notebook/credit_scoring_data.ipynb

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   "execution_count": 1,
   "id": "09ae6694-cb44-4f56-93f1-dff72c246bec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter the uneccesary warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bb32a777-1a5a-41ba-8013-c03e8ee26905",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import pandas and numpy\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Fix the random seed\n",
    "np.random.seed(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c87b38d0-33b0-4a61-828b-14b1c2f4e26b",
   "metadata": {},
   "source": [
    "# 1. Loading and understanding the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "39d70ad9-eceb-40a6-a99d-cbb3deaf5b2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Load the dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "89137b86-8d27-4f40-ab37-f974eb8e22f9",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Variable Name</td>\n",
       "      <td>Description</td>\n",
       "      <td>Type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SeriousDlqin2yrs</td>\n",
       "      <td>Person experienced 90 days past due delinquenc...</td>\n",
       "      <td>Y/N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RevolvingUtilizationOfUnsecuredLines</td>\n",
       "      <td>Total balance on credit cards and personal lin...</td>\n",
       "      <td>percentage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>age</td>\n",
       "      <td>Age of borrower in years</td>\n",
       "      <td>integer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NumberOfTime30-59DaysPastDueNotWorse</td>\n",
       "      <td>Number of times borrower has been 30-59 days p...</td>\n",
       "      <td>integer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DebtRatio</td>\n",
       "      <td>Monthly debt payments, alimony,living costs di...</td>\n",
       "      <td>percentage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MonthlyIncome</td>\n",
       "      <td>Monthly income</td>\n",
       "      <td>real</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NumberOfOpenCreditLinesAndLoans</td>\n",
       "      <td>Number of Open loans (installment like car loa...</td>\n",
       "      <td>integer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NumberOfTimes90DaysLate</td>\n",
       "      <td>Number of times borrower has been 90 days or m...</td>\n",
       "      <td>integer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NumberRealEstateLoansOrLines</td>\n",
       "      <td>Number of mortgage and real estate loans inclu...</td>\n",
       "      <td>integer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NumberOfTime60-89DaysPastDueNotWorse</td>\n",
       "      <td>Number of times borrower has been 60-89 days p...</td>\n",
       "      <td>integer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NumberOfDependents</td>\n",
       "      <td>Number of dependents in family excluding thems...</td>\n",
       "      <td>integer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                              Unnamed: 0  \\\n",
       "0                          Variable Name   \n",
       "1                       SeriousDlqin2yrs   \n",
       "2   RevolvingUtilizationOfUnsecuredLines   \n",
       "3                                    age   \n",
       "4   NumberOfTime30-59DaysPastDueNotWorse   \n",
       "5                              DebtRatio   \n",
       "6                          MonthlyIncome   \n",
       "7        NumberOfOpenCreditLinesAndLoans   \n",
       "8                NumberOfTimes90DaysLate   \n",
       "9           NumberRealEstateLoansOrLines   \n",
       "10  NumberOfTime60-89DaysPastDueNotWorse   \n",
       "11                    NumberOfDependents   \n",
       "\n",
       "                                           Unnamed: 1  Unnamed: 2  \n",
       "0                                         Description        Type  \n",
       "1   Person experienced 90 days past due delinquenc...         Y/N  \n",
       "2   Total balance on credit cards and personal lin...  percentage  \n",
       "3                            Age of borrower in years     integer  \n",
       "4   Number of times borrower has been 30-59 days p...     integer  \n",
       "5   Monthly debt payments, alimony,living costs di...  percentage  \n",
       "6                                      Monthly income        real  \n",
       "7   Number of Open loans (installment like car loa...     integer  \n",
       "8   Number of times borrower has been 90 days or m...     integer  \n",
       "9   Number of mortgage and real estate loans inclu...     integer  \n",
       "10  Number of times borrower has been 60-89 days p...     integer  \n",
       "11  Number of dependents in family excluding thems...     integer  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = pd.read_excel('./Data Dictionary.xls')\n",
    "columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "079aa447-ef67-41cc-a76e-7978be65026c",
   "metadata": {},
