0z020zLvaPp+bfazd3pfp1TcaUc8+bciLevZFAetQ6cRXLx+/6BPdisIzEiua9wFfAs4rqjeIIlEYLRoN9/lNxzfasKwUCx2MFOrsDX6WazPzAs95Pfu8ulVLTlqK3C+z6shCiJL0ZPZRvKL58fHrN5St188iURgt6rWjQYOe29MtiinEwhIaReTa2rKL0bJGMp3UrVpCLiuNEEQf6fmUVOBAJ/V6XSQKo0XuDKFOZx/NHmyNNMqMFJI23d076+3HieXqswezF8pllc2bs483p8IW3XN5uVybm9+hZgmJLuiHKBzspF6i/hOI9mT4KnAn8CPAGcCNwN3x39OLriNRGC3yXD8dUzCKCI4Uku6XdI8/bwRg5o3lPeUyjaaNf5XEd1mladiLRihZi/E0khAVGLmRAnAt8G/j15tikXgbcEV87Argd4quI1EYLRq1y7NdP7XLCyqWmAEU+9WTqS5qHPZNPNpuG5f3tBvhpqEt6rVv3+712YPZYjNzIFdM2gLcyQh4c7FcoZp557EMzUoSJelIFICjwAMZ5SjwWKheUYljE3uJ94dOHL8L2BK/3gLcVXQticKI0Wh4Y+6VG/MTzb0y32WUtVq4IO6QPK+xvKddT4qmcoY+37TJfX4+7AYzL7e6OS8mkGfUm36vKqk2sur3ErmpJpKRSnMB/BDwBeCaeCbTHwKbgW+lzvtm0bUkCh3Sz//oVVYw5xm+rF5vXk+8yqrmRqN9mmqiBNdK1KJgelucIS1ieaKUl5wvK8HeMEcKCnhPLKMmCtuAx4DnxO+vAt5aVhSAHc2UGwsLC/35xsaAju36qPxHLzB4DS4JP1/RM8QJ9NoyqiaT5s3M5Ny7PXaR6cZvusbS313RpjsnnZR93+1Xtz9z2QR//fg31OK5iWXUROHJwL7E+xcCfyb3UXm6suuj8h89J5ja4GKft4fCz5e39sCjmEJmbGN5T2nXzAZRmTngtZn7s7+22tHyK56TovS4xxUKUeuZs3oAg3DrKM3GxDJSohC1hz3AD8SvfxN4e1ySgea3FV1nWkWhK7te8j96T2xOJ8nhyFnA1ny+vNk5jZyNZur59219D8n3mza5z83lT7dNW/KiHEgZz1/4zMNgVDoQoud0LApE23H+Zd45nZQ4rrAK3Ar8KXA6UANuiqek3gScUXSdaRWFrjpwIZ92Yp/jxvKe/J56GYqGMzlB19xAr3u+YZ+ddWM9XD9PUJoGPSNtdu502yyjWaSqqXYUPvMwGBVXo+g5XY0UgBsYkVTZ6TKtotBVB65IFBoNr9v+atevkjQueZFAW4JTQuuJ+4UMOwTbPzvr4cVos7PZxi6x61vuJj9VLfk4jBTcNftoQulWFK4HDgBXA+9slqJ6gyjTKgpddeASPdS2YGw8nbNSrzXUmJDRTlyksbzH67Z/o6GOp5kWPl/OLJ6smETboCBp0PO+vITx3vB9zR6MgsydWvLU95YbUxCix3QrCpdllaJ6gyjTKgruXXTgYiMXNEJcUq3XGhoRFCSBy9QSeygKBoeeLz0lNSdjaXL2UrAp7C3+8vIUuFv3SuohM9dcCNEHRi7Q3KsyzaLQKc3eOQG/ezPXUJtg2EPZRqrIRx8wmJVcYKFFbnNz4amliQt1PYkmT4HlXhFjSLcjhXOJ8hR9BfhasxTVG0SRKFSjzGxMYz1y4SRdJba/1YNvI8+650ylLO2iKmp0Xs6hEk3s5DuUBohxp1tR+AywPZ4pVI+nkL6lqN4gikShGmUWyNbZm79aOE2V1BYJA5+7arieuHXIb59UkQJLnenlCS08y0GTccSk0K0o3Bz/vS1xbE9RvUEUiUI1ipJvlg6+Jmg0MjbW2XQ8GlnkzEiqtGo4Lztpye5+o1EyRUUOXY84NMwQI0K3ovA3wAzwYeBXgZ8rs9p4EEWi4BuNbcEOXeGRwrrDum/m2xsNcAlrFzSUtj9l3dtdQBtcVPWc2bIcbk9ZkWfQQ8a3rFUP1O8qNqFhhhghuhWFZwOPA7YCfxSLw3OL6g2iTL0o5PnbMwxOmZjCJh45YXRLWLvwtdbaDxbMSAqPZDYGxed50BubXxUUhKA7q0yivJysrV2NFLQ6WIwQPZl9RJTy+rSy5w+iTIIodOVRKAoSZBicMsk3W6t0SxiskJ2f5Xj+TZIl3nWsSlLQaCHa+omd2Zr7LdhSeE+HoqB4QdbWXq0PyRQkMfpMkPuv25HCNuA2YF9cvgw8q6jeIMq4i0KZTBC5v8GiIEGOwcndfIy10tau0kihQLxK5qpr78gn4g656Sjy9m4okxOpzL9JCI0UxpsJc/91Kwq3Ai9MvH8BcGtRvUGUcReFqh3Xtt9gByOFMlXrswe7D76mYwolxSttdPO2H2gz+uTkEMoSqVrtxHMWCWzZVcp56xkmyKhMHRMm6l0HmsscG0YZd1HI8yiU+g1WjCl4qmrWPjObNlWzU0Fbt7yngkWvV7p+ntGvnLiuSZ5KljHeZYz+BLkfpo4Jc/91JArA+XH5PeA9wAXAjwLvBnaF6g2yjLso5Bn+0r/BCrOP0qTjqsmOcxWCKSnKWPMSBrfRCMcu0ka/UuK65HcVam/ZL2XCepIixSj9+/agc9GpKHwqp3wyVG+QZdxFIa9z2avfYOj308tO64Zr1Y6WW3BWQbzcPZgOO8votyX6s6VywtTNlzKonmQ6/1PZRYaiO0bF/dejdij30QiTZ7S7/bcPXaPMHjBd3aNowVnVGzXC6bxnOR7dK2d7zdYD5g416tUfPskgepJFo6+s/SAkFL1jFNx/PfqddRtTeALwOuAdSp09WEr9BuOTGlzi9dmD0TTNev5oo2C5QPDGWYdDYYNMH37THVPwgG2Ha5fnJ+krMZ20cIZRtz36QfQky8zZTY9YFMyeLHo0Iu1WFD4bC8IvKXX2iBEbokyDmUo9UaaYedC4Ze1xkJO5Onu2D0TR7ZAPf37el7ff2W7X4pFHm1uIS6LvoUqQuJ89+n73JItmSOWJopgMRmSk8MWic4ZVpl4U4h9IaMbN7Ez29MzckULgRxfaDS1oh0IjheaNMu7T4OLglNLcbS/zGpI2zKPiG+6EKqv72tReTASjEFMA3gi8CtgCnNEsRfUGUaZeFOKeY8iQwlrmXsu5MYVAbzR8j6yynh9TMMu8T0jcIGPkEXqQNpXLYBR8w51Q5CYLjSQ0UpgshjX7qHUCvBb4VryaeW9ctJ/CKFA0UuC4L/OueMvL9Q2/n+DvqgcjhZrdl39CYKSQJzz12tH2BlddWzCuYpAkb/ZRL2cQiImmW1H4B+DMovOGUaZeFHJiCi2b0JwJVLa3WDGmkF4AN28P5Y8ScrayDM0wam6Z0Gxey67HGVPbYw0XV3MbTYJYNJmkZxF9o1tRuAGYLzpvGGVcRKGv/0/jKUANLvZZsoPLdfZW8yuXmRUUr0fYMOvJ9oeNdLMxjcaJ67QS2l0SPcPmV7WJm1krX16mXd/EIz7Hw+3ClLb1zcR5bcOa4p3bhJg0uhWFjwB/H69q1pTUkiS9G13PEsxTlcSNwu6XaL+E0oJUpGKJEUra+GeNWqJ2rXmdfdkejk3Ho/TWpBae1Y6WmjiU6cLKsvVF6yfSLi4hJpRuReGyrFJUbxBlVEWhTIaH+uxBbyzvKbcOIXWxxtwr413ETvTOnfxAbRlBajS83O5k9XownUSNw7n3D8ZCi3ITeeczMgvvE2qoEBOKVjQPmHI92rV2o5plsFMXy8vtkxdbKOoEF65MTlRqcEnQVZXeEKdsyVzXkDLMnc7IDN5nfj5n9V3GlyTEhNDtSGEv8LV0Kao3iDKqolCmRxv0/9fzL1aUBXSZdxUa5qxOcHBNTLNnndhPID3NtReiUGakkBlTyAp259n62YMbh2bjvG5BiA7pVhRqiXI28AbgvxTVG0QZVVEo6tFGvflA3v+0wU5dLG9NQp5oZNraROygcB+CuFLRs9U4XDhaSd9rnodaMYUiw5wV7ggdK23r+zkTQLOBxAjSc/cR8JlO6qWuMQt8CfhY/P4M4Ebg7vjv6UXXGFVRyDJIJ4Kte1vB1GBPNh1ITlwsVM9Yy10RnBSd7dvbYwe5I5CENc0bBaXTUcBahgA8GK2dSKerKGE8q9rXodtjjULEiNLtSOH8RNkGvBr4clG9Etf9NeCPE6LwNuCK+PUVwO8UXWNURcE9wyAt79lgIHLz/udsztKoXR5