SVM Practice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cec09de4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor as vif\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55e4f638",
   "metadata": {},
   "source": [
    "# SVM Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "12d84243",
   "metadata": {},
   "outputs": [],
   "source": [
    "data1=pd.read_csv('dementia_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "20961410",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Subject ID</th>\n",
       "      <th>MRI ID</th>\n",
       "      <th>Group</th>\n",
       "      <th>Visit</th>\n",
       "      <th>MR Delay</th>\n",
       "      <th>M/F</th>\n",
       "      <th>Hand</th>\n",
       "      <th>Age</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>SES</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>CDR</th>\n",
       "      <th>eTIV</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OAS2_0001</td>\n",
       "      <td>OAS2_0001_MR1</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>87</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OAS2_0001</td>\n",
       "      <td>OAS2_0001_MR2</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>2</td>\n",
       "      <td>457</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>88</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR1</td>\n",
       "      <td>Demented</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>75</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1678</td>\n",
       "      <td>0.736</td>\n",
       "      <td>1.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR2</td>\n",
       "      <td>Demented</td>\n",
       "      <td>2</td>\n",
       "      <td>560</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>76</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1738</td>\n",
       "      <td>0.713</td>\n",
       "      <td>1.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR3</td>\n",
       "      <td>Demented</td>\n",
       "      <td>3</td>\n",
       "      <td>1895</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>80</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1698</td>\n",
       "      <td>0.701</td>\n",
       "      <td>1.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>OAS2_0185</td>\n",
       "      <td>OAS2_0185_MR2</td>\n",
       "      <td>Demented</td>\n",
       "      <td>2</td>\n",
       "      <td>842</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>82</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1693</td>\n",
       "      <td>0.694</td>\n",
       "      <td>1.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>OAS2_0185</td>\n",
       "      <td>OAS2_0185_MR3</td>\n",
       "      <td>Demented</td>\n",
       "      <td>3</td>\n",
       "      <td>2297</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1688</td>\n",
       "      <td>0.675</td>\n",
       "      <td>1.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>OAS2_0186</td>\n",
       "      <td>OAS2_0186_MR1</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>R</td>\n",
       "      <td>61</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.801</td>\n",
       "      <td>1.331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>OAS2_0186</td>\n",
       "      <td>OAS2_0186_MR2</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>2</td>\n",
       "      <td>763</td>\n",
       "      <td>F</td>\n",
       "      <td>R</td>\n",
       "      <td>63</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1327</td>\n",
       "      <td>0.796</td>\n",
       "      <td>1.323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>OAS2_0186</td>\n",
       "      <td>OAS2_0186_MR3</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>3</td>\n",
       "      <td>1608</td>\n",
       "      <td>F</td>\n",
       "      <td>R</td>\n",
       "      <td>65</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1333</td>\n",
       "      <td>0.801</td>\n",
       "      <td>1.317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>373 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Subject ID         MRI ID        Group  Visit  MR Delay M/F Hand  Age  \\\n",
       "0    OAS2_0001  OAS2_0001_MR1  Nondemented      1         0   M    R   87   \n",
       "1    OAS2_0001  OAS2_0001_MR2  Nondemented      2       457   M    R   88   \n",
       "2    OAS2_0002  OAS2_0002_MR1     Demented      1         0   M    R   75   \n",
       "3    OAS2_0002  OAS2_0002_MR2     Demented      2       560   M    R   76   \n",
       "4    OAS2_0002  OAS2_0002_MR3     Demented      3      1895   M    R   80   \n",
       "..         ...            ...          ...    ...       ...  ..  ...  ...   \n",
       "368  OAS2_0185  OAS2_0185_MR2     Demented      2       842   M    R   82   \n",
       "369  OAS2_0185  OAS2_0185_MR3     Demented      3      2297   M    R   86   \n",
       "370  OAS2_0186  OAS2_0186_MR1  Nondemented      1         0   F    R   61   \n",
       "371  OAS2_0186  OAS2_0186_MR2  Nondemented      2       763   F    R   63   \n",
       "372  OAS2_0186  OAS2_0186_MR3  Nondemented      3      1608   F    R   65   \n",
       "\n",
       "     EDUC  SES  MMSE  CDR  eTIV   nWBV    ASF  \n",
       "0      14  2.0  27.0  0.0  1987  0.696  0.883  \n",
       "1      14  2.0  30.0  0.0  2004  0.681  0.876  \n",
       "2      12  NaN  23.0  0.5  1678  0.736  1.046  \n",
       "3      12  NaN  28.0  0.5  1738  0.713  1.010  \n",
       "4      12  NaN  22.0  0.5  1698  0.701  1.034  \n",
       "..    ...  ...   ...  ...   ...    ...    ...  \n",
       "368    16  1.0  28.0  0.5  1693  0.694  1.037  \n",
       "369    16  1.0  26.0  0.5  1688  0.675  1.040  \n",
       "370    13  2.0  30.0  0.0  1319  0.801  1.331  \n",
       "371    13  2.0  30.0  0.0  1327  0.796  1.323  \n",
       "372    13  2.0  30.0  0.0  1333  0.801  1.317  \n",
       "\n",
