DefaultCreditCard-SVM-Statquest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Begin of Default Credit Card Classifier with SVM from StatQuest YT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing libs that we need"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "from sklearn.utils import resample\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Open and read the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Open and read the datasets form source\n",
    "\n",
    "df = pd.read_csv('../../datasets/default-credit-card/default_of_credit_card_clients.csv', \n",
    "                 header=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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       "      <th>PAY_4</th>\n",
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       "      <th>PAY_AMT6</th>\n",
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       "      <td>3455</td>\n",
       "      <td>3261</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
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       "      <td>2000</td>\n",
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       "      <td>1000</td>\n",
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       "   ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
       "0   1      20000    2          2         1   24      2      2     -1     -1   \n",
       "1   2     120000    2          2         2   26     -1      2      0      0   \n",
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       "3   4      50000    2          2         1   37      0      0      0      0   \n",
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       "\n",
       "   ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  PAY_AMT3  \\\n",
       "0  ...          0          0          0         0       689         0   \n",
       "1  ...       3272       3455       3261         0      1000      1000   \n",
       "2  ...      14331      14948      15549      1518      1500      1000   \n",
       "3  ...      28314      28959      29547      2000      2019      1200   \n",
       "4  ...      20940      19146      19131      2000     36681     10000   \n",
       "\n",
       "   PAY_AMT4  PAY_AMT5  PAY_AMT6  default payment next month  \n",
       "0         0         0         0                           1  \n",
       "1      1000         0      2000                           1  \n",
       "2      1000      1000      5000                           0  \n",
       "3      1100      1069      1000                           0  \n",
       "4      9000       689       679                           0  \n",
       "\n",
       "[5 rows x 25 columns]"
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     "execution_count": 21,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>-1</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>48944</td>\n",
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       "      <th>29999</th>\n",
       "      <td>30000</td>\n",
       "      <td>50000</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
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       "          ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  \\\n",
       "29995  29996     220000    1          3         1   39      0      0      0   \n",
       "29996  29997     150000    1          3         2   43     -1     -1     -1   \n",
       "29997  29998      30000    1          2         2   37      4      3      2   \n",
       "29998  29999      80000    1          3         1   41      1     -1      0   \n",
       "29999  30000      50000    1          2         1   46      0      0      0   \n",
       "\n",
       "       PAY_4  ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  \\\n",
       "29995      0  ...      88004      31237      15980      8500     20000   \n",
       "29996     -1  ...       8979       5190          0      1837      3526   \n",
       "29997     -1  ...      20878      20582      19357         0         0   \n",
       "29998      0  ...      52774      11855      48944     85900      3409   \n",
       "29999      0  ...      36535      32428      15313      2078      1800   \n",
       "\n",
       "       PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  default payment next month  \n",
       "29995      5003      3047      5000      1000                           0  \n",
       "29996      8998       129         0         0                           0  \n",
       "29997     22000      4200      2000      3100                           1  \n",
       "29998      1178      1926     52964      1804                           1  \n",
       "29999      1430      1000      1000      1000                           1  \n",
       "\n",
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     "execution_count": 22,
     "metadata": {},
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   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Start modifying the raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "      <td>15549</td>\n",
       "      <td>1518</td>\n",
       "      <td>1500</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>50000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>28314</td>\n",
       "      <td>28959</td>\n",
       "      <td>29547</td>\n",