MI12fPej12tHCkUKWMV/mXeG9jRNtCSbZa2YrbR6IEyNtmE00cyB79k+R/77R8Ebt8nIxmFFiEJlTheiAbkXhU4lyI7Ab+P6iegXX3ArcBPx4QhTuArbEr7cAdxVdZ5RFIZOUUjSW98Rz/DPm8+cYjrxRQJmZT6ERQcuA5/SsQ6OgZd514kBzY5pOfDrpevEc1uBIJvw1DZ8J261LTA79cB+9oZN6ifofAp5FtJtbUxS+lTrnm4G6O4BVYHVhYaE/39gg6cBwBDugcRC1Ubs8cg9VmMKZjh3ksbycsfYiNMpJU7Tmok1x8vM7jbR91UhBjCj9EIUDndSL674UeHf8urIoJMvYjRSyCBiORu3ySrazbWFWbJzLupPSsYMOmtweD+nRd+HkzLqqd367vqOYghhR+iEKBzupF9f9b8AhogR7/wQcAxpT4T7KImE4TgRo19viBkEvC+vtrqeExVzefE1GLzt17VTsoChAG4ppdN1rzxnaZMZgxsG+Dj3aLUQ7IzVSSF0nOVJ4eyrQ/Lai+qMmCh3//w8EUkv1inN61w0uyUw5sZ2/2Dj7Z/OrNjxDbue2Ed4as+tee+hZYrHIS4EhhChPR6IAHAUeyChHgcdC9aqUlCjU4uDz3fHfwj0bBikKmbOJUkHjbjwFZVfrts3PDyWeI5zuepbj7XWWlyODH6hT2/xwq5G5W2N2+yWHNpVWb1uIntHzkcKolEGJQhkffre956p5fTJX8qbaFHLzBOts2pSbVC957VLpqjv9siUAQvSVPFGw6PPxZNu2bb66utr3+ywuwv797cfr7GMf5wAwwxrOTNs5ZrC+nn/9lRW47DJYW+u+rck2Lc4eZP/a1mp12Mt+FjPPexwPcJTvCl9ofh5274alpcrtFkIMDjO72d23ZX3WbsVEGwcOBI6z0Hq9QPZJCwuZh1usrMCOHb0RhHSbdq39OvPz5euscDH3cQaQ3VF4kNN4De8KX+jYMdi5s0pzhRAjhkShiJUVFmYOZX6UFIJdvIl5O7bh8/l52LWrdRkWF2FmJvq7shId37kzsqUh5u0YNY6Ubm6zTStczM7Z3+HYMZidLa6zwsXs4L08xOMBC5xp7ObV+Rfbv3/jAwohxouQX2kcSt9jCjnbXWatCwjtj5A3oyccS9i4k1n6/pk7juWk0A6ukYtzJpVJpNdsV6kTx2K+qBDTCQo0d0hiStDGwOq+SBBmZ70VTU4YwHSsNC9lf+5CsMSBtsCuLQW3vQzNIEoLQys9hVmpoDREM5dKR8VHemVZBygILiYEiUKnlDF+qR5xldxDoVxF8/PRRjal7p0UDdtfYNzXT+zY1twbOTZuZafELm++ptyJzQecFLQ6WUwQEoVOKWspEz3iKruDNatldkCLLtQ8sV73Bpf4Jh4tvl9OKovG9qtL7dpWae7sgEcKfe3IK4+RmCAkCp1Sttuf6BGXtZnpVcJpa9ZY3uM1jniUkmLdaxzesJ4gafFC7qkN9wtsrdm6v9mGNBtB+1dW9Qbci+57R14ZT8UEIVHohqTBbsYQOhgp1GqBXmyGNWvMvdI3nfRY2zXmeHjjCuSYsG1ez17tnDZkqdhJOoC9QYfyouOzs0Pzt/e9I6+RgpggJAq9okR3NJT9OWHDN5JhbPJmAm3YMzm+b54olDJkCSMfunetljg/M3f2cP3rfe/IK6YgJgiJQi8p4bjOtJmbjkeZSNP1MqxZ3paarX0POJFeO3RujcPtB+fm2tucuEjZfQuaGwTBus9y3GF9qBNyBtKR1+wjMSFIFAZM0EA1e/nJXmaHI4WstQgbbH/S1ZQsqXiEu2/oBZfZtyAv1DKszrM68kKUR6IwYPIWirVZ2g5jCmHhyNlfIaf73Nh+tdfZ57DWNlpIG9cyE6OGgTryQpRDojBgSo0Ukj6ZrNlHjeSsotTsozw3T1p4SviCGst7MvddCLmEimZYTeSEHCmOmCAkCgOm1HaZGV3qXLuT+rA+c6Cc8JToyodWQNdnD2Y+36iOFPqGfFNiwpAoDIENNrx21Btzr2wzKslcSbVa5O4va3eydlXLFJ4STv/cUUfg2UYtptBXNB1VTBgShVEg1dPP2qmtkt2p14O5j4I+nYDbo+pIIfk4EEwBNTlo4ZqYMCQKA6KK27n09pusZSfcqx0tJwYlerRZMYV5Hoy2HBUaKYiJQ6IwAKq6nUsnGm3GCOKLBeMVthRQFSvVfW+uOzDWomR5EoQTKKYgJgyJQi8JDAeqdCYbDffZmfACtWCMoF4P36d2tOJSalEJzT4SE4REoVfk9BjLup3zgrRzPOw1DofdQmb595HhEkKUIE8ULPp8PNm2bZuvrq4O7oaLi9F2k2nqdRbZF/qIffuKLzHLY1zLK1ji/eH7V7iPEEKEMLOb3X1b1mfao7kKBw4Ej+/aFe3JnCS5R3PRJdaZyReE+GJl7yOEEJ0gUajCwkLw+NIS7N4d9djNor+7d8PSUslLcCCqCFHl5eXMi5W9jxBCdILcR1VYWYEdO+DYsRPH5ucrWeXMS/AQu2tXsnTVc2TdhRB9R+6jXtFpN31lJQomzMywtHOR3Zd9ZuMlGptZuu+dEgQhxNAZuCiY2VPM7FNmdqeZ3WFmr4+Pn2FmN5rZ3fHf0wfdtlIsLUUR3fX16G8ZQdixA/bvZ8UvYnH/X/HyP3gePPgg111X7hKt68TCwuJi9F4IIXrMMEYKjwH/3t1/EHgu8FozeypwBXCTu58L3BS/H3927oRjx1jhYnbwXvaziDPD/vsfx44dJW17Qlhwj/6WriyEEOUZuCi4+73u/sX49VHgTuBs4ELg2vi0a4GXDbptnZLbiY+nG+3ktzjG5g31jh2LNKOQWFg6qyyEEOUZakzBzBaBZwKfB57k7vdCJBzAEwN1dpjZqpmtHjlyZGBtDVHYiY+nGx0ge9pRaIpqqZNKVRZCiPIMTRTM7HHAnwBvcPcHytZz993uvs3dt5111ln9a2BJCjvx8cKCBbINeGiKaqmTSlUWQojyDEUUzGyOSBBW3P3D8eGvm9mW+PMtwOFhtK0qhZ34eMbSrto7mOehDeeUXnSmFWtCiAExjNlHBlwN3Onu70h8dANwWfz6MuCjg25bJ5TqxC8tsXTfO9nd2NzZojOtWBNCDIiBL14zsxcAe4DbgPX48JuI4grXAwvAAeAX3P0bedca+OK1DHqwnk0IIQbKSC1ec/fPuLu5+3nu/kNx+bi73+/u29393PhvriAMhYxpRoWdeK0vEEKMEScNuwFjQ3pI0JxmBCzFOYmq1NEwQggxiij3UVly0mYHc1Z3UkcIIfrMSLmPRoJOXDqdrBXQ+gIhxJgxfaLQacqITtYKaH2BEGLMmD5R6DRlRCdrBbS+QAgxZkyfKHTq0ulkrYDWFwghxozpCzQr+CuEmHIUaE4il44QQgSZPlGQS0cIIYJM5+K1pSWJgBBCZDB9IwUhhBBBJApCCCFaSBSEEEK0kCgIIYRoIVEQQgjRYqwXr5nZESBjJdrEcCZw37AbMSD0rJPHtDwnjN+z1t09c5P7sRaFScfMVkOrDicNPevkMS3PCZP1rHIfCSGEaCFREEII0UKiMNrsHnYDBoiedfKYlueECXpWxRSEEEK00EhBCCFEC4mCEEKIFhKFEcLMZs3sS2b2sfj9GWZ2o5ndHf89fdht7AVm9gQz+5CZfdXM7jSzH5ngZ32jmd1hZreb2fvN7JRJeVYze5+ZHTaz2xPHgs9mZlea2T1mdpeZ/dRwWt0ZgWd9e/wbvtXMPmJmT0h8NrbPKlEYLV4P3Jl4fwVwk7ufC9wUv58ErgL+wt3/GfAMomeeuGc1s7OB1wHb3P3pwCxwEZPzrNcAL0kdy3w2M3sq0bM/La7zbjObHVxTu+Ya2p/1RuDp7n4e8PfAlTD+zypRGBHMbCvwL4E/TBy+ELg2fn0t8LIBN6vnmNnjgRcBVwO4+3fc/VtM4LPGnAScamYnAfPAPzIhz+runwa+kTocerYLgQ+4+6Puvhe4B/jhQbSzF2Q9q7t/wt0fi99+Dtgavx7rZ5UojA7/A/hPwHri2JPc/V6A+O8Th9CuXvM9wBHgj2JX2R+a2WYm8Fnd/f8BvwscAO4Fvu3un2ACnzVB6NnOBg4mzjsUH5sUfhn48/j1WD+rRGEEMLOXAofd/eZht2UAnAScD/yBuz8TeIjxdZ/kEvvTLwTOAb4b