       "[373 rows x 15 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "4296006d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 373 entries, 0 to 372\n",
      "Data columns (total 15 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Subject ID  373 non-null    object \n",
      " 1   MRI ID      373 non-null    object \n",
      " 2   Group       373 non-null    object \n",
      " 3   Visit       373 non-null    int64  \n",
      " 4   MR Delay    373 non-null    int64  \n",
      " 5   M/F         373 non-null    object \n",
      " 6   Hand        373 non-null    object \n",
      " 7   Age         373 non-null    int64  \n",
      " 8   EDUC        373 non-null    int64  \n",
      " 9   SES         354 non-null    float64\n",
      " 10  MMSE        371 non-null    float64\n",
      " 11  CDR         373 non-null    float64\n",
      " 12  eTIV        373 non-null    int64  \n",
      " 13  nWBV        373 non-null    float64\n",
      " 14  ASF         373 non-null    float64\n",
      "dtypes: float64(5), int64(5), object(5)\n",
      "memory usage: 43.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "781a65be",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Subject ID</th>\n",
       "      <th>MRI ID</th>\n",
       "      <th>Group</th>\n",
       "      <th>Visit</th>\n",
       "      <th>MR Delay</th>\n",
       "      <th>M/F</th>\n",
       "      <th>Hand</th>\n",
       "      <th>Age</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>SES</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>CDR</th>\n",
       "      <th>eTIV</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OAS2_0001</td>\n",
       "      <td>OAS2_0001_MR1</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>87</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OAS2_0001</td>\n",
       "      <td>OAS2_0001_MR2</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>2</td>\n",
       "      <td>457</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>88</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>OAS2_0004</td>\n",
       "      <td>OAS2_0004_MR1</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>R</td>\n",
       "      <td>88</td>\n",
       "      <td>18</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1215</td>\n",
       "      <td>0.710</td>\n",
       "      <td>1.444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>OAS2_0004</td>\n",
       "      <td>OAS2_0004_MR2</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>2</td>\n",
       "      <td>538</td>\n",
       "      <td>F</td>\n",
       "      <td>R</td>\n",
       "      <td>90</td>\n",
       "      <td>18</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1200</td>\n",
       "      <td>0.718</td>\n",
       "      <td>1.462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>OAS2_0005</td>\n",
       "      <td>OAS2_0005_MR1</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>80</td>\n",
       "      <td>12</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1689</td>\n",
       "      <td>0.712</td>\n",
       "      <td>1.039</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",
       "      <td>...</td>\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>368</th>\n",
       "      <td>OAS2_0185</td>\n",
       "      <td>OAS2_0185_MR2</td>\n",
       "      <td>Demented</td>\n",
       "      <td>2</td>\n",
       "      <td>842</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>82</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1693</td>\n",
       "      <td>0.694</td>\n",
       "      <td>1.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>OAS2_0185</td>\n",
       "      <td>OAS2_0185_MR3</td>\n",
       "      <td>Demented</td>\n",
       "      <td>3</td>\n",
       "      <td>2297</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1688</td>\n",
       "      <td>0.675</td>\n",
       "      <td>1.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>OAS2_0186</td>\n",
       "      <td>OAS2_0186_MR1</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>R</td>\n",
       "      <td>61</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.801</td>\n",
       "      <td>1.331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>OAS2_0186</td>\n",
       "      <td>OAS2_0186_MR2</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>2</td>\n",
       "      <td>763</td>\n",
       "      <td>F</td>\n",
       "      <td>R</td>\n",
       "      <td>63</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1327</td>\n",
       "      <td>0.796</td>\n",
       "      <td>1.323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>OAS2_0186</td>\n",
       "      <td>OAS2_0186_MR3</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>3</td>\n",
       "      <td>1608</td>\n",
       "      <td>F</td>\n",
       "      <td>R</td>\n",
       "      <td>65</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1333</td>\n",
       "      <td>0.801</td>\n",
       "      <td>1.317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Subject ID         MRI ID        Group  Visit  MR Delay M/F Hand  Age  \\\n",
       "0    OAS2_0001  OAS2_0001_MR1  Nondemented      1         0   M    R   87   \n",
       "1    OAS2_0001  OAS2_0001_MR2  Nondemented      2       457   M    R   88   \n",
       "5    OAS2_0004  OAS2_0004_MR1  Nondemented      1         0   F    R   88   \n",
       "6    OAS2_0004  OAS2_0004_MR2  Nondemented      2       538   F    R   90   \n",
       "7    OAS2_0005  OAS2_0005_MR1  Nondemented      1         0   M    R   80   \n",
       "..         ...            ...          ...    ...       ...  ..  ...  ...   \n",
       "368  OAS2_0185  OAS2_0185_MR2     Demented      2       842   M    R   82   \n",
       "369  OAS2_0185  OAS2_0185_MR3     Demented      3      2297   M    R   86   \n",
       "370  OAS2_0186  OAS2_0186_MR1  Nondemented      1         0   F    R   61   \n",
       "371  OAS2_0186  OAS2_0186_MR2  Nondemented      2       763   F    R   63   \n",