   "source": [
    "## Load the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54f526cf-21ac-45c3-b1f9-2dbf2f95cec5",
   "metadata": {},
   "source": [
    "### The first 10 rows from the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8ffe2b8c-e4d3-4320-aeb2-b8a7918dc953",
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>4.00000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.00000</td>\n",
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       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
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       "      <td>0.754464</td>\n",
       "      <td>0.189169</td>\n",
       "      <td>0.644226</td>\n",
       "      <td>0.018798</td>\n",
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       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>30.00000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>DebtRatio</th>\n",
       "      <td>0.802982</td>\n",
       "      <td>0.121876</td>\n",
       "      <td>0.085113</td>\n",
       "      <td>0.03605</td>\n",
       "      <td>0.024926</td>\n",
       "      <td>0.375607</td>\n",
       "      <td>0.209940</td>\n",
       "      <td>0.606291</td>\n",
       "      <td>0.309476</td>\n",
       "      <td>0.531529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <td>9120.000000</td>\n",
       "      <td>2600.000000</td>\n",
       "      <td>3042.000000</td>\n",
       "      <td>3300.00000</td>\n",
       "      <td>63588.000000</td>\n",
       "      <td>3500.000000</td>\n",
       "      <td>3500.000000</td>\n",
       "      <td>23684.000000</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>6501.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfDependents</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
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      "text/plain": [
       "                                                0            1            2  \\\n",
       "Unnamed: 0                               1.000000     2.000000     3.000000   \n",
       "SeriousDlqin2yrs                         1.000000     0.000000     0.000000   \n",
       "RevolvingUtilizationOfUnsecuredLines     0.766127     0.957151     0.658180   \n",
       "age                                     45.000000    40.000000    38.000000   \n",
       "NumberOfTime30-59DaysPastDueNotWorse     2.000000     0.000000     1.000000   \n",
       "DebtRatio                                0.802982     0.121876     0.085113   \n",
       "MonthlyIncome                         9120.000000  2600.000000  3042.000000   \n",
       "NumberOfOpenCreditLinesAndLoans         13.000000     4.000000     2.000000   \n",
       "NumberOfTimes90DaysLate                  0.000000     0.000000     1.000000   \n",
       "NumberRealEstateLoansOrLines             6.000000     0.000000     0.000000   \n",
       "NumberOfTime60-89DaysPastDueNotWorse     0.000000     0.000000     0.000000   \n",
       "NumberOfDependents                       2.000000     1.000000     0.000000   \n",
       "\n",
       "                                               3             4            5  \\\n",
       "Unnamed: 0                               4.00000      5.000000     6.000000   \n",
       "SeriousDlqin2yrs                         0.00000      0.000000     0.000000   \n",
       "RevolvingUtilizationOfUnsecuredLines     0.23381      0.907239     0.213179   \n",
       "age                                     30.00000     49.000000    74.000000   \n",
       "NumberOfTime30-59DaysPastDueNotWorse     0.00000      1.000000     0.000000   \n",
       "DebtRatio                                0.03605      0.024926     0.375607   \n",
       "MonthlyIncome                         3300.00000  63588.000000  3500.000000   \n",
       "NumberOfOpenCreditLinesAndLoans          5.00000      7.000000     3.000000   \n",
       "NumberOfTimes90DaysLate                  0.00000      0.000000     0.000000   \n",
       "NumberRealEstateLoansOrLines             0.00000      1.000000     1.000000   \n",
       "NumberOfTime60-89DaysPastDueNotWorse     0.00000      0.000000     0.000000   \n",
       "NumberOfDependents                       0.00000      0.000000     1.000000   \n",
       "\n",
       "                                                6             7            8  \\\n",
       "Unnamed: 0                               8.000000     10.000000    11.000000   \n",
       "SeriousDlqin2yrs                         0.000000      0.000000     0.000000   \n",
       "RevolvingUtilizationOfUnsecuredLines     0.754464      0.189169     0.644226   \n",
       "age                                     39.000000     57.000000    30.000000   \n",
       "NumberOfTime30-59DaysPastDueNotWorse     0.000000      0.000000     0.000000   \n",
       "DebtRatio                                0.209940      0.606291     0.309476   \n",
       "MonthlyIncome                         3500.000000  23684.000000  2500.000000   \n",
       "NumberOfOpenCreditLinesAndLoans          8.000000      9.000000     5.000000   \n",