2Gxmlw63VUPDMo5NxHx4M9sJPAasNA9lnDY2zypRGA2eD/ysme0DPgD8uJk1gK+b2RaA+O/h4TWxZxwCDrn75+P3HyISiUl81hcDe939iLsfBz4MPI/JfNYmoWc7BDwlcd5WIlfaWGNmlwEvBZb8xKKvsX5WicII4O5XuvtWd18kClB90t0vBW4ALotPuwz46JCa2DPc/Z+Ag2b2A/Gh7cBXmMBnJXIbPdfM5s3MiJ71TibzWZuEnu0G4CIzO9nMzgHOBb4whPb1DDN7CfDrwM+6+7HER2P9rCcNuwEil98GrjezXyEyML8w5Pb0isuBFTPbBHwN+CWiDspEPau7f97MPgR8kci98CWidAiPYwKe1czeD1wAnGlmh4A3E/jNuvsdZnY9UQfgMeC17r42lIZ3QOBZrwROBm6MNJ/Pufurx/5ZleZCCCFEE7mPhBBCtJAoCCGEaCFREEII0UKiIIQQooVEQQghRAuJghhrzGzNzG6Js5D+bzObzzn3gmYG2pxzXm1mr6hw/wertDdRb5uZvTPw2T4zO7OT6wrRLVqnIMadh939hwDMbAV4NfCOTi/m7v+rR+0qus8qsDqIewlRBY0UxCSxB/g+M9sc57//uzjp3oXJk8xsJu6NPyFx7B4ze5KZ/aaZ/Yf42PeZ2V+a2ZfN7Itm9r2hG8ejkL9K7BOxEq9ixsyebWafja/zBTM7LTlqMbOamX0ibut7SOTOMbNL4zq3mNl7mimYzexBM9sVX/NzZvak+PiT4tz+X47L8/KuI0QaiYKYCOLU1D8N3AbsJEoV8mzgx4C3x5lYAXD3daL0Cz8X130OsM/dv5667Arw++7+DKKcRfcWNOOZwBuApxJlg31+vGr7g8Dr4+u8GHg4Ve/NwGfiBIE3AAtxu34Q+DfA8+PR0BqwFNfZTLSC9hnAp4FXxcffCfx1fPx84I6C6wixAbmPxLhzqpndEr/eQ7RPw2eJEgz+h/j4KcSGNsEHgf8M/BFRvqkPJj80s9OAs939IwDu/kiJtnzB3Q/F9W8BFoFvA/e6+9/F13kg/jxZ70XAz8ef/5mZfTM+vh14FvB38fmnciLB3HeAZnzkZuAn4tc/DrwivtYa8G0ze3nOdYTYgERBjDutmEKT2G3zr9z9rtTxJyXe/i2Rq+ksoo1g/mvqulnpj4t4NPF6jej/l1EubXLWOQZc6+5XZnx2PJGVs3mvEHnXEWIDch+JSeT/ApcnfPrPTJ8QG9SPEAWl73T3+1OfPwAcMrOXxdc4OW9mUw5fBb7bzJ4dX+e02NWV5NPE7hwz+2mgua/xTcC/NrMnxp+dYWb1gvvdBCzH589atNNdJ9cRU4pEQUwibwXmgFst2mj9rYHzPghcSsp1lODlwOvM7FYil9STqzbE3b9D5M9/l5l9mWhf31NSp70FeJGZfRH4SaLsorj7V4DfAD4Rt+FGYEvBLV8P/JiZ3UbkVnpah9cRU4qypAohhGihkYIQQogWEgUhhBAtJApCCCFaSBSEEEK0kCgIIYRoIVEQQgjRQqIghBCixf8HM0h6FuIqpwMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(An.pelvic_incidence,An.lumbar_lordosis_angle,color='red')   #anormal \n",
    "plt.scatter(N.pelvic_incidence,N.lumbar_lordosis_angle,color='blue')\n",
    "plt.xlabel(\"Pelvic Incidence\")\n",
    "plt.ylabel(\"Lumbar Lordosis Angle\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can't say its correlated but it is more possible to have anormal status if the patient has higher pelvic and lumbar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=dataframe.status.values\n",
    "x_data=dataframe.drop('status',axis=1)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=(x_data-np.min(x_data))/(np.max(x_data)-np.min(x_data))  #scale all values between 0 and 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "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>pelvic_incidence</th>\n",
       "      <th>pelvic_tilt numeric</th>\n",
       "      <th>lumbar_lordosis_angle</th>\n",
       "      <th>sacral_slope</th>\n",
       "      <th>pelvic_radius</th>\n",
       "      <th>degree_spondylolisthesis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.355688</td>\n",
       "      <td>0.519900</td>\n",
       "      <td>0.229180</td>\n",
       "      <td>0.250857</td>\n",
       "      <td>0.307461</td>\n",
       "      <td>0.025148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.124501</td>\n",
       "      <td>0.296783</td>\n",
       "      <td>0.098578</td>\n",
       "      <td>0.144629</td>\n",
       "      <td>0.476649</td>\n",
       "      <td>0.036365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.411666</td>\n",
       "      <td>0.513932</td>\n",
       "      <td>0.322995</td>\n",
       "      <td>0.307661</td>\n",
       "      <td>0.386097</td>\n",
       "      <td>0.017523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.416151</td>\n",
       "      <td>0.557414</td>\n",
       "      <td>0.271260</td>\n",
       "      <td>0.289436</td>\n",
       "      <td>0.341826</td>\n",
       "      <td>0.051838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.227272</td>\n",
       "      <td>0.289479</td>\n",
       "      <td>0.128129</td>\n",
       "      <td>0.247022</td>\n",
       "      <td>0.409579</td>\n",
       "      <td>0.044173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pelvic_incidence  pelvic_tilt numeric  lumbar_lordosis_angle  sacral_slope  \\\n",
       "0          0.355688             0.519900               0.229180      0.250857   \n",
       "1          0.124501             0.296783               0.098578      0.144629   \n",