       "      <td>2000</td>\n",
       "      <td>2019</td>\n",
       "      <td>1200</td>\n",
       "      <td>1100</td>\n",
       "      <td>1069</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>50000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20940</td>\n",
       "      <td>19146</td>\n",
       "      <td>19131</td>\n",
       "      <td>2000</td>\n",
       "      <td>36681</td>\n",
       "      <td>10000</td>\n",
       "      <td>9000</td>\n",
       "      <td>689</td>\n",
       "      <td>679</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
       "0   1      20000    2          2         1   24      2      2     -1     -1   \n",
       "1   2     120000    2          2         2   26     -1      2      0      0   \n",
       "2   3      90000    2          2         2   34      0      0      0      0   \n",
       "3   4      50000    2          2         1   37      0      0      0      0   \n",
       "4   5      50000    1          2         1   57     -1      0     -1      0   \n",
       "\n",
       "   ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  PAY_AMT3  \\\n",
       "0  ...          0          0          0         0       689         0   \n",
       "1  ...       3272       3455       3261         0      1000      1000   \n",
       "2  ...      14331      14948      15549      1518      1500      1000   \n",
       "3  ...      28314      28959      29547      2000      2019      1200   \n",
       "4  ...      20940      19146      19131      2000     36681     10000   \n",
       "\n",
       "   PAY_AMT4  PAY_AMT5  PAY_AMT6  DEFAULT  \n",
       "0         0         0         0        1  \n",
       "1      1000         0      2000        1  \n",
       "2      1000      1000      5000        0  \n",
       "3      1100      1069      1000        0  \n",
       "4      9000       689       679        0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Rename the 'default payment next month' column into 'default' column for easier usage\n",
    "\n",
    "df.rename({'default payment next month':'DEFAULT'}, axis='columns', inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>DEFAULT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>689</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3272</td>\n",
       "      <td>3455</td>\n",
       "      <td>3261</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>14331</td>\n",
       "      <td>14948</td>\n",
       "      <td>15549</td>\n",
       "      <td>1518</td>\n",
       "      <td>1500</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>28314</td>\n",
       "      <td>28959</td>\n",
       "      <td>29547</td>\n",
       "      <td>2000</td>\n",
       "      <td>2019</td>\n",
       "      <td>1200</td>\n",
       "      <td>1100</td>\n",
       "      <td>1069</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20940</td>\n",
       "      <td>19146</td>\n",
       "      <td>19131</td>\n",
       "      <td>2000</td>\n",
       "      <td>36681</td>\n",
       "      <td>10000</td>\n",
       "      <td>9000</td>\n",
       "      <td>689</td>\n",
       "      <td>679</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
       "0      20000    2          2         1   24      2      2     -1     -1   \n",
       "1     120000    2          2         2   26     -1      2      0      0   \n",
       "2      90000    2          2         2   34      0      0      0      0   \n",
       "3      50000    2          2         1   37      0      0      0      0   \n",
       "4      50000    1          2         1   57     -1      0     -1      0   \n",
       "\n",
       "   PAY_5  ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  PAY_AMT3  \\\n",
       "0     -2  ...          0          0          0         0       689         0   \n",
       "1      0  ...       3272       3455       3261         0      1000      1000   \n",
       "2      0  ...      14331      14948      15549      1518      1500      1000   \n",
       "3      0  ...      28314      28959      29547      2000      2019      1200   \n",
       "4      0  ...      20940      19146      19131      2000     36681     10000   \n",
       "\n",
       "   PAY_AMT4  PAY_AMT5  PAY_AMT6  DEFAULT  \n",
       "0         0         0         0        1  \n",
       "1      1000         0      2000        1  \n",
       "2      1000      1000      5000        0  \n",
       "3      1100      1069      1000        0  \n",
       "4      9000       689       679        0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## We don't need the 'ID' column, so we drop the 'ID' column from our dataFrame\n",
    "\n",
    "df.drop('ID', axis=1, inplace=True) ## 0=rows ; 1=columns ; This is the alternative from using 'columns' or 'rows'\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LIMIT_BAL    int64\n",
       "SEX          int64\n",
       "EDUCATION    int64\n",
       "MARRIAGE     int64\n",
       "AGE          int64\n",
       "PAY_0        int64\n",
       "PAY_2        int64\n",
       "PAY_3        int64\n",
       "PAY_4        int64\n",
       "PAY_5        int64\n",
       "PAY_6        int64\n",
       "BILL_AMT1    int64\n",
       "BILL_AMT2    int64\n",
       "BILL_AMT3    int64\n",
       "BILL_AMT4    int64\n",
       "BILL_AMT5    int64\n",