       "NumberOfTimes90DaysLate                  0.000000      0.000000     0.000000   \n",
       "NumberRealEstateLoansOrLines             0.000000      4.000000     0.000000   \n",
       "NumberOfTime60-89DaysPastDueNotWorse     0.000000      0.000000     0.000000   \n",
       "NumberOfDependents                       0.000000      2.000000     0.000000   \n",
       "\n",
       "                                                9  \n",
       "Unnamed: 0                              12.000000  \n",
       "SeriousDlqin2yrs                         0.000000  \n",
       "RevolvingUtilizationOfUnsecuredLines     0.018798  \n",
       "age                                     51.000000  \n",
       "NumberOfTime30-59DaysPastDueNotWorse     0.000000  \n",
       "DebtRatio                                0.531529  \n",
       "MonthlyIncome                         6501.000000  \n",
       "NumberOfOpenCreditLinesAndLoans          7.000000  \n",
       "NumberOfTimes90DaysLate                  0.000000  \n",
       "NumberRealEstateLoansOrLines             2.000000  \n",
       "NumberOfTime60-89DaysPastDueNotWorse     0.000000  \n",
       "NumberOfDependents                       2.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"sample_data.csv\")\n",
    "data.head(10).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9251cc93-cf76-4074-9641-7ca6b4d593a6",
   "metadata": {},
   "source": [
    "### The sample function randomly samples 10 rows from the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ecafb913-897f-455c-80cf-294a75877f27",
   "metadata": {},
   "outputs": [
    {
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       "      <td>50352.000000</td>\n",
       "      <td>149078.000000</td>\n",
       "      <td>25025.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
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       "    <tr>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
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       "      <td>0.023977</td>\n",
       "      <td>0.491959</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000453</td>\n",
       "      <td>0.058108</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>37.00000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>50.00000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DebtRatio</th>\n",
       "      <td>0.362659</td>\n",
       "      <td>0.704918</td>\n",
       "      <td>0.313948</td>\n",
       "      <td>0.30288</td>\n",
       "      <td>0.678491</td>\n",
       "      <td>0.653061</td>\n",
       "      <td>0.12802</td>\n",
       "      <td>0.214443</td>\n",
       "      <td>0.409190</td>\n",
       "      <td>0.494584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <td>4000.000000</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>5833.00000</td>\n",
       "      <td>3100.000000</td>\n",
       "      <td>5732.000000</td>\n",
       "      <td>5256.00000</td>\n",
       "      <td>65000.000000</td>\n",
       "      <td>15559.000000</td>\n",
       "      <td>6000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfDependents</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             101863        19008   \\\n",
       "Unnamed: 0                            127060.000000  23774.000000   \n",
       "SeriousDlqin2yrs                           0.000000      1.000000   \n",
       "RevolvingUtilizationOfUnsecuredLines       0.018762      0.639319   \n",
       "age                                       40.000000     63.000000   \n",
       "NumberOfTime30-59DaysPastDueNotWorse       0.000000      0.000000   \n",
       "DebtRatio                                  0.362659      0.704918   \n",
       "MonthlyIncome                           4000.000000   2500.000000   \n",
       "NumberOfOpenCreditLinesAndLoans            6.000000      8.000000   \n",
       "NumberOfTimes90DaysLate                    0.000000      0.000000   \n",
       "NumberRealEstateLoansOrLines               2.000000      0.000000   \n",
       "NumberOfTime60-89DaysPastDueNotWorse       0.000000      0.000000   \n",
       "NumberOfDependents                         0.000000      0.000000   \n",
       "\n",
       "                                             117997        99895   \\\n",
       "Unnamed: 0                            147171.000000  124606.00000   \n",
       "SeriousDlqin2yrs                           0.000000       1.00000   \n",
       "RevolvingUtilizationOfUnsecuredLines       0.407724       1.00000   \n",
       "age                                       69.000000      37.00000   \n",
       "NumberOfTime30-59DaysPastDueNotWorse       0.000000       0.00000   \n",
       "DebtRatio                                  0.313948       0.30288   \n",
       "MonthlyIncome                           2200.000000    5833.00000   \n",
       "NumberOfOpenCreditLinesAndLoans            7.000000       5.00000   \n",
       "NumberOfTimes90DaysLate                    0.000000       0.00000   \n",
       "NumberRealEstateLoansOrLines               0.000000       0.00000   \n",