       "372  OAS2_0186  OAS2_0186_MR3  Nondemented      3      1608   F    R   65   \n",
       "\n",
       "     EDUC  SES  MMSE  CDR  eTIV   nWBV    ASF  \n",
       "0      14  2.0  27.0  0.0  1987  0.696  0.883  \n",
       "1      14  2.0  30.0  0.0  2004  0.681  0.876  \n",
       "5      18  3.0  28.0  0.0  1215  0.710  1.444  \n",
       "6      18  3.0  27.0  0.0  1200  0.718  1.462  \n",
       "7      12  4.0  28.0  0.0  1689  0.712  1.039  \n",
       "..    ...  ...   ...  ...   ...    ...    ...  \n",
       "368    16  1.0  28.0  0.5  1693  0.694  1.037  \n",
       "369    16  1.0  26.0  0.5  1688  0.675  1.040  \n",
       "370    13  2.0  30.0  0.0  1319  0.801  1.331  \n",
       "371    13  2.0  30.0  0.0  1327  0.796  1.323  \n",
       "372    13  2.0  30.0  0.0  1333  0.801  1.317  \n",
       "\n",
       "[354 rows x 15 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.dropna(inplace=True)\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "9c845e72",
   "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>Visit</th>\n",
       "      <th>MR Delay</th>\n",
       "      <th>Age</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>SES</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>CDR</th>\n",
       "      <th>eTIV</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>354.000000</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>354.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.884181</td>\n",
       "      <td>601.353107</td>\n",
       "      <td>77.033898</td>\n",
       "      <td>14.703390</td>\n",
       "      <td>2.460452</td>\n",
       "      <td>27.409605</td>\n",
       "      <td>0.271186</td>\n",
       "      <td>1489.991525</td>\n",
       "      <td>0.729879</td>\n",
       "      <td>1.193780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.925330</td>\n",
       "      <td>640.596081</td>\n",
       "      <td>7.811808</td>\n",
       "      <td>2.895662</td>\n",
       "      <td>1.134005</td>\n",
       "      <td>3.712626</td>\n",
       "      <td>0.370537</td>\n",
       "      <td>175.768462</td>\n",
       "      <td>0.037842</td>\n",
       "      <td>0.136821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1106.000000</td>\n",
       "      <td>0.644000</td>\n",
       "      <td>0.876000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1358.250000</td>\n",
       "      <td>0.699000</td>\n",
       "      <td>1.100250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>559.500000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1470.500000</td>\n",
       "      <td>0.729000</td>\n",
       "      <td>1.193500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>882.500000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>16.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1595.250000</td>\n",
       "      <td>0.757000</td>\n",
       "      <td>1.292500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>2639.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>0.837000</td>\n",
       "      <td>1.587000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Visit     MR Delay         Age        EDUC         SES  \\\n",
       "count  354.000000   354.000000  354.000000  354.000000  354.000000   \n",
       "mean     1.884181   601.353107   77.033898   14.703390    2.460452   \n",
       "std      0.925330   640.596081    7.811808    2.895662    1.134005   \n",
       "min      1.000000     0.000000   60.000000    6.000000    1.000000   \n",
       "25%      1.000000     0.000000   71.000000   12.000000    2.000000   \n",
       "50%      2.000000   559.500000   77.000000   15.000000    2.000000   \n",
       "75%      2.000000   882.500000   82.000000   16.750000    3.000000   \n",
       "max      5.000000  2639.000000   98.000000   23.000000    5.000000   \n",
       "\n",
       "             MMSE         CDR         eTIV        nWBV         ASF  \n",
       "count  354.000000  354.000000   354.000000  354.000000  354.000000  \n",
       "mean    27.409605    0.271186  1489.991525    0.729879    1.193780  \n",
       "std      3.712626    0.370537   175.768462    0.037842    0.136821  \n",
       "min      4.000000    0.000000  1106.000000    0.644000    0.876000  \n",
       "25%     27.000000    0.000000  1358.250000    0.699000    1.100250  \n",
       "50%     29.000000    0.000000  1470.500000    0.729000    1.193500  \n",
       "75%     30.000000    0.500000  1595.250000    0.757000    1.292500  \n",
       "max     30.000000    2.000000  2004.000000    0.837000    1.587000  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "3143ad51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['R'], dtype=object)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Hand'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ee0c9dcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Nondemented', 'Demented', 'Converted'], dtype=object)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.Group.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "f8454e4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Group'>"
      ]
     },
     "execution_count": 85,
     "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": [
    "data1.groupby(by='Group').count().Visit.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "6aab00d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fahmimn21/miniforge3/envs/tf/lib/python3.8/site-packages/pandas/core/frame.py:4906: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().drop(\n",
      "/var/folders/jn/3zxggwvn0w9fxzbpwtz0q8p80000gn/T/ipykernel_69463/589045375.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['Group']=data1.Group.replace({'Nondemented':0,'Demented':1})\n",
      "/var/folders/jn/3zxggwvn0w9fxzbpwtz0q8p80000gn/T/ipykernel_69463/589045375.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['M/F']=data1['M/F'].replace({'M':1,'F':0})\n"
     ]
    },
    {
     "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>Group</th>\n",