       "2          0.411666             0.513932               0.322995      0.307661   \n",
       "3          0.416151             0.557414               0.271260      0.289436   \n",
       "4          0.227272             0.289479               0.128129      0.247022   \n",
       "\n",
       "   pelvic_radius  degree_spondylolisthesis  \n",
       "0       0.307461                  0.025148  \n",
       "1       0.476649                  0.036365  \n",
       "2       0.386097                  0.017523  \n",
       "3       0.341826                  0.051838  \n",
       "4       0.409579                  0.044173  "
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()   #scaled values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train Test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "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=0.3,random_state=42)   #split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "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>pelvic_incidence</th>\n",
       "      <th>pelvic_tilt numeric</th>\n",
       "      <th>lumbar_lordosis_angle</th>\n",
       "      <th>sacral_slope</th>\n",
       "      <th>pelvic_radius</th>\n",
       "      <th>degree_spondylolisthesis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>0.547397</td>\n",
       "      <td>0.651030</td>\n",
       "      <td>0.396005</td>\n",
       "      <td>0.366864</td>\n",
       "      <td>0.436897</td>\n",
       "      <td>0.039892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>0.466958</td>\n",
       "      <td>0.397938</td>\n",
       "      <td>0.399299</td>\n",
       "      <td>0.420809</td>\n",
       "      <td>0.379990</td>\n",
       "      <td>0.027135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0.567349</td>\n",
       "      <td>0.706883</td>\n",
       "      <td>0.419356</td>\n",
       "      <td>0.357071</td>\n",
       "      <td>0.597676</td>\n",
       "      <td>0.198769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.175250</td>\n",
       "      <td>0.341026</td>\n",
       "      <td>0.197765</td>\n",
       "      <td>0.170401</td>\n",
       "      <td>0.581075</td>\n",
       "      <td>0.038347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.227212</td>\n",
       "      <td>0.350010</td>\n",
       "      <td>0.155129</td>\n",
       "      <td>0.215604</td>\n",
       "      <td>0.414736</td>\n",
       "      <td>0.007524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pelvic_incidence  pelvic_tilt numeric  lumbar_lordosis_angle  \\\n",
       "299          0.547397             0.651030               0.396005   \n",
       "288          0.466958             0.397938               0.399299   \n",
       "72           0.567349             0.706883               0.419356   \n",
       "15           0.175250             0.341026               0.197765   \n",
       "10           0.227212             0.350010               0.155129   \n",
       "\n",
       "     sacral_slope  pelvic_radius  degree_spondylolisthesis  \n",
       "299      0.366864       0.436897                  0.039892  \n",
       "288      0.420809       0.379990                  0.027135  \n",
       "72       0.357071       0.597676                  0.198769  \n",
       "15       0.170401       0.581075                  0.038347  \n",
       "10       0.215604       0.414736                  0.007524  "
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn=KNeighborsClassifier(n_neighbors=3)   #neighnors is a hyperparameter. means the num of neighbor we want it to consider\n",
    "knn.fit(x_train,y_train)\n",
    "prediction=knn.predict(x_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets see the score of this knn classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: 0.7741935483870968\n"
     ]
    }
   ],
   "source": [
    "print(\"score: {}\".format(knn.score(x_test,y_test))) #score when n_neighnors is 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lets see the best n_neighbors value for our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.7634408602150538,\n",
       " 0.7419354838709677,\n",
       " 0.7741935483870968,\n",
       " 0.7526881720430108,\n",