       "NumberOfTime60-89DaysPastDueNotWorse       0.000000       0.00000   \n",
       "NumberOfDependents                         0.000000       2.00000   \n",
       "\n",
       "                                            45042          114519  \\\n",
       "Unnamed: 0                            56218.000000  142826.000000   \n",
       "SeriousDlqin2yrs                          0.000000       0.000000   \n",
       "RevolvingUtilizationOfUnsecuredLines      0.023977       0.491959   \n",
       "age                                      36.000000      45.000000   \n",
       "NumberOfTime30-59DaysPastDueNotWorse      0.000000       0.000000   \n",
       "DebtRatio                                 0.678491       0.653061   \n",
       "MonthlyIncome                          3100.000000    5732.000000   \n",
       "NumberOfOpenCreditLinesAndLoans           4.000000      13.000000   \n",
       "NumberOfTimes90DaysLate                   0.000000       0.000000   \n",
       "NumberRealEstateLoansOrLines              1.000000       1.000000   \n",
       "NumberOfTime60-89DaysPastDueNotWorse      0.000000       0.000000   \n",
       "NumberOfDependents                        1.000000       1.000000   \n",
       "\n",
       "                                           59952         40287   \\\n",
       "Unnamed: 0                            74749.00000  50352.000000   \n",
       "SeriousDlqin2yrs                          1.00000      0.000000   \n",
       "RevolvingUtilizationOfUnsecuredLines      1.00000      0.000453   \n",
       "age                                      50.00000     58.000000   \n",
       "NumberOfTime30-59DaysPastDueNotWorse      2.00000      2.000000   \n",
       "DebtRatio                                 0.12802      0.214443   \n",
       "MonthlyIncome                          5256.00000  65000.000000   \n",
       "NumberOfOpenCreditLinesAndLoans           1.00000      8.000000   \n",
       "NumberOfTimes90DaysLate                   0.00000      0.000000   \n",
       "NumberRealEstateLoansOrLines              0.00000      1.000000   \n",
       "NumberOfTime60-89DaysPastDueNotWorse      1.00000      0.000000   \n",
       "NumberOfDependents                        0.00000      0.000000   \n",
       "\n",
       "                                             119531        20013   \n",
       "Unnamed: 0                            149078.000000  25025.000000  \n",
       "SeriousDlqin2yrs                           0.000000      0.000000  \n",
       "RevolvingUtilizationOfUnsecuredLines       0.058108      0.000000  \n",
       "age                                       63.000000     43.000000  \n",
       "NumberOfTime30-59DaysPastDueNotWorse       0.000000      0.000000  \n",
       "DebtRatio                                  0.409190      0.494584  \n",
       "MonthlyIncome                          15559.000000   6000.000000  \n",
       "NumberOfOpenCreditLinesAndLoans           20.000000      7.000000  \n",
       "NumberOfTimes90DaysLate                    0.000000      0.000000  \n",
       "NumberRealEstateLoansOrLines               3.000000      2.000000  \n",
       "NumberOfTime60-89DaysPastDueNotWorse       0.000000      0.000000  \n",
       "NumberOfDependents                         0.000000      0.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sample(10).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcd20975-5d02-4467-b1df-9187c8f632c6",
   "metadata": {},
   "source": [
    "### Data dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a50282fe-88f0-49f0-b0d3-76ac51ab450c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120269, 12)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dd2a4231-8d0c-4462-a47a-e95468056042",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Data columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "58eca2af-c009-41fc-9439-64961bd1f806",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'SeriousDlqin2yrs',\n",
       "       'RevolvingUtilizationOfUnsecuredLines', 'age',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfOpenCreditLinesAndLoans', 'NumberOfTimes90DaysLate',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberOfDependents'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86532402-d676-43e2-a05a-1ff32a533870",
   "metadata": {},
   "source": [
    "## Find out how many unique labels/classes are there in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1998b832-fdea-404c-9968-afa46231f21d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  1.,  0.,  3.,  4.,  5.,  6.,  8.,  7., 20., 10.,  9., 13.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['NumberOfDependents'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "78d7b05d-74be-455b-9ef2-12bdb046adb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Wondering who has 20 dependents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "30fbe730-1696-4ece-85df-f709fc37b70f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5075</th>\n",
       "      <td>6300</td>\n",
       "      <td>0</td>\n",