       "      <th>Visit</th>\n",
       "      <th>MR Delay</th>\n",
       "      <th>M/F</th>\n",
       "      <th>Age</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>SES</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>CDR</th>\n",
       "      <th>eTIV</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>87</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>457</td>\n",
       "      <td>1</td>\n",
       "      <td>88</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>18</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1215</td>\n",
       "      <td>0.710</td>\n",
       "      <td>1.444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>538</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>18</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1200</td>\n",
       "      <td>0.718</td>\n",
       "      <td>1.462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>12</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1689</td>\n",
       "      <td>0.712</td>\n",
       "      <td>1.039</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>842</td>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1693</td>\n",
       "      <td>0.694</td>\n",
       "      <td>1.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2297</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1688</td>\n",
       "      <td>0.675</td>\n",
       "      <td>1.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.801</td>\n",
       "      <td>1.331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>763</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1327</td>\n",
       "      <td>0.796</td>\n",
       "      <td>1.323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1608</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1333</td>\n",
       "      <td>0.801</td>\n",
       "      <td>1.317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>317 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Group  Visit  MR Delay  M/F  Age  EDUC  SES  MMSE  CDR  eTIV   nWBV  \\\n",
       "0        0      1         0    1   87    14  2.0  27.0  0.0  1987  0.696   \n",
       "1        0      2       457    1   88    14  2.0  30.0  0.0  2004  0.681   \n",
       "5        0      1         0    0   88    18  3.0  28.0  0.0  1215  0.710   \n",
       "6        0      2       538    0   90    18  3.0  27.0  0.0  1200  0.718   \n",
       "7        0      1         0    1   80    12  4.0  28.0  0.0  1689  0.712   \n",
       "..     ...    ...       ...  ...  ...   ...  ...   ...  ...   ...    ...   \n",
       "368      1      2       842    1   82    16  1.0  28.0  0.5  1693  0.694   \n",
       "369      1      3      2297    1   86    16  1.0  26.0  0.5  1688  0.675   \n",
       "370      0      1         0    0   61    13  2.0  30.0  0.0  1319  0.801   \n",
       "371      0      2       763    0   63    13  2.0  30.0  0.0  1327  0.796   \n",
       "372      0      3      1608    0   65    13  2.0  30.0  0.0  1333  0.801   \n",
       "\n",
       "       ASF  \n",
       "0    0.883  \n",
       "1    0.876  \n",
       "5    1.444  \n",
       "6    1.462  \n",
       "7    1.039  \n",
       "..     ...  \n",
       "368  1.037  \n",
       "369  1.040  \n",
       "370  1.331  \n",
       "371  1.323  \n",
       "372  1.317  \n",
       "\n",
       "[317 rows x 12 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1=data1[data1.Group!='Converted']\n",
    "data1.drop(columns=['Subject ID','MRI ID','Hand'],inplace=True)\n",
    "data1['Group']=data1.Group.replace({'Nondemented':0,'Demented':1})\n",
    "data1['M/F']=data1['M/F'].replace({'M':1,'F':0})\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "8a7fda86",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data1.drop(columns='Group')\n",
    "y=data1.Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b8591f7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X.boxplot(figsize=(16,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "9049e515",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X[X['MR Delay']<=2100].boxplot(figsize=(16,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "62cd7c0a",
   "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>Visit</th>\n",
       "      <th>MR Delay</th>\n",
       "      <th>M/F</th>\n",
       "      <th>Age</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>SES</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>CDR</th>\n",
       "      <th>eTIV</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>87</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>457</td>\n",
       "      <td>1</td>\n",
       "      <td>88</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>18</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1215</td>\n",
       "      <td>0.710</td>\n",
       "      <td>1.444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>538</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>18</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1200</td>\n",
       "      <td>0.718</td>\n",
       "      <td>1.462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>12</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1689</td>\n",
       "      <td>0.712</td>\n",
       "      <td>1.039</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1704</td>\n",
       "      <td>0.711</td>\n",
       "      <td>1.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>2</td>\n",
       "      <td>842</td>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1693</td>\n",
       "      <td>0.694</td>\n",
       "      <td>1.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.801</td>\n",
       "      <td>1.331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>2</td>\n",
       "      <td>763</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1327</td>\n",
       "      <td>0.796</td>\n",
       "      <td>1.323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>3</td>\n",