       "BILL_AMT6    int64\n",
       "PAY_AMT1     int64\n",
       "PAY_AMT2     int64\n",
       "PAY_AMT3     int64\n",
       "PAY_AMT4     int64\n",
       "PAY_AMT5     int64\n",
       "PAY_AMT6     int64\n",
       "DEFAULT      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check each column dataType\n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1], dtype=int64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SEX'].unique() ## cuma ambil data yg unik, gasemua diprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 3, 5, 4, 6, 0], dtype=int64)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['EDUCATION'].unique() ## cuma ambil data yg unik, gasemua diprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 0], dtype=int64)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MARRIAGE'].unique() ## cuma ambil data yg unik, gasemua diprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.loc[(df['EDUCATION'] == 0) | (df['MARRIAGE'] == 0)]) ## cek panjang(jumlah) data yg punya value 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df) ## cek panjang(jumlah) keseluruhan data mentah"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "## ambil data berdasarkan label\n",
    "## ini hanya ambil yg edukasi sama marriage != 0, yg 0 gadiambil, masukin ke variabel baru\n",
    "\n",
    "df_no_missing = df.loc[(df['EDUCATION'] != 0) & (df['MARRIAGE'] != 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29932"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_no_missing) ## cek panjang data yang kolom edukasi dan menikah udah difilter di atas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 3, 5, 4, 6], dtype=int64)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_no_missing['EDUCATION'].unique() ## cek nilai unik dari kolom edukasi (hanya tampil 1 kali)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3], dtype=int64)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_no_missing['MARRIAGE'].unique() ## cek nilai unik dari kolom menikah (hanya tampil 1 kali)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DOWNSAMPLE THE DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29932"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## check the len of the dataFrame with no missing value anymore\n",
    "len(df_no_missing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "## split the data into 2\n",
    "## one with the columns default value == 0\n",
    "## and another one with the columns default value == 1\n",
    "df_not_default = df_no_missing[df_no_missing['DEFAULT'] == 0]\n",
    "df_default = df_no_missing[df_no_missing['DEFAULT'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## downsampling the data with resample func from pandas\n",
    "## not replacing the data\n",
    "## number of samples is 1000\n",
    "## random state = so the randomness is set\n",
    "\n",
    "df_not_default_downsampled = resample(df_not_default,\n",
    "                                      replace=False,\n",
    "                                      n_samples=1000,\n",
    "                                      random_state=42)\n",
    "len(df_not_default_downsampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## downsampling the data with resample func from pandas\n",
    "## not replacing the data\n",
    "## number of samples is 1000\n",
    "## random state = so the randomness is set\n",
    "\n",
    "df_default_downsampled = resample(df_default,\n",
    "                                  replace=False,\n",
    "                                  n_samples=1000,\n",
    "                                  random_state=42)\n",
    "len(df_default_downsampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## merge again the default and not default data that has been downsampled into 1000 data each\n",
    "## in total now we have 2000 data! From the beginning of 29932 data\n",
    "\n",
    "df_downsample = pd.concat([df_not_default_downsampled, df_default_downsampled])\n",
    "len(df_downsample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Format Data Part 1\n",
    "We have taken care of the missing data, so now we are ready to start formatting the data for making our Support Vector Machine (SVM)\n",
    "\n",
    "### We need two steps.\n",
    "\n",
    "First, split the data into two parts\n",
    "1. Columns data that we need to make our classifications (X) capital\n",
    "2. Columns data that we need to make our predicts (y) not-capital\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>130000</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>...</td>\n",
       "      <td>50000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2500</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4678</th>\n",
       "      <td>170000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>172307</td>\n",
       "      <td>35234</td>\n",
       "      <td>32869</td>\n",
       "      <td>33862</td>\n",
       "      <td>7200</td>\n",
       "      <td>7500</td>\n",
       "      <td>1200</td>\n",
       "      <td>1200</td>\n",
       "      <td>1500</td>\n",
       "      <td>1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16004</th>\n",
       "      <td>180000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>26310</td>\n",
       "      <td>26662</td>\n",
       "      <td>26166</td>\n",