       "      <td>0.226616</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0.576539</td>\n",
       "      <td>6316.0</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0  SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "5075        6300                 0                              0.226616   40   \n",
       "\n",
       "      NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "5075                                     1   0.576539         6316.0   \n",
       "\n",
       "      NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "5075                               11                        2   \n",
       "\n",
       "      NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "5075                             1                                     0   \n",
       "\n",
       "      NumberOfDependents  \n",
       "5075                20.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data['NumberOfDependents']>=20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5f4e8510-04a8-4110-94ce-5fd5d84d5a4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the Counter class\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6990833-993a-4ac9-b100-1d4ccbeeaad2",
   "metadata": {},
   "source": [
    "## See how the different classes[age] in the dataset are distributed below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "43c398ef-7e3f-4298-89d3-c7bb8125088f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(49, 3217),\n",
       " (48, 3163),\n",
       " (47, 3134),\n",
       " (46, 3117),\n",
       " (50, 3105),\n",
       " (51, 3001),\n",
       " (53, 2974),\n",
       " (45, 2969),\n",
       " (52, 2951),\n",
       " (54, 2905),\n",
       " (56, 2846),\n",
       " (44, 2820),\n",
       " (63, 2802),\n",
       " (43, 2751),\n",
       " (55, 2745),\n",
       " (62, 2725),\n",
       " (61, 2713),\n",
       " (58, 2702),\n",
       " (57, 2699),\n",
       " (41, 2660),\n",
       " (40, 2645),\n",
       " (42, 2633),\n",
       " (59, 2574),\n",
       " (39, 2566),\n",
       " (60, 2505),\n",
       " (64, 2278),\n",
       " (38, 2266),\n",
       " (37, 2157),\n",
       " (36, 2056),\n",
       " (33, 1957),\n",
       " (35, 1930),\n",
       " (65, 1922),\n",
       " (66, 1874),\n",
       " (34, 1867),\n",
       " (67, 1835),\n",
       " (32, 1804),\n",
       " (31, 1783),\n",
       " (30, 1674),\n",
       " (68, 1613),\n",
       " (29, 1468),\n",
       " (69, 1443),\n",
       " (28, 1349),\n",
       " (70, 1270),\n",
       " (72, 1226),\n",
       " (71, 1212),\n",
       " (27, 1126),\n",
       " (73, 1093),\n",
       " (74, 1059),\n",
       " (26, 1012),\n",
       " (75, 860),\n",
       " (76, 839),\n",
       " (25, 797),\n",
       " (77, 775),\n",
       " (78, 749),\n",
       " (79, 697),\n",
       " (24, 652),\n",
       " (80, 621),\n",
       " (81, 530),\n",
       " (23, 515),\n",
       " (82, 443),\n",
       " (22, 329),\n",
       " (83, 317),\n",
       " (85, 309),\n",
       " (84, 304),\n",
       " (86, 254),\n",
       " (87, 211),\n",
       " (88, 193),\n",
       " (89, 178),\n",
       " (21, 130),\n",
       " (90, 114),\n",
       " (91, 83),\n",
       " (92, 55),\n",
       " (93, 48),\n",
       " (94, 21),\n",
       " (95, 20),\n",
       " (97, 10),\n",
       " (96, 6),\n",
       " (98, 3),\n",
       " (99, 3),\n",
       " (103, 2),\n",
       " (102, 2),\n",
       " (101, 2),\n",
       " (0, 1)]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes = Counter(data[\"age\"].values)\n",
    "classes.most_common()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "94a3a01f-0bc2-4cd8-b993-63d129120244",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Num_Observations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49</td>\n",
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       "      <th>3</th>\n",
       "      <td>46</td>\n",
       "      <td>3117</td>\n",
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       "      <th>4</th>\n",
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       "    <tr>\n",
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       "      <th>78</th>\n",
       "      <td>99</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>102</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>101</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>83 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Num_Observations\n",
       "0    49              3217\n",
       "1    48              3163\n",
       "2    47              3134\n",
       "3    46              3117\n",
       "4    50              3105\n",
       "..  ...               ...\n",
       "78   99                 3\n",
       "79  103                 2\n",
       "80  102                 2\n",
       "81  101                 2\n",
       "82    0                 1\n",
       "\n",
       "[83 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_dist = pd.DataFrame(classes.most_common(), columns=['Age', 'Num_Observations'])\n",