       "      <td>1608</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1333</td>\n",
       "      <td>0.801</td>\n",
       "      <td>1.317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>308 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Visit  MR Delay  M/F  Age  EDUC  SES  MMSE  CDR  eTIV   nWBV    ASF\n",
       "0        1         0    1   87    14  2.0  27.0  0.0  1987  0.696  0.883\n",
       "1        2       457    1   88    14  2.0  30.0  0.0  2004  0.681  0.876\n",
       "5        1         0    0   88    18  3.0  28.0  0.0  1215  0.710  1.444\n",
       "6        2       538    0   90    18  3.0  27.0  0.0  1200  0.718  1.462\n",
       "7        1         0    1   80    12  4.0  28.0  0.0  1689  0.712  1.039\n",
       "..     ...       ...  ...  ...   ...  ...   ...  ...   ...    ...    ...\n",
       "367      1         0    1   80    16  1.0  28.0  0.5  1704  0.711  1.030\n",
       "368      2       842    1   82    16  1.0  28.0  0.5  1693  0.694  1.037\n",
       "370      1         0    0   61    13  2.0  30.0  0.0  1319  0.801  1.331\n",
       "371      2       763    0   63    13  2.0  30.0  0.0  1327  0.796  1.323\n",
       "372      3      1608    0   65    13  2.0  30.0  0.0  1333  0.801  1.317\n",
       "\n",
       "[308 rows x 11 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=X[X['MR Delay']<=2100]\n",
    "y=y.loc[X.index]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "7bcc22b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "5      0\n",
       "6      0\n",
       "7      0\n",
       "      ..\n",
       "367    1\n",
       "368    1\n",
       "370    0\n",
       "371    0\n",
       "372    0\n",
       "Name: Group, Length: 308, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "b66d933d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.98195801, -0.96091916,  1.15470054, ...,  2.74979325,\n",
       "        -0.90418022, -2.21400864],\n",
       "       [ 0.25250349, -0.13259151,  1.15470054, ...,  2.84418416,\n",
       "        -1.29759824, -2.26396942],\n",
       "       [-0.98195801, -0.96091916, -0.8660254 , ..., -1.53666455,\n",
       "        -0.53699006,  1.78999087],\n",
       "       ...,\n",
       "       [-0.98195801, -0.96091916, -0.8660254 , ..., -0.95921427,\n",
       "         1.84974597,  0.98348116],\n",
       "       [ 0.25250349,  0.42204364, -0.8660254 , ..., -0.91479502,\n",
       "         1.71860663,  0.92638313],\n",
       "       [ 1.48696499,  1.95363415, -0.8660254 , ..., -0.88148058,\n",
       "         1.84974597,  0.88355961]])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler=StandardScaler()\n",
    "X_std=scaler.fit_transform(X)\n",
    "X_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "2ecdc489",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X_std,y,test_size=0.3,random_state=50,stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "34c43d54",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "model=SVC(kernel='linear',C=0.5)\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "y_pred=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "34bbcdc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1,\n",
       "       1, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "1a657e0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print(accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "01981711",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5941558441558441\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fahmimn21/miniforge3/envs/tf/lib/python3.8/site-packages/sklearn/base.py:434: UserWarning: X has feature names, but SVC was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "#Kernel=rbf dan C=0.5\n",
    "model=SVC(kernel='rbf',C=0.5)\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "y_pred=model.predict(X)\n",
    "print(accuracy_score(y,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "e17fd6f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6038961038961039\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fahmimn21/miniforge3/envs/tf/lib/python3.8/site-packages/sklearn/base.py:434: UserWarning: X has feature names, but SVC was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "#Kernel=linear dan C=0.5\n",
    "model=SVC(kernel='linear',C=0.5)\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "y_pred=model.predict(X)\n",
    "print(accuracy_score(y,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "a4ccc628",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9545454545454546\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fahmimn21/miniforge3/envs/tf/lib/python3.8/site-packages/sklearn/base.py:434: UserWarning: X has feature names, but SVC was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "#Kernel=poly dan C=0.5\n",
    "model=SVC(kernel='poly',C=0.5, degree=1)\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "y_pred=model.predict(X)\n",
    "print(accuracy_score(y,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dc3d714",
   "metadata": {},
   "source": [
    "# SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "67c6992e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2=pd.read_csv('HousingPrices-Amsterdam-August-2021.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "11af61af",
   "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>Unnamed: 0</th>\n",
       "      <th>Address</th>\n",
       "      <th>Zip</th>\n",
       "      <th>Price</th>\n",
       "      <th>Area</th>\n",
       "      <th>Room</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Blasiusstraat 8 2, Amsterdam</td>\n",
       "      <td>1091 CR</td>\n",
       "      <td>685000.0</td>\n",
       "      <td>64</td>\n",
       "      <td>3</td>\n",
       "      <td>4.907736</td>\n",
       "      <td>52.356157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Kromme Leimuidenstraat 13 H, Amsterdam</td>\n",
       "      <td>1059 EL</td>\n",