       "      <td>26176</td>\n",
       "      <td>1800</td>\n",
       "      <td>1800</td>\n",
       "      <td>1500</td>\n",
       "      <td>1056</td>\n",
       "      <td>950</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22974</th>\n",
       "      <td>210000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>...</td>\n",
       "      <td>410</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>979</td>\n",
       "      <td>412</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17535</th>\n",
       "      <td>190000</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>80548</td>\n",
       "      <td>81778</td>\n",
       "      <td>83082</td>\n",
       "      <td>84811</td>\n",
       "      <td>3300</td>\n",
       "      <td>3331</td>\n",
       "      <td>3359</td>\n",
       "      <td>2663</td>\n",
       "      <td>2751</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
       "641       130000    2          3         1   28      0      0      0      0   \n",
       "4678      170000    1          3         1   29      0      0      0      0   \n",
       "16004     180000    2          2         1   29      0      0      0      0   \n",
       "22974     210000    2          2         2   32     -2     -2     -2     -2   \n",
       "17535     190000    2          3         1   45      0      0      0      0   \n",
       "\n",
       "       PAY_5  ...  BILL_AMT3  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  \\\n",
       "641       -2  ...      50000          0          0          0      2500   \n",
       "4678       0  ...     172307      35234      32869      33862      7200   \n",
       "16004      0  ...      26310      26662      26166      26176      1800   \n",
       "22974     -2  ...        410          0          0          0       979   \n",
       "17535      0  ...      80548      81778      83082      84811      3300   \n",
       "\n",
       "       PAY_AMT2  PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  \n",
       "641        1000         0         0         0         0  \n",
       "4678       7500      1200      1200      1500      1300  \n",
       "16004      1800      1500      1056       950      1000  \n",
       "22974       412         0         0         0         0  \n",
       "17535      3331      3359      2663      2751      3000  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## We now make a copy from df_downsample for our classification column (X)\n",
    "\n",
    "X = df_downsample.drop(['DEFAULT'], axis=1).copy() ## 1 == 'columns'\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "641      0\n",
       "4678     0\n",
       "16004    0\n",
       "22974    0\n",
       "17535    0\n",
       "Name: DEFAULT, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We now get the 'DEFAULT' column from df_downsample for our predictions (y)\n",
    "\n",
    "y = df_downsample['DEFAULT'].copy()\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Format Data Part 2\n",
    "We are now have our classification columns (X) and our prediction column (y)\n",
    "\n",
    "Now because the 'EDUCATION', 'MARRIAGE', etc. columns are categorical and not have the value that boolean (just 0 and 1), we have to tranform that categorical data into boolean! (just 0 and 1). How we do that?\n",
    "\n",
    "One-Hot Encoding! or dummy encoding. That's the trick's name.\n",
    "\n",
    "We transfom that categorical value in one columns into the columns itself. Ex. MARRIAGE column have 3 values [0,1,2], so the One-Hot Encoding will transform that one column with 3 values into 3 columns with 2 values, only 0 and 1 (boolean). We do this because most of the algorithms are not understand the categorical data and just understand the boolean, which means they just understand 0 and 1, so we make a multi-column to solve that!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
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       "      <th>AGE</th>\n",
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       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>MARRIAGE_1</th>\n",
       "      <th>MARRIAGE_2</th>\n",
       "      <th>MARRIAGE_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>4678</th>\n",
       "      <td>170000</td>\n",
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       "      <td>29</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>33862</td>\n",
       "      <td>7200</td>\n",
       "      <td>7500</td>\n",
       "      <td>1200</td>\n",
       "      <td>1200</td>\n",
       "      <td>1500</td>\n",
       "      <td>1300</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>16004</th>\n",
       "      <td>180000</td>\n",
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       "      <td>29</td>\n",
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       "      <td>...</td>\n",
       "      <td>26176</td>\n",
       "      <td>1800</td>\n",
       "      <td>1800</td>\n",
       "      <td>1500</td>\n",
       "      <td>1056</td>\n",
       "      <td>950</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22974</th>\n",
       "      <td>210000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>979</td>\n",
       "      <td>412</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>17535</th>\n",