    "class_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a1b35655-8e00-4879-8e40-66e749c40b70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                              0\n",
       "SeriousDlqin2yrs                        0\n",
       "RevolvingUtilizationOfUnsecuredLines    0\n",
       "age                                     0\n",
       "NumberOfTime30-59DaysPastDueNotWorse    0\n",
       "DebtRatio                               0\n",
       "MonthlyIncome                           0\n",
       "NumberOfOpenCreditLinesAndLoans         0\n",
       "NumberOfTimes90DaysLate                 0\n",
       "NumberRealEstateLoansOrLines            0\n",
       "NumberOfTime60-89DaysPastDueNotWorse    0\n",
       "NumberOfDependents                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b7f65450-64a4-4c17-af9d-96b051158ed9",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>120269.0</td>\n",
       "      <td>75026.012514</td>\n",
       "      <td>43286.029117</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37678.000000</td>\n",
       "      <td>74969.000000</td>\n",
       "      <td>112494.000000</td>\n",
       "      <td>150000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <td>120269.0</td>\n",
       "      <td>0.069486</td>\n",
       "      <td>0.254280</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <td>120269.0</td>\n",
       "      <td>5.899873</td>\n",
       "      <td>257.040685</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.035084</td>\n",
       "      <td>0.177282</td>\n",
       "      <td>0.579428</td>\n",
       "      <td>50708.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>120269.0</td>\n",
       "      <td>51.289792</td>\n",
       "      <td>14.426684</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <td>120269.0</td>\n",
       "      <td>0.381769</td>\n",
       "      <td>3.499234</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DebtRatio</th>\n",
       "      <td>120269.0</td>\n",
       "      <td>26.598777</td>\n",
       "      <td>424.446457</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.143388</td>\n",
       "      <td>0.296023</td>\n",
       "      <td>0.482559</td>\n",
       "      <td>61106.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <td>120269.0</td>\n",
       "      <td>6670.221237</td>\n",
       "      <td>14384.674215</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3400.000000</td>\n",
       "      <td>5400.000000</td>\n",
       "      <td>8249.000000</td>\n",
       "      <td>3008750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <td>120269.0</td>\n",
       "      <td>8.758475</td>\n",
       "      <td>5.172835</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <td>120269.0</td>\n",
       "      <td>0.211925</td>\n",
       "      <td>3.465276</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <td>120269.0</td>\n",
       "      <td>1.054519</td>\n",
       "      <td>1.149273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <td>120269.0</td>\n",
       "      <td>0.187829</td>\n",
       "      <td>3.447901</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfDependents</th>\n",
       "      <td>120269.0</td>\n",
       "      <td>0.851832</td>\n",
       "      <td>1.148391</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         count          mean           std  \\\n",
       "Unnamed: 0                            120269.0  75026.012514  43286.029117   \n",
       "SeriousDlqin2yrs                      120269.0      0.069486      0.254280   \n",
       "RevolvingUtilizationOfUnsecuredLines  120269.0      5.899873    257.040685   \n",
       "age                                   120269.0     51.289792     14.426684   \n",
       "NumberOfTime30-59DaysPastDueNotWorse  120269.0      0.381769      3.499234   \n",
       "DebtRatio                             120269.0     26.598777    424.446457   \n",
       "MonthlyIncome                         120269.0   6670.221237  14384.674215   \n",
       "NumberOfOpenCreditLinesAndLoans       120269.0      8.758475      5.172835   \n",
       "NumberOfTimes90DaysLate               120269.0      0.211925      3.465276   \n",
       "NumberRealEstateLoansOrLines          120269.0      1.054519      1.149273   \n",
       "NumberOfTime60-89DaysPastDueNotWorse  120269.0      0.187829      3.447901   \n",
       "NumberOfDependents                    120269.0      0.851832      1.148391   \n",
       "\n",
       "                                      min           25%           50%  \\\n",
       "Unnamed: 0                            1.0  37678.000000  74969.000000   \n",
       "SeriousDlqin2yrs                      0.0      0.000000      0.000000   \n",
       "RevolvingUtilizationOfUnsecuredLines  0.0      0.035084      0.177282   \n",
       "age                                   0.0     40.000000     51.000000   \n",
       "NumberOfTime30-59DaysPastDueNotWorse  0.0      0.000000      0.000000   \n",
       "DebtRatio                             0.0      0.143388      0.296023   \n",