       "      <td>475000.0</td>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>4.850476</td>\n",
       "      <td>52.348586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Zaaiersweg 11 A, Amsterdam</td>\n",
       "      <td>1097 SM</td>\n",
       "      <td>850000.0</td>\n",
       "      <td>109</td>\n",
       "      <td>4</td>\n",
       "      <td>4.944774</td>\n",
       "      <td>52.343782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Tenerifestraat 40, Amsterdam</td>\n",
       "      <td>1060 TH</td>\n",
       "      <td>580000.0</td>\n",
       "      <td>128</td>\n",
       "      <td>6</td>\n",
       "      <td>4.789928</td>\n",
       "      <td>52.343712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Winterjanpad 21, Amsterdam</td>\n",
       "      <td>1036 KN</td>\n",
       "      <td>720000.0</td>\n",
       "      <td>138</td>\n",
       "      <td>5</td>\n",
       "      <td>4.902503</td>\n",
       "      <td>52.410538</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>920</td>\n",
       "      <td>Ringdijk, Amsterdam</td>\n",
       "      <td>1097 AE</td>\n",
       "      <td>750000.0</td>\n",
       "      <td>117</td>\n",
       "      <td>1</td>\n",
       "      <td>4.927757</td>\n",
       "      <td>52.354173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920</th>\n",
       "      <td>921</td>\n",
       "      <td>Kleine Beerstraat 31, Amsterdam</td>\n",
       "      <td>1033 CP</td>\n",
       "      <td>350000.0</td>\n",
       "      <td>72</td>\n",
       "      <td>3</td>\n",
       "      <td>4.890612</td>\n",
       "      <td>52.414587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>921</th>\n",
       "      <td>922</td>\n",
       "      <td>Stuyvesantstraat 33 II, Amsterdam</td>\n",
       "      <td>1058 AK</td>\n",
       "      <td>350000.0</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>4.856935</td>\n",
       "      <td>52.363256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>923</td>\n",
       "      <td>John Blankensteinstraat 51, Amsterdam</td>\n",
       "      <td>1095 MB</td>\n",
       "      <td>599000.0</td>\n",
       "      <td>113</td>\n",
       "      <td>4</td>\n",
       "      <td>4.965731</td>\n",
       "      <td>52.375268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923</th>\n",
       "      <td>924</td>\n",
       "      <td>S. F. van Ossstraat 334, Amsterdam</td>\n",
       "      <td>1068 JS</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>79</td>\n",
       "      <td>4</td>\n",
       "      <td>4.810678</td>\n",
       "      <td>52.355493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>924 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0                                 Address      Zip     Price  \\\n",
       "0             1            Blasiusstraat 8 2, Amsterdam  1091 CR  685000.0   \n",
       "1             2  Kromme Leimuidenstraat 13 H, Amsterdam  1059 EL  475000.0   \n",
       "2             3              Zaaiersweg 11 A, Amsterdam  1097 SM  850000.0   \n",
       "3             4            Tenerifestraat 40, Amsterdam  1060 TH  580000.0   \n",
       "4             5              Winterjanpad 21, Amsterdam  1036 KN  720000.0   \n",
       "..          ...                                     ...      ...       ...   \n",
       "919         920                     Ringdijk, Amsterdam  1097 AE  750000.0   \n",
       "920         921         Kleine Beerstraat 31, Amsterdam  1033 CP  350000.0   \n",
       "921         922       Stuyvesantstraat 33 II, Amsterdam  1058 AK  350000.0   \n",
       "922         923   John Blankensteinstraat 51, Amsterdam  1095 MB  599000.0   \n",
       "923         924      S. F. van Ossstraat 334, Amsterdam  1068 JS  300000.0   \n",
       "\n",
       "     Area  Room       Lon        Lat  \n",
       "0      64     3  4.907736  52.356157  \n",
       "1      60     3  4.850476  52.348586  \n",
       "2     109     4  4.944774  52.343782  \n",
       "3     128     6  4.789928  52.343712  \n",
       "4     138     5  4.902503  52.410538  \n",
       "..    ...   ...       ...        ...  \n",
       "919   117     1  4.927757  52.354173  \n",
       "920    72     3  4.890612  52.414587  \n",
       "921    51     3  4.856935  52.363256  \n",
       "922   113     4  4.965731  52.375268  \n",
       "923    79     4  4.810678  52.355493  \n",
       "\n",
       "[924 rows x 8 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "87205279",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 924 entries, 0 to 923\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  924 non-null    int64  \n",
      " 1   Address     924 non-null    object \n",
      " 2   Zip         924 non-null    object \n",
      " 3   Price       920 non-null    float64\n",
      " 4   Area        924 non-null    int64  \n",
      " 5   Room        924 non-null    int64  \n",
      " 6   Lon         924 non-null    float64\n",
      " 7   Lat         924 non-null    float64\n",
      "dtypes: float64(3), int64(3), object(2)\n",
      "memory usage: 57.9+ KB\n"
     ]
    }
   ],
   "source": [
    "data2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "0fcb0698",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Price</th>\n",
       "      <th>Area</th>\n",
       "      <th>Room</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Lat</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>924.000000</td>\n",
       "      <td>9.200000e+02</td>\n",
       "      <td>924.000000</td>\n",
       "      <td>924.000000</td>\n",
       "      <td>924.000000</td>\n",
       "      <td>924.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>462.500000</td>\n",
       "      <td>6.220654e+05</td>\n",
       "      <td>95.952381</td>\n",
       "      <td>3.571429</td>\n",
       "      <td>4.888605</td>\n",
       "      <td>52.363326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>266.880123</td>\n",
       "      <td>5.389942e+05</td>\n",