       "      <td>190000</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>84811</td>\n",
       "      <td>3300</td>\n",
       "      <td>3331</td>\n",
       "      <td>3359</td>\n",
       "      <td>2663</td>\n",
       "      <td>2751</td>\n",
       "      <td>3000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LIMIT_BAL  SEX  EDUCATION  AGE  PAY_0  PAY_2  PAY_3  PAY_4  PAY_5  \\\n",
       "641       130000    2          3   28      0      0      0      0     -2   \n",
       "4678      170000    1          3   29      0      0      0      0      0   \n",
       "16004     180000    2          2   29      0      0      0      0      0   \n",
       "22974     210000    2          2   32     -2     -2     -2     -2     -2   \n",
       "17535     190000    2          3   45      0      0      0      0      0   \n",
       "\n",
       "       PAY_6  ...  BILL_AMT6  PAY_AMT1  PAY_AMT2  PAY_AMT3  PAY_AMT4  \\\n",
       "641       -2  ...          0      2500      1000         0         0   \n",
       "4678       0  ...      33862      7200      7500      1200      1200   \n",
       "16004      0  ...      26176      1800      1800      1500      1056   \n",
       "22974     -2  ...          0       979       412         0         0   \n",
       "17535      0  ...      84811      3300      3331      3359      2663   \n",
       "\n",
       "       PAY_AMT5  PAY_AMT6  MARRIAGE_1  MARRIAGE_2  MARRIAGE_3  \n",
       "641           0         0           1           0           0  \n",
       "4678       1500      1300           1           0           0  \n",
       "16004       950      1000           1           0           0  \n",
       "22974         0         0           0           1           0  \n",
       "17535      2751      3000           1           0           0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Visualisizing the One-Hot Encoder with get_dummies() from pandas\n",
    "## Now we have split the 'MARRIAGE' into 3 columns with the value only 0 and 1.\n",
    "\n",
    "X_encoded = pd.get_dummies(X, columns=['MARRIAGE'])\n",
    "X_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
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       "      <td>210000</td>\n",
       "      <td>32</td>\n",
       "      <td>355</td>\n",
       "      <td>975</td>\n",
       "      <td>410</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>979</td>\n",
       "      <td>412</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17535</th>\n",
       "      <td>190000</td>\n",
       "      <td>45</td>\n",
       "      <td>76433</td>\n",
       "      <td>78472</td>\n",
       "      <td>80548</td>\n",
       "      <td>81778</td>\n",
       "      <td>83082</td>\n",
       "      <td>84811</td>\n",
       "      <td>3300</td>\n",
       "      <td>3331</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LIMIT_BAL  AGE  BILL_AMT1  BILL_AMT2  BILL_AMT3  BILL_AMT4  BILL_AMT5  \\\n",
       "641       130000   28     100143      50456      50000          0          0   \n",
       "4678      170000   29     165027     168990     172307      35234      32869   \n",
       "16004     180000   29      25781      26000      26310      26662      26166   \n",
       "22974     210000   32        355        975        410          0          0   \n",
       "17535     190000   45      76433      78472      80548      81778      83082   \n",
       "\n",
       "       BILL_AMT6  PAY_AMT1  PAY_AMT2  ...  PAY_5_7  PAY_6_-2  PAY_6_-1  \\\n",
       "641            0      2500      1000  ...        0         1         0   \n",
       "4678       33862      7200      7500  ...        0         0         0   \n",
       "16004      26176      1800      1800  ...        0         0         0   \n",
       "22974          0       979       412  ...        0         1         0   \n",
       "17535      84811      3300      3331  ...        0         0         0   \n",
       "\n",
       "       PAY_6_0  PAY_6_2  PAY_6_3  PAY_6_4  PAY_6_5  PAY_6_6  PAY_6_7  \n",
       "641          0        0        0        0        0        0        0  \n",
       "4678         1        0        0        0        0        0        0  \n",
       "16004        1        0        0        0        0        0        0  \n",
       "22974        0        0        0        0        0        0        0  \n",
       "17535        1        0        0        0        0        0        0  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_encoded = pd.get_dummies(X, columns=['SEX',\n",
    "                                       'EDUCATION',\n",
    "                                       'MARRIAGE',\n",
    "                                       'PAY_0',\n",
    "                                       'PAY_2',\n",
    "                                       'PAY_3',\n",
    "                                       'PAY_4',\n",
    "                                       'PAY_5',\n",
    "                                       'PAY_6',])\n",
    "X_encoded.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Format Data Part 3\n",
    "\n",
    "We are now going into the SVM thing!\n",
    "So, we are using Radial Basis Function (RBF) from the Support Vector Machine (SVM). Because the RBF of SVM assumes that our data are scaled an centered, in other words, our data should have a mean value = 0 and a standard deviation value = 1. So, we need to do this to our training dataset and testing dataset.\n",