       " 0.7634408602150538,\n",
       " 0.7741935483870968,\n",
       " 0.7741935483870968,\n",
       " 0.7526881720430108,\n",
       " 0.7634408602150538,\n",
       " 0.7526881720430108,\n",
       " 0.7526881720430108,\n",
       " 0.7526881720430108,\n",
       " 0.7741935483870968,\n",
       " 0.7741935483870968]"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_list=[]\n",
    "for each in range(1,15):\n",
    "    knn=KNeighborsClassifier(n_neighbors=each)\n",
    "    knn.fit(x_train,y_train)\n",
    "    score_list.append(knn.score(x_test,y_test))\n",
    "score_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'accuracy')"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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471Wb/G7KmoVDQU4OTbKwmPC7KcZjJ5xUsPU4TMGJjMVpaajOOoe6u62BgBRP4BidnOVHp0Y51LW1qFfR6ww1MruQ4O1ztqhTqSvViiqwwFH0omMzWfc2AGqrKtjRUl80lVVPvBxlMaFFnaaCixcRq6wqff2DEzTUVGY0FVCxscBR5KJZDv5LtbeIKqu6e6PsbW9cWv62WF27pYGKgNh9jjJwfDDGnraGou4hr8QCRxFT1eSo8TV+oukMBXnn3DRTsws5blluvT06Re+ZMQ53bfW7KetWW1XBzpb6okkRmrVR1ZKtqAKPA4eI3Cki/SJySkQeTvP8Z0Sk1/l6TUQWRaTZeW6jiHxHRPpE5LiIvN/Z/jkRiaQcd5eXP0MhG5+ZZ3puMatR46nctMmJAu919PRGEYH7SiBwgLOok/U4StrgRJzxmfmSvL8BHgYOEakAvgJ8BNgHPCAi+1L3UdUvqGqXqnYBnwVeUNXzztN/AjytqmFgP3A85dAvucep6lNe/QyFLrI0hmNtM8QWQ2WVqtLTG+HndzbTvsaUXKHZGwpy5vwMkwXe0zNr576nwiW06l8qL3scNwGnVPW0qs4B3wIOXWH/B4DHAESkEbgF+HMAVZ1T1TEP21qU1lqK69q2aQP11RUFfaP21cg4p0eninKKkZV0Fvkkk2Z1bkVVqc2K6/IycHQAZ1Ien3W2XUZE6oA7gcedTdcAI8BfiMgxEfkzEUldH/QhEXlFRL4uIsVb1L9OF0eNry1wBALCngJPm3Qfi1JdEeAj1xfHgk2ZsMqq0tc/OMHWplqa6qr8boonvAwc6UoJVprf4l7gxZQ0VSVwI/BVVT0ATAHuPZKvAruALmAA+GLaFxd5UESOisjRkZGRtf0EBS46HqemMkBzffWazxEONdI3GEO18KYeWUwo33slym3hVpo2lM4bcNumDTTUVBZ0wDbr0zcYK6mlYpfzMnCcBbanPN4GRFfY96M4aaqUY8+q6k+cx98hGUhQ1SFVXVTVBPCnJFNil1HVr6nqQVU92NpafBPiZSJyYYaOjRsQWXu5XzgUZGx6nuECnOr7H988x0hstqTSVAAi4twgtx5HKZpbSPDmyGTJVlSBt4Hjp8BuEdkpItUkg8OR5TuJSBNwK9DjblPVQeCMiHQ6m24H3nD2T81Z3A+85k3zC18ky3U40llaC7sABwJ290YI1lRyW3iL303JOXeSyULs6Zn1OT06yfyilmxFFXgYOFR1AXgIeIZkRdS3VfV1EfmkiHwyZdf7gWdVdflse58Cvikir5BMS/2hs/2PRORVZ/ttwKe9+hkK3XoG/7kKNd8en1/k6dcGufO6UMlNSQ3J3/v4zDyDE3G/m2JyzH0vlXKqytMl1JxS2aeWbXtk2eNHgUfTHNsLHEyz/WO5bGOxml1YZDg2u+aKKtfGumpCjbUFlzZ5/vgwk7MLRT/FyEpSS6FLpczYJPUNxqiqEK5pKa3Fm1LZyPEiNTie/KS63lQVJJeSLbTA0d0bYUuwhvdds9nvpnjCLdN0yzZN6egbmGBXa0NRrhmTqdL9yUqcO/gv23U40ukMBTk1HGO+QKb6Hpue44f9w9y3fysVJTjPD0BTXRVbm2rpt8qqktM/GCvp+xtggaNouYP/cjHz5t5QI/OLylujhbGo01OvDjK/WPwz4a7GKqtKz/j0PNHx+NIgz1JlgeMKvnvsLJ878rrfzUgrOhZHBEJNuelxQOFUVnX3RtjVWs97tpb2my/c3sibI5PMLfjf0xufmeeLz/YzPWfToKxH/9LiTdbjKFt9gzG++ZN3CiaFkyo6NkNrQw01leuvONrV2kBlQAqisioyNsM/vXWew10d6xqfUgzCoSDzi8rp0Um/m8Jj//Qu/9cPTtHTu9JQK5MJd1CnparKmJvCOT1SGCmcVOuZTn256soAu1obCiJtcsS5cB0qsUF/6YQLaM6q7mORS/41a9M3GKNpQxWhxvVnAgqZBY4rcFM4hTg1RDQHg/9ShduDBXEB6+mNcONVG7lqc53fTfHcNa31VFUIx32urOofjNE3GOOq5jp+8tb5pTnQTPb6BiboDAVLvrdsgeMK3BROIXwST+Uu4JTLJSk7Q0EiYzOMz8zn7JzZ6hucoG8wVvI3xV1VFcment+VVd29ESoCwpf+RRcAR162dNVaJBLKiaFJ9pZ4mgoscFzRUgqnQG4au85NzTG7kGBrDm6Mu/Y6aRM/F3XqPhalIiDcXUIz4a4m7HNlVSKhHOmN8ovXtvDeqzfRtX2jpavWKDKWXGOl1CuqwALHqgolhZNqaTr1TblL5yyl5XwKkskLWIRbdrewuaHGlzb4IdzeyMB4nPFpf3p6R9+5QGRshsMHkqsrHu7aSt9grOD+5ovB0uJNJV5RBRY4VtUZChL18Y2dTjSHg/9c7U21NNZW+vbp96dvnyc6Hi+bNJXL7/to3b0RNlRV8KF9IQDucQZddvdaryNb7oeuPSW6eFMqCxyrcFM4/QW0LvfZHA7+c4nI0tocfujujVJXXcEd+9p8eX2/+Pn3NbeQ4KlXB7hjXxv1Nclp61oaavjFa1s40hslkbCZe7PRN5QsMGio8XQKwIJggWMVfn8iTCc6Fqe+uiLnixu5abl8T/XtXsA+tK+NuurSf9OlamusoWlDlS+VVS+cGGFsen4pTeU6fGArkbEZjr5zIe9tKmZuRVU5sMCxCr9TOOlEnTEcuS756wwFmZxdWOrR5MsP+4cZn5nnUJmlqcDt6QV9qazq7o3QXF/NL+2+dKGzD+0LsaGqwtJVWYjPL/LW6FRZVFSBBY5VLaVwCqiyKpeD/1L5NSCtpzfK5vpqfunalry+bqEIO4s65TM1FIvP8/03hrj7+naqKi69DNTXVHLHvjaeenWgIKZDKQanhidJKGVRUQUWODISbg9yYmiyYHK+uR785/IjLReLz/P940Pcc0M7lRXl+ecYbm9kam5xacbjfHjm9SFmFxKXpalchw9sZWx6nhdOjOStTcWsnCqqwAJHRtwUTj7f2CuJzy9ybmoupzfGXQ01lWxv3pDXtNzTrw0yu5AoyzSVy49JJnt6I2xv3sCNV21K+/wv7W6lub7a0lUZ6huYoKYywI7N9X43JS8yChwi8riI3C0iZRloUldr85sXpbipOtvyW1nV0xvl6s11HNi+MW+vWWjcRZ3ylSIcjsV58dQoh/avPJFkVUWAu69v5/tvDBGLF04peqHqH4qxpy1YsuvHLJdpIPgq8KvASRH5vIiEPWxTwfF7cFyqpQWcPFpudG97kLdGp4jPL3py/lTDE3H+25ujHNq/teTn9rmS+ppKrmquy1vA/t7LAySUFdNUrsMHtjK7kODZ14fy0q5idnwgVjYVVZBh4FDV76vqrwE3Am8Dz4nIfxORT4hIbmtCC9BSCqcAxnJcHDXuTeDoDAVZTCinhr2f6