       "      <td>57.447436</td>\n",
       "      <td>1.592332</td>\n",
       "      <td>0.053140</td>\n",
       "      <td>0.024028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.750000e+05</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.644819</td>\n",
       "      <td>52.291519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>231.750000</td>\n",
       "      <td>3.500000e+05</td>\n",
       "      <td>60.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.855834</td>\n",
       "      <td>52.352077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>462.500000</td>\n",
       "      <td>4.670000e+05</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.886818</td>\n",
       "      <td>52.364631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>693.250000</td>\n",
       "      <td>7.000000e+05</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.922337</td>\n",
       "      <td>52.377598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>924.000000</td>\n",
       "      <td>5.950000e+06</td>\n",
       "      <td>623.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>5.029122</td>\n",
       "      <td>52.423805</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0         Price        Area        Room         Lon  \\\n",
       "count  924.000000  9.200000e+02  924.000000  924.000000  924.000000   \n",
       "mean   462.500000  6.220654e+05   95.952381    3.571429    4.888605   \n",
       "std    266.880123  5.389942e+05   57.447436    1.592332    0.053140   \n",
       "min      1.000000  1.750000e+05   21.000000    1.000000    4.644819   \n",
       "25%    231.750000  3.500000e+05   60.750000    3.000000    4.855834   \n",
       "50%    462.500000  4.670000e+05   83.000000    3.000000    4.886818   \n",
       "75%    693.250000  7.000000e+05  113.000000    4.000000    4.922337   \n",
       "max    924.000000  5.950000e+06  623.000000   14.000000    5.029122   \n",
       "\n",
       "              Lat  \n",
       "count  924.000000  \n",
       "mean    52.363326  \n",
       "std      0.024028  \n",
       "min     52.291519  \n",
       "25%     52.352077  \n",
       "50%     52.364631  \n",
       "75%     52.377598  \n",
       "max     52.423805  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "036e8b03",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2.dropna(axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "4c7068d4",
   "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>Unnamed: 0</th>\n",
       "      <th>Address</th>\n",
       "      <th>Zip</th>\n",
       "      <th>Price</th>\n",
       "      <th>Area</th>\n",
       "      <th>Room</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Blasiusstraat 8 2, Amsterdam</td>\n",
       "      <td>1091 CR</td>\n",
       "      <td>685000.0</td>\n",
       "      <td>64</td>\n",
       "      <td>3</td>\n",
       "      <td>4.907736</td>\n",
       "      <td>52.356157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Kromme Leimuidenstraat 13 H, Amsterdam</td>\n",
       "      <td>1059 EL</td>\n",
       "      <td>475000.0</td>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>4.850476</td>\n",
       "      <td>52.348586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Zaaiersweg 11 A, Amsterdam</td>\n",
       "      <td>1097 SM</td>\n",
       "      <td>850000.0</td>\n",
       "      <td>109</td>\n",
       "      <td>4</td>\n",
       "      <td>4.944774</td>\n",
       "      <td>52.343782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Tenerifestraat 40, Amsterdam</td>\n",
       "      <td>1060 TH</td>\n",
       "      <td>580000.0</td>\n",
       "      <td>128</td>\n",
       "      <td>6</td>\n",
       "      <td>4.789928</td>\n",
       "      <td>52.343712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Winterjanpad 21, Amsterdam</td>\n",
       "      <td>1036 KN</td>\n",
       "      <td>720000.0</td>\n",
       "      <td>138</td>\n",
       "      <td>5</td>\n",
       "      <td>4.902503</td>\n",
       "      <td>52.410538</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>920</td>\n",
       "      <td>Ringdijk, Amsterdam</td>\n",
       "      <td>1097 AE</td>\n",
       "      <td>750000.0</td>\n",
       "      <td>117</td>\n",
       "      <td>1</td>\n",
       "      <td>4.927757</td>\n",
       "      <td>52.354173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920</th>\n",
       "      <td>921</td>\n",
       "      <td>Kleine Beerstraat 31, Amsterdam</td>\n",
       "      <td>1033 CP</td>\n",
       "      <td>350000.0</td>\n",
       "      <td>72</td>\n",
       "      <td>3</td>\n",
       "      <td>4.890612</td>\n",
       "      <td>52.414587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>921</th>\n",
       "      <td>922</td>\n",
       "      <td>Stuyvesantstraat 33 II, Amsterdam</td>\n",
       "      <td>1058 AK</td>\n",
       "      <td>350000.0</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>4.856935</td>\n",
       "      <td>52.363256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>923</td>\n",
       "      <td>John Blankensteinstraat 51, Amsterdam</td>\n",
       "      <td>1095 MB</td>\n",
       "      <td>599000.0</td>\n",
       "      <td>113</td>\n",
       "      <td>4</td>\n",
       "      <td>4.965731</td>\n",
       "      <td>52.375268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923</th>\n",
       "      <td>924</td>\n",
       "      <td>S. F. van Ossstraat 334, Amsterdam</td>\n",
       "      <td>1068 JS</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>79</td>\n",
       "      <td>4</td>\n",
       "      <td>4.810678</td>\n",
       "      <td>52.355493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>920 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0                                 Address      Zip     Price  \\\n",
       "0             1            Blasiusstraat 8 2, Amsterdam  1091 CR  685000.0   \n",