    "\n",
    "First thing first, we have to split our data, so wewill scale and centering the data independently to avoid ___Data Leakage___."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Because we have done One-Hot Encoding before, the X variable is X_encoded\n",
    "## and the predict label (y) still the same\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, random_state=42)\n",
    "X_train_scaled = scale(X_train)\n",
    "X_test_scaled = scale(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM TIME!\n",
    "\n",
    "Now, we have done manipulating, preparing, and formatting our data dor Support Vector Machine. So, let's do it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(random_state=42)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Train the SVC instances in cld_svm with our training dataset, with fit() function\n",
    "\n",
    "clf_svm = SVC(kernel='rbf', random_state=42)\n",
    "clf_svm.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x69d9d90>"
      ]
     },
     "execution_count": 81,
     "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": [
    "## Visualisizing the test data\n",
    "\n",
    "plot_confusion_matrix(clf_svm,\n",
    "                     X_test_scaled,\n",
    "                     y_test,\n",
    "                     values_format='d',\n",
    "                     display_labels=[\"Did not default\", \"Defaulted\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizing the Parameters\n",
    "\n",
    "So, the output of the classification is not that gewd. We have to optimize something, that is the parameters. How to do that?\n",
    "\n",
    "\n",
    "Okay, ___Cross-Validation (CV)___ come to the rescue!\n",
    "\n",
    "Optimizng the SVM is all about finding the best value for the ___gamma___, and, potentially, the ___regularization parameter, C___.\n",
    "\n",
    "We will use ___GridSearchCV()___, because we have two params to optimize. We specify bunch of potential values for the ___gamma___ and ___C___, and ___GridSearchCV()___ will tests all possible combinations. How cool is that, huh?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 100, 'gamma': 0.001, 'kernel': 'rbf'}\n"
     ]
    }
   ],
   "source": [
    "param_grid = [\n",
    "    {'C': [0.5, 1, 10, 100], # NOTE: C > 0\n",
    "     'gamma': ['scale', 1, 0.1, 0.01, 0.001, 0.0001],\n",
    "     'kernel': ['rbf', 'linear', 'poly']},\n",
    "]\n",
    "\n",
    "optimal_params = GridSearchCV(SVC(),\n",
    "                             param_grid,\n",
    "                             cv=5, # n of folds cv\n",
    "                             scoring=\"accuracy\",\n",
    "                             verbose=0) # if we want to see what GridSearch is doin, set verbose=2\n",
    "optimal_params.fit(X_train_scaled, y_train)\n",
    "print(optimal_params.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Playing with the real SVM now! (After we've done the optimizing part)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100, gamma=0.001, random_state=42)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## We train again the training dataset with our new SVC.\n",
    "## Now, we provide the params that we get from cross-validating the params to get the best one possible\n",
    "\n",
    "clf_svm = SVC(random_state=42, gamma=0.001, C=100, kernel='rbf')\n",
    "clf_svm.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1eaea718>"
      ]
     },
     "execution_count": 90,
     "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": [
    "plot_confusion_matrix(clf_svm,\n",
    "                     X_test_scaled,\n",
    "                     y_test,\n",
    "                     values_format='d',\n",
    "                     display_labels=['Did not default', 'Defaulted'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we cannot draw 24 dimensions (or infinite dimensions becase we use this beautiful rbf) so we have to collapse the data into two dimensions so we can draw the output.\n",
    "\n",
    "We will use Principal Component Analysis (PCA) to shrink the dimensions into 2. Ehhmm, well, i have to read and find out more about PCA later on, cheers!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_downsample.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "pca = PCA()\n",
    "X_train_pca = pca.fit_transform(X_train_scaled)\n",
    "\n",
    "per_var = np.round(pca.explained_variance_ratio_* 100, decimals=1)\n",
    "labels = [str(x) for x in range(1, len(per_var)+1)]\n",
    "\n",
    "plt.bar(x=range(1, len(per_var)+1), height=per_var)\n",
    "plt.tick_params(axis='x',\n",
    "                which='both',\n",
    "                bottom=False,\n",
    "                top=False,\n",
    "                labelbottom=False)\n",
    "plt.ylabel('Percentage of explained variance')\n",
    "plt.xlabel('Principal components')\n",
    "plt.title('Scree plot')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "## The first 2 columns should taller than the others, that means the 2 columns can accurately reflect the original data\n",
    "##But, we don't see that in our Scree plot below, so, it's kinda useless"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}