vvIy1ESSlmnqVzJqUfy88GkpzfCe7Y2cu2WK1/obrxqE9ubN1i6ahXnJmcZnZwt+anUU2WcehKRzcBvAP8aOAb8CclA8pwnLSswnW2FUVkVGYsTEGjzaNrmfFZW9fRGub6jiV2tDZ6/VqELh/LT0zs9MskrZ8c5nMG09SLCof0dvHhqlOFY3NN2FTP3vRIuk4oqyPwex98A/wDUAfeq6n2q+leq+imgLN71+UzhXEnkwgxtjbWXlVDmyo7NdVRXBjz/9PvmyCSvRsY51HXldEm56Aw1klA87+l190YRgXv3Z/Z7P3xgKwmFJ14e8LRdxey4EzgsVXW5L6vqPlX9P1X1kr8gVT240kEicqeI9IvIKRF5OM3znxGRXufrNRFZFJFm57mNIvIdEekTkeMi8n5ne7OIPCciJ51/05eF5FhnKJiXN/ZqojmeTn25yooAe9q8X9Sp51gkqwtYqXPLOL38vasqPb0R3n/N5oyXHL52S5D3bG2kx9JVK+ofnKCloZrWYPlMzplp4NgrIhvdByKySUT+zZUOEJEK4CvAR4B9wAMisi91H1X9gqp2qWoX8FngBVU97zz9J8DTqhoG9gPHne0PA8+r6m7geeex5wqlsio67s3gv1ReV1apKt29UX5h12bPUm7FZsfmemoqA56mQ3vPjPHOuemM0lSpDnd18PLZcd4aLbyVMAtB32B53RiHzAPHb6rqmPtAVS8Av7nKMTcBp1T1tKrOAd8CDl1h/weAxwBEpBG4Bfhz5/XmUl7/EPAN5/tvAIcz/BnWZSmF4+N9jkRCGRiLex449rYHGYnNcm5y1pPzHzszxrvnp8tiedhMVQSEPW1BTyc77OmNUl0Z4M7rQ1kdd+/+rYjYsrLpLCaUE0Oxsrq/AZkHjoCk1Es6vYnqVY7pAM6kPD7rbLuMiNQBdwKPO5uuAUaAvxCRYyLyZyLijqxpc9Nlzr9bVjjngyJyVESOjoysf/Srm8Lxc5bc0clZ5hYTnlVUudxPT17dID/iXsCuy+4CVuo6Q0HPJjtcWEzwxCtRbg9vobE2u0LIUFMt779mMz29kbxPgFno3jk3RXw+YT2OFTwDfFtEbheRD5DsGTy9yjHpCvNX+qu7F3gxJU1VSbJi66uqegCYIsuUlKp+TVUPqurB1tbW1Q/IQL4Hxy3njuHo8Gjwn8vLtJx7Afvg3uwvYKUuHAoy6pR25tqLb55jdHJuzb28w10dvH1umpfPjue4ZcXN/XC113ocaf074AfA/wj8Nsl7C7+/yjFnge0pj7cBKy1m/FGcNFXKsWdV9SfO4++QDCQAQyLSDuD8O5zhz7BuXqdwVrM0+M/jVFVrsIbN9dWe9Dh+dGp0XRewUuZlKXTPsQiNtZXcFl7bh6g7rw9RXRmwdNUyxwdjBAR2t5VFcemSTAcAJlT1q6r636vqr6jq/6Oqq9Wl/hTYLSI7RaSaZHA4snwnEWkCbgV6Ul5vEDgjIp3OptuBN5zvjwAfd77/eOpxXvM6hbOapcF/HgcOSFb5eFGS29MbpbG2kl/uzE0vsJR4VVk1M7fIM68Pctf17dRUVqzpHI21Vdwe3sITr0RZWLQZc139gxPsaKmntmptv9dilek4jt1OaewbInLa/brSMaq6ADxEMs11HPi2qr4uIp8UkU+m7Ho/8KyqLi/Z+BTwTRF5BegC/tDZ/nngDhE5CdzhPM4L9xPhcd8CR5xgbSXBPKR4OtsaOTE0yWIOZwSenlvgmdcHufuGtV/ASllLQw0tDdU5L8B47vgQU3OL6+7lHerqYHRyjhffPJejlhW/vsFYWY0Yd2W63NpfAP8e+BJwG/AJ0t/DuISqPgU8tWzbI8sePwo8mubYXuCyMSKqeo5kDyTvLqZw/KmsOnvB2zEcqcKhIDPzi7x7fpqdLbmZ8fO5N4aYzsEFrJSFQ405L8DoORahvamWn9/ZvK7z3BZupbG2kp5jEW7dYz3GqdkF3j0/za/cuM3vpuRdpvc4Nqjq84Co6juq+jngA941q3AlUzj+paryFjja3bRc7oJkT2+UrU213LRjfRewUtbpLOqUq57e+ak5Xjgxwn37txJY58ytNZUV3HV9O8+8PsjMnL8zKBSCE0MxVMtrxLgr08ARd6ZUPykiD4nI/axQBlvqkimc3L2xs5GPwX+u3VuCiJCz8tDzU3P8/YkR7u1a/wWslIVDQWYXErxzLjeD7Z58dYCFhOasl3eoq4OpuUWeO24z5pZrRRVkHjh+l+Q8Vf8WeC/w61y8QV1Wwu1B4vO5e2Nnamp2gbHp+bwFjg3VFezcXJ+zQoAnX4mykNCsRy2Xm1yXQvcci7B7SwN7c7Qy3c/vbCbUWEuPVVfRNxijrrqCbR6PqypEqwYOZ7DfP1fVSVU9q6qfcCqrfpyH9hWcsE+VVV5Pp55OZw6n+u7ujdLZFmRve/l9OsvG7rYGApKbwHHm/DRH37nA4QMrL9iUrUBAuK9rKy+cGOH81FxOzlms+gYn6AwFy7IHvWrgcMpu3yu5+ssrcru3BAlI/iur8jX4L1U41Mg756eZnltY13nOnJ/mpXcucGiVhYMM1FZVsKOlPieVVUdeTg6bui/HE0ke6trKQkJ58tXynTFXVcu2ogoyT1UdA3pE5GMi8t+5X142rFBtqK5gx+b6vFdW5WvwX6rOUBBVODG0vhmB3ZlVc30BK1V7c1BZpap0H4tw8OpNbG+uy1HLkva1N7J7S0NZp6uGY7OMTc+X3RxVrkwDRzNwjmQl1b3O1z1eNarQ+VFZFR2boTIgbAnmr8exNweVVe5MuDftaGbbptxewEpVZyjIO+emmZpde0/vjYEJTg5PerK6oohw+EAHR9+5wJnz0zk/fzE47vQIy7GiCjIfOf6JNF//0uvGFarOtkbePb++N3a2omNxQk21VOQxn7p9Ux111RXrqqx6PTrBqeFJS1NlwU1/nFhHr6OnN0plQLj7+vZcNesSbu/RTYeVm4ur/pVn4MhoAKCI/AVpJigs1+ARbndTODEOXJWXdaSIXMhfKa4r4E71vY7eVU9vhKoK7y5gpSi1smotf1+LCeVIb5Rb97TSXL/aJNZrs725joNXb6L7WIR/88u7cnbzvVj0DcYINdaysc6b32+hyzRV9QTwpPP1PNAI+LsUno/8qKyKjM2wLc+BA5I/a9/gxJqm015MKEdejnLrni1l+wZbi22bNlBfXbHmv6+fvHWOwYm4J2mqVIcOdHByeJI3fFyjxi99g7GlQbLlKNNU1eMpX98E/jlwnbdNK1xuCidf9zkWE8rghPcLOKUTDgW5MD3PSCz7GYF/cvocQxOzHLY0VVYCAWHPOkqhe45Fqa+u4I69bTlu2aXuvr6dyoBwpLe80lXziwlODZffqn+pMu1xLLcbuCqXDSkmbgrHi9lj0xmaiLOYUF8CR+c6Jnbs7o3QUFPJBz2+gJWicCi59ku2Pb34/CJPvTbAh98TYkO1txNJNtdXc+ueVo68HCXhw0wKfnlrdIr5RS3LEeOuTGfHjYnIhPsFfI/kGh1la69TWZWPFdGiS6W4+V+f+2JaLrsgGZ9f5G9fHeTD7wmV3ZTTuRAOBRmbnmc4y57eD/uHicUXPE9TuQ4d6GBgPM5P3jq/+s4lws00WI9jFaoaVNXGlK89qvr46keWrs62tb2x18Idw+HH1Aab6qtpa6yhL8vKqr/rGyY2u8ChLktTrYV7UTqe5f2D7mNRWhqquXnXZi+adZk79rZRX12xNFanHPQNTFAZEHa1ltfiTaky7XHc7yy45D7eKCKHPWtVEVhK4eThxmB0LA5Ae5M/c+K4aZNsdPdGaGmo4RfydAErNWspwBifmecHfcPcc8NWKivWmoXOzobqCj78nhBPvTrA7EJ5zJjbPxhjV2sD1ZX5+R0Xokx/8n+vqkuLDavqGMn1OcpWPiurImPTbKyror4m0+VTciscCnJqeJL5DFd+G5+e5+/6Rrh3f3veLmClZmNdNaHG2qwC9tOvDTC3mOBwntJUrkMHOpiIL/B3fSN5fV2/9A2W941xyDxwpNvPn6tYgVhK4eQhcETH4nlbhyOdcHuQucUEb49mNiPw37oXMJsJd12ynaGg+1iUHZvr2L+tafWdc+jmXZtpaagui3TVRHyeyNhMWZfiQuaB46iI/CcR2SUi14jIl4CXvGxYMVhLCmctomP5H/yXqrMtu8qq7t