       "1             2  Kromme Leimuidenstraat 13 H, Amsterdam  1059 EL  475000.0   \n",
       "2             3              Zaaiersweg 11 A, Amsterdam  1097 SM  850000.0   \n",
       "3             4            Tenerifestraat 40, Amsterdam  1060 TH  580000.0   \n",
       "4             5              Winterjanpad 21, Amsterdam  1036 KN  720000.0   \n",
       "..          ...                                     ...      ...       ...   \n",
       "919         920                     Ringdijk, Amsterdam  1097 AE  750000.0   \n",
       "920         921         Kleine Beerstraat 31, Amsterdam  1033 CP  350000.0   \n",
       "921         922       Stuyvesantstraat 33 II, Amsterdam  1058 AK  350000.0   \n",
       "922         923   John Blankensteinstraat 51, Amsterdam  1095 MB  599000.0   \n",
       "923         924      S. F. van Ossstraat 334, Amsterdam  1068 JS  300000.0   \n",
       "\n",
       "     Area  Room       Lon        Lat  \n",
       "0      64     3  4.907736  52.356157  \n",
       "1      60     3  4.850476  52.348586  \n",
       "2     109     4  4.944774  52.343782  \n",
       "3     128     6  4.789928  52.343712  \n",
       "4     138     5  4.902503  52.410538  \n",
       "..    ...   ...       ...        ...  \n",
       "919   117     1  4.927757  52.354173  \n",
       "920    72     3  4.890612  52.414587  \n",
       "921    51     3  4.856935  52.363256  \n",
       "922   113     4  4.965731  52.375268  \n",
       "923    79     4  4.810678  52.355493  \n",
       "\n",
       "[920 rows x 8 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "a8469255",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data2[['Area','Room','Lon','Lat']]\n",
    "y=data2['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "88c1f4f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 129,
     "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": [
    "Corr=data2.drop(columns=['Unnamed: 0','Address','Zip']).corr()\n",
    "\n",
    "sns.heatmap(Corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "307a0487",
   "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>feature</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Area</td>\n",
       "      <td>11.105288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Room</td>\n",
       "      <td>17.834197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lon</td>\n",
       "      <td>8381.678634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lat</td>\n",
       "      <td>8400.360469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  feature          VIF\n",
       "0    Area    11.105288\n",
       "1    Room    17.834197\n",
       "2     Lon  8381.678634\n",
       "3     Lat  8400.360469"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif_data = pd.DataFrame()\n",
    "vif_data[\"feature\"] = X.columns\n",
    "vif_data[\"VIF\"] = [vif(X.values, i) for i in range(len(X.columns))]\n",
    "vif_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "b411c709",
   "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>feature</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Area</td>\n",
       "      <td>11.047326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Room</td>\n",
       "      <td>17.698256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lon</td>\n",
       "      <td>6.204906</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  feature        VIF\n",
       "0    Area  11.047326\n",
       "1    Room  17.698256\n",
       "2     Lon   6.204906"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1=X.drop(columns='Lat')\n",
    "\n",
    "vif_data = pd.DataFrame()\n",
    "vif_data[\"feature\"] = X1.columns\n",
    "vif_data[\"VIF\"] = [vif(X1.values, i) for i in range(len(X1.columns))]\n",
    "vif_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "c3467ebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler=StandardScaler()\n",
    "X_std=scaler.fit_transform(X1)\n",
    "\n",
    "X_train,X_test,y_train,y_test=train_test_split(X_std,y,test_size=0.3, random_state=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "c4932e21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 247069.11807208098\n",
      "Relative MAE: 4.315617782918445\n",
      "R2 Score: 0.07788846693823903\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.metrics import mean_absolute_error,r2_score\n",
    "\n",
    "model=SVR(kernel='linear',C=100,epsilon=3)\n",
    "model.fit(X_train,y_train)\n",
    "y_pred=model.predict(X_test)\n",
    "\n",
    "mae=mean_absolute_error(y_test,y_pred)\n",
    "range_y=y_test.max()-y_test.min()\n",
    "print('MAE:',mae)\n",
    "print('Relative MAE:',mae*100/range_y)\n",
    "print('R2 Score:',r2_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "0a431b02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 245636.75188219515\n",
      "Relative MAE: 4.29059828615188\n",
      "R2 Score: 0.3306533275801491\n"
     ]
    }
   ],
   "source": [
    "model=SVR(kernel='poly',C=100,epsilon=3,degree=3)\n",
    "model.fit(X_train,y_train)\n",
    "y_pred=model.predict(X_test)\n",
    "\n",
    "mae=mean_absolute_error(y_test,y_pred)\n",
    "range_y=y_test.max()-y_test.min()\n",
    "print('MAE:',mae)\n",
    "print('Relative MAE:',mae*100/range_y)\n",
    "print('R2 Score:',r2_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "794941ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 271762.0213545467\n",
      "Relative MAE: 4.746934870821776\n",
      "R2 Score: -0.05518681334519582\n"
     ]
    }
   ],
   "source": [
    "model=SVR(kernel='rbf',C=0.5,epsilon=1)\n",
    "model.fit(X_train,y_train)\n",
    "y_pred=model.predict(X_test)\n",
    "\n",
    "mae=mean_absolute_error(y_test,y_pred)\n",
    "range_y=y_test.max()-y_test.min()\n",
    "print('MAE:',mae)\n",
    "print('Relative MAE:',mae*100/range_y)\n",
    "print('R2 Score:',r2_score(y_test,y_pred))"
   ]
  }
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