4IO1vquSHPF7BS0xkKcmo4llFPb3A8zo/fOsehrtzNhJupyooA99ywlef7hpmIz+f1tfOt3EeMuzINHJ8C5oC/Ar4NzAC/7VWjikU4izf2ekTyuGRsOru21FMZkIwqqwbGZ/jJW+c51LW17EYT59reUCPzi8pbGfT0jrwcQZW8p6lchw90MLeQ4OlXB315/XzpWwoc5VuKC5lXVU2p6sOqetD5+l9UNb8rGRWgcHsw4zf2Wk3E54nNLvgaOGoqK7imtT6jyqojvdHkBczSVOuWTWVV97Eo+7c15Wx9+Gzt39bEjs11dJd4uqpvYIJgbSXtTfkvjS8kmVZVPSciG1MebxKRZzxrVZFwUzhepquiPkynnk6mabnu3ij7t29kh08XsFKyq7XB6eld+fd+cijGGwMTOVsedi1EhENdHfzj6XMMjsd9a4fX+gdj7A01ln1vOtNUVYtTSQWAql6gTNccT+WmcHKx6M5KIhf8G/yXqjMUJDI2c8Uc9omhGMcHJjhsYzdyoroywK7WhlUDdndvhIDAPfv9nUjy8IEOVOF7JTpjrqrSbxVVQOaBIyEiS1OMiMgO0syWu5yI3Cki/SJySkQeTvP8Z0Sk1/l6TUQWRaTZee5tEXnVee5oyjGfE5FIynF3Zfgz5JybwvGyJNePJWPTcdfmOHGFn7X7WISKgHDPDRY4ciXcfuXZiVWVnt4oN1/bkte1WtLZ2VLP/m1NJZuuiozNEJtdKPuKKsg8cPwB8CMR+UsR+UvgBeCzVzrAWav8K8BHgH3AAyKyL3UfVf2CqnapapdzvhdUNXXugtuc5w8uO/2X3ONU9akMfwZPeF1ZFRmLU10RoKW+xrPXyMRqc1YlEhcvYK1Bf9taStye3vhM+p7eS+9c4OyFmYK5p3Soq8NZgyW/C53lg3uPr9wrqiDzm+NPAweBfpKVVb9HsrLqSm4CTqnqaVWdA74FHLrC/g8Aj2XSnkKSSQpnPSJjM7RvrCWQxwWc0tnaVEuwtnLFyqqX3r1AZGzG0lQ55k6kt9KiTt29EWqrAnz4ulA+m7Wie/a3E5DkzfpS4y7nu6fNAkemN8f/Ncl1OH7P+fpL4HOrHNYBnEl5fNbZlu78dcCdQOr8Vwo8KyIviciDyw55SEReEZGvi0jalW5E5EEROSoiR0dGvBvRenF5VW8+YUXH/C3FdYlIcm2OFSqruo8lL2Afek9hXMBKhZtPT3cfbX4xwZOvDPDBvW00+DSrwHJbgrXcfG0LPS9H8jIBaD4dH5hg26YNBGur/G6K7zJNVf0O8HPAO6p6G3AAWO1qnO4j8kp/SfcCLy5LU92sqjeSTHX9tojc4mz/KrAL6AIGgC+mO6Gqfs0tH25tbV2lqWvXGfK2ssrvwX+pwqFG+tPMCDy3kODJVwe4Y1+oYC5gpaK9qZbG2sq0f19/f2KEC9PzBZOmch3u6uDM+Rl+9u4Fv5uSU/2DsbIfv+HKNHDEVTUOICI1qtoHdK5yzFlge8rjbcBK/dePsixNpapR599h4LskU1+o6pCqLqpqAvhTd7tf3BSOF5VV84sJhnxawCmdzlCQ2OzC0my9rr8/McLY9LylqTyQ7Omlv4/W3RtlY10Vt+zx7oPRWnz4uhC1VYGSSlfNLixyenTK7m84Mg0cZ51xHN3AcyLSw8pBwPVTYLeI7BSRapLB4cjynZxZd28FelK21YtI0P0e+BDwmvM4tebwfne7X9wUjhepqsHxOAnFlyVj01kpLdfzcpRNBXgBKxVuZVVqT29qdoHn3hjk7uvbC26WVnfxridfHfB8VoV8OTU8yWJCraLKkenN8ftVdUxVPwf8b8CfA4dXOWYBeAh4BjgOfFtVXxeRT4rIJ1N2vR94dtlI9DaSVVwvA/8EPOncoAf4I6dM9xXgNuDTmfwMXlophbNehTL4z+XeFEz99DvpXsBuaKfKZsL1RGcoyOTsAmcvXOzpPfvGIPH5/M+Em6nDXR2cn5rjH06Wxoy5VlF1qawT0qr6Qhb7PgU8tWzbI8sePwo8umzbaWD/Cuf8WKavny+pKZxtm+pydt6Ijyv/pROsrWLbpg2XBI5nX3cuYAWWZy8lbl69fzDG9ubk31f3sSgdGzfw3qvS1ob47pY9rWysq6L7WJQPhIt/6eD+oRjVlQF2bLYZEWDta46bFG4KJ9tV8lZTaD0OwEnLXbyf090bZdumDbz36sK8gJUCt7LKLQcdnZzlR6dGOdS11fcy7ZVUVwa4+/p2nntjiKnZBb+bs27HBybYvaXB1pdx2G8hB9wUTv8KtfZrFRmL09JQXVBrdneGgrw5MsXswiIjsVl+dHLEZsL1WENNJdubNyxNdvjEy1EWE1qwaSrX4QMdzMwv8twbQ343Zd2soupSFjhywE3h5HoZ2UgBleK6wqFGFhPKm8NTPPFKlITNhJsXnW2NS0UJ3b1RwqFgwQ9Ee+9Vm+jYuKHopyA5PzXHcGzW7m+ksMCRI15UVhXK4L9U7punb3CC7t4o+9ob2V3gF7BSEA4FOT06xYmhGL1nxgq+twEQCAj3dW3lH06OMjo563dz1qzPSc3a5IYXWeDIkXCokdOjyRROLqhqQQ3+c+1sqae6IsDTrw3y8pkxDtnYjbwItwdZTChffLYfEbhvf3H83g93dbCYUJ58ZcDvpqzZ0qp/Voq7xAJHjnSGkm/sU8OTOTnf2PQ803OLBRc4KisCXLulgWffGEpewCxw5IXb03vm9SFu2tFccH8XK+kMBQmHgkWdruobiNFcX01rg03e6bLAkSO5rqxyS3ELLVUFFz95/fzOZtqbCq99pWjH5vqlgX7FkKZKdfhAB8feHeOdc8W5aGjfUIxwKGgFIClsYqEccd/YuaqsihZy4HA+/dpN8fyprAiwe0sDJ4cmues6fxdsytZ9+7fyH5/u48H/9yW2NBbfp/bj0Ql+7X1Xrb5jGbHAkSPuGztXlVXRAhv8l+rO97Tzytlx7r6huC5gxe43fmEHo5NzNNUV1+ysWzdu4MFfuoZ/evs8k0U4pmP/9iZbnGwZCxw51BkK8qOTozk5V2RshtqqAM311Tk5Xy5dtbmOL//qjX43o+z8s4PbV9+pQH32rr1+N8HkkN3jyKG9oUaGY7Ocn5pb97miY8lZcS2vaowpNBY4cqgzZYzDekUKcAyHMcaABY6cCuewsioyNsNWq1gyxhQgCxw51NpQQ3N99bpHkLvzQHVsssBhjCk8FjhyaGld7nWW5A6Ox4HCmhXXGGNcFjhyrDMU5MRgjERi7Ys6RS4UbimuMcZY4MixvaFGZuYXeff89JrP4Y4a37Yxd4tCGWNMrljgyLFcVFZFx+KIQFtT8Y2yNcaUPgscObanLYjIpetyZysyNk1rQw01lYWzgJMxxrgscOTYhuoKdmyuX1dJbnQsbhVVxpiCZYHDA+FQcF2THRbiOhzGGOPyNHCIyJ0i0i8ip0Tk4TTPf0ZEep2v10RkUUSanefeFpFXneeOphzTLCLPichJ599NXv4Ma9EZCvL2uSmm57Kf0E1VbdS4MaageRY4RKQC+ArwEWAf8ICI7EvdR1W/oKpdqtoFfBZ4QVXPp+xym/P8wZRtDwPPq+pu4HnncUEJh4Kowsmh7Bd1Ojc1x+xCwgKHMaZgednjuAk4paqnVXUO+BZw6Ar7PwA8lsF5DwHfcL7/BnB4PY30QjjUCKytsuridOoWOIwxhcnLwNEBnEl5fNbZdhkRqQPuBB5P2azAsyLykog8mLK9TVUHAJx/t6xwzgdF5KiIHB0ZGVnHj5G9q5rr2FBVsabKKhv8Z4wpdF4GjnTzga80nPpe4MVlaaqbVfVGkqmu3xaRW7J5cVX9mqoeVNWDra2t2Ry6boGAsCcUXFNllQ3+M8YUOi8Dx1kgdeWZbUB0hX0/yrI0lapGnX+Hge+STH0BDIlIO4Dz73AO25wz4bYgfYMTqGY39Uh0LE59dQWNG2yNLWNMYfIycPwU2C0iO0WkmmRwOLJ8JxFpAm4FelK21YtI0P0e+BDwmvP0EeDjzvcfTz2ukITbg1yYnmckNpvVcZGxaVvAyRhT0Dz7WKuqCyLyEPAMUAF8XVVfF5FPOs8/4ux6P/Csqk6lHN4GfNe5eFYC/5+qPu0893ng2yLyr4B3gX/m1c+wHhenHomxpTHz+xU2+M8YU+g8zYeo6lPAU8u2PbLs8aPAo8u2nQb2r3DOc8DtuWynF1Irq27Zk/k9lujYDNdva/KqWcYYs242ctwjzfXVbAnWZFVZNTO3yLmpORvDYYwpaBY4PBRub8yqsio6nqyossBhjClkFjg8FA4FOTU8ycJiIqP9bfCfMaYYWODwUDgUZG4xwVujU6vvjA3+M8YUBwscHkqtrMpEdGyGgEAoiyosY4zJNwscHrp2SwMVAcl4zqrIWJxQYy2VFfbfYowpXHaF8lBNZQXXtNTTn0WPw+5vGGMKnQUOj4XbGzmeYWVVxAKHMaYIWODwWDgUJDI2w0R8/or7JRLKwPiMjRo3xhQ8CxweCzs3yE+skq4anZxlflGtx2GMKXgWODyWaWXV2TF38J9VVBljCpsFDo91bNxAsKZy1Rvk0aXAYetwGGMKmwUOj4kInaHgqiW5F0eNW4/DGFPYLHDkQbg9SN9g7IqLOkUuzBCsrSRYW5XHlhljTPYscORBZ6iRWHyB6Hh8xX0iY3Gb3NAYUxQscOTBXucGef8V0lXRsRkLHMaYomCBIw/2OIHjSgMBbfCfMaZYWODIg8baKjo2blixsmpydoHxmXkb/GeMKQoWOPIkfIXKqgFbh8MYU0QscORJuD3I6ZEpZhcWL3vOBv8ZY4qJBY486Qw1spBQ3hy+fFEnG/xnjCkmFjjyZKmyaujydFV0bIbKgNAarMl3s4wxJmueBg4RuVNE+kXklIg8nOb5z4hIr/P1mogsikhzyvMVInJMRJ5I2fY5EYmkHHeXlz9Druxoqae6IkBfmsqqyIUZQk21VATEh5YZY0x2PAscIlIBfAX4CLAPeEBE9qXuo6pfUNUuVe0CPgu8oKrnU3b5HeB4mtN/yT1OVZ/y5ifIraqKANduaUg72WHUBv8ZY4qIlz2Om4BTqnpaVeeAbwGHrrD/A8Bj7gMR2QbcDfyZh23Mq5UqqyI2+M8YU0S8DBwdwJmUx2edbZcRkTrgTuDxlM3/Gfh9IJHmkIdE5BUR+bqIbFrhnA+KyFEROToyMrKW9udcuD3I0MQsF6bmlrYtLCYYnIhbKa4xpmh4GTjSJexXmuXvXuBFN00lIvcAw6r6Upp9vwrsArqAAeCL6U6oql9T1YOqerC1tTXbtnuiM9QIXLo2x3BslsWE2uA/Y0zR8DJwnAW2pzzeBkRX2PejpKSpgJuB+0TkbZIprg+IyH8FUNUhVV1U1QTwpyRTYkUh3ZxVURv8Z4wpMl4Gjp8Cu0Vkp4hUkwwOR5bvJCJNwK1Aj7tNVT+rqttUdYdz3A9U9ded/dtTDr8feM27HyG3WoM1bKqruqTHEbHBf8aYIlPp1YlVdUFEHgKeASqAr6vq6yLySef5R5xd7weeVdXLR8al90ci0kUy7fU28Fs5bbiHRIRwqDFt4LAehzGmWHgWOACcUtmnlm17ZNnjR4FHr3COHwI/THn8sRw2Me86Q0G+ffQMiYQSCAjRsRk21VVRV+3pf4UxxuSMjRzPs73tQabnFjlzYRpIDv6z3oYxpphY4Mgzt7LKXZvDBv8ZY4qNBY4829PWgAhLa3NEbQEnY0yRscCRZ3XVlVzdXEff4AQT8XliswvW4zDGFBULHD7oDAXpH4wRuWAVVcaY4mOBwwfhUCNvn5vizZFJABs1bowpKhY4fBAOBUkovNCfnENrqw3+M8YUEQscPgi3Jyur/q5/hOqKAC31toCTMaZ4WODwwVXNddRWBRidnGXrxloCtoCTMaaIWODwQUVA6GxLTnhoN8aNMcXGAodPOkMWOIwxxckCh0/CzghyG8NhjCk2Fjh8EnZ6HBY4jDHFxgKHT967YxO/dcs13L53i99NMcaYrNhc3j6pqazgs3ft9bsZxhiTNetxGGOMyYoFDmOMMVmxwGGMMSYrFjiMMcZkxQKHMcaYrFjgMMYYkxULHMYYY7JigcMYY0xWRFX9boPnRGQEeMfvdqygBRj1uxFrUKztBmu7X6zt/lhP269W1dblG8sicBQyETmqqgf9bke2irXdYG33i7XdH1603VJVxhhjsmKBwxhjTFYscPjva343YI2Ktd1gbfeLtd0fOW+73eMwxhiTFetxGGOMyYoFDmOMMVmxwOEDEdkuIn8nIsdF5HUR+R2/25QtEakQkWMi8oTfbcmGiGwUke+ISJ/z+3+/323KhIh82vlbeU1EHhORWr/bdCUi8nURGRaR11K2NYvIcyJy0vl3k59tTGeFdn/B+Xt5RUS+KyIbfWziitK1PeW5/1lEVERacvFaFjj8sQD8nqruBd4H/LaI7PO5Tdn6HeC4341Ygz8BnlbVMLCfIvgZRKQD+LfAQVW9DqgAPupvq1b1KHDnsm0PA8+r6m7geedxoXmUy9v9HHCdqt4AnAA+m+9GZehRLm87IrIduAN4N1cvZIHDB6o6oKo/c76Pkbx4dfjbqsyJyDbgbuDP/G5LNkSkEbgF+HMAVZ1T1TFfG5W5SmCDiFQCdUDU5/Zckar+PXB+2eZDwDec778BHM5nmzKRrt2q+qyqLjgPfwxsy3vDMrDC7xzgS8DvAzmrhLLA4TMR2QEcAH7ic1Oy8Z9J/iEmfG5Htq4BRoC/cNJsfyYi9X43ajWqGgH+mOQnxgFgXFWf9bdVa9KmqgOQ/PAEbPG5PWvxL4G/9bsRmRKR+4CIqr6cy/Na4PCRiDQAjwO/q6oTfrcnEyJyDzCsqi/53ZY1qARuBL6qqgeAKQozXXIJ517AIWAnsBWoF5Ff97dV5UdE/oBkmvmbfrclEyJSB/wB8L/n+twWOHwiIlUkg8Y3VfVv/G5PFm4G7hORt4FvAR8Qkf/qb5MydhY4q6pu7+47JANJofsg8JaqjqjqPPA3wC/43Ka1GBKRdgDn32Gf25MxEfk4cA/wa1o8g992kfyw8bLzft0G/ExEQus9sQUOH4iIkMyzH1fV/+R3e7Khqp9V1W2quoPkDdofqGpRfPpV1UHgjIh0OptuB97wsUmZehd4n4jUOX87t1MEN/XTOAJ83Pn+40CPj23JmIjcCfw74D5Vnfa7PZlS1VdVdYuq7nDer2eBG533wbpY4PDHzcDHSH5a73W+7vK7UWXiU8A3ReQVoAv4Q3+bszqnh/Qd4GfAqyTftwU9BYaIPAb8I9ApImdF5F8BnwfuEJGTJKt8Pu9nG9NZod1fBoLAc8579RFfG7mCFdruzWsVT6/LGGNMIbAehzHGmKxY4DDGGJMVCxzGGGOyYoHDGGNMVixwGGOMyYoFDmPWQER2pJuFtNDOaYwXLHAYY4zJigUOY9ZJRK5xJk38uWXb/yp1YKeIPCoiv+L0LP5BRH7mfF02fYiI/IaIfDnl8RMi8svO9x8SkX90jv1rZ84zY/LGAocx6+BMX/I48AlV/emyp78F/Atnv2qSU4U8RXKOpjtU9Ubn+f+Sxeu1AP8r8EHn+KPA/7Ten8OYbFT63QBjilgryfmWfkVVX0/z/N8C/0VEakgusPP3qjojIk3Al0WkC1gE9mTxmu8D9gEvJqetoprkNBPG5I0FDmPWbhw4Q3LuscsCh6rGReSHwIdJ9iwec576NDBEcgXCABBPc+4FLs0IuEvFCvCcqj6Qg/YbsyaWqjJm7eZIrmL3P4jIr66wz7eATwC/BDzjbGsCBlQ1QXKyy4o0x70NdIlIwFn68yZn+4+Bm0XkWkiuuSAi2fRYjFk3CxzGrIOqTpFcp+HTInIozS7Pklyu9vuqOuds+7+Bj4vIj0mmqabSHPci8BbJ2XD/mOTMuKjqCPAbwGPODL8/BsI5+4GMyYDNjmuMMSYr1uMwxhiTFQscxhhjsmKBwxhjTFYscBhjjMmKBQ5jjDFZscBhjDEmKxY4jDHGZOX/B4tx+d2MTjCxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,15),score_list)\n",
    "plt.xlabel(\"k value\")\n",
    "plt.ylabel(\"accuracy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, there are some values that is better than the other k values"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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