       "MonthlyIncome                         0.0   3400.000000   5400.000000   \n",
       "NumberOfOpenCreditLinesAndLoans       0.0      5.000000      8.000000   \n",
       "NumberOfTimes90DaysLate               0.0      0.000000      0.000000   \n",
       "NumberRealEstateLoansOrLines          0.0      0.000000      1.000000   \n",
       "NumberOfTime60-89DaysPastDueNotWorse  0.0      0.000000      0.000000   \n",
       "NumberOfDependents                    0.0      0.000000      0.000000   \n",
       "\n",
       "                                                75%        max  \n",
       "Unnamed: 0                            112494.000000   150000.0  \n",
       "SeriousDlqin2yrs                           0.000000        1.0  \n",
       "RevolvingUtilizationOfUnsecuredLines       0.579428    50708.0  \n",
       "age                                       61.000000      103.0  \n",
       "NumberOfTime30-59DaysPastDueNotWorse       0.000000       98.0  \n",
       "DebtRatio                                  0.482559    61106.5  \n",
       "MonthlyIncome                           8249.000000  3008750.0  \n",
       "NumberOfOpenCreditLinesAndLoans           11.000000       58.0  \n",
       "NumberOfTimes90DaysLate                    0.000000       98.0  \n",
       "NumberRealEstateLoansOrLines               2.000000       54.0  \n",
       "NumberOfTime60-89DaysPastDueNotWorse       0.000000       98.0  \n",
       "NumberOfDependents                         2.000000       20.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1675c812-a560-4875-97ee-8fb22801372b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 120269 entries, 0 to 120268\n",
      "Data columns (total 12 columns):\n",
      " #   Column                                Non-Null Count   Dtype  \n",
      "---  ------                                --------------   -----  \n",
      " 0   Unnamed: 0                            120269 non-null  int64  \n",
      " 1   SeriousDlqin2yrs                      120269 non-null  int64  \n",
      " 2   RevolvingUtilizationOfUnsecuredLines  120269 non-null  float64\n",
      " 3   age                                   120269 non-null  int64  \n",
      " 4   NumberOfTime30-59DaysPastDueNotWorse  120269 non-null  int64  \n",
      " 5   DebtRatio                             120269 non-null  float64\n",
      " 6   MonthlyIncome                         120269 non-null  float64\n",
      " 7   NumberOfOpenCreditLinesAndLoans       120269 non-null  int64  \n",
      " 8   NumberOfTimes90DaysLate               120269 non-null  int64  \n",
      " 9   NumberRealEstateLoansOrLines          120269 non-null  int64  \n",
      " 10  NumberOfTime60-89DaysPastDueNotWorse  120269 non-null  int64  \n",
      " 11  NumberOfDependents                    120269 non-null  float64\n",
      "dtypes: float64(4), int64(8)\n",
      "memory usage: 11.0 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c1a46319-40bc-43f8-8456-ffb132ca45ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import seaborn\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f2ae026e-0fe5-4ef7-a547-c7e56573ab6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='RevolvingUtilizationOfUnsecuredLines'>"
      ]
     },
     "execution_count": 20,
     "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": [
    "sns.boxplot(data=data, x='RevolvingUtilizationOfUnsecuredLines')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f571db43-c0ed-4007-b133-a1e62bcacb0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Density'>"
      ]
     },
     "execution_count": 21,
     "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": [
    "sns.distplot(data.age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "455a27f5-a900-498c-b6a0-31db05611a03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DebtRatio', ylabel='Density'>"
      ]
     },
     "execution_count": 22,
     "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": [
    "sns.distplot(data.DebtRatio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c9f64675-0f3d-4629-a0ff-ad03d8a7feb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11c8a80a0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x = 'age', y = 'MonthlyIncome', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3762375e-d632-4978-a0e6-d2b62f696e94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11d6337f0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x = 'MonthlyIncome', y = 'DebtRatio', data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b5e4217-5c29-4a30-b73e-d01569b2944b",
   "metadata": {},
   "source": [
    "## Senario 1, Assuming total balance of credit and debt has 50% creteria to \"pass\" assessment as a good score, for example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "eee9c6d9-6d21-4b01-9232-c201783855c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d510da18-f7c6-43a8-bd8e-eb3233fc9c35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  0 30 ...  0  0  0]\n",
      " [ 0  0 81 ...  0  0  0]\n",
      " [ 0  0 26 ...  0  0  1]\n",
      " ...\n",
      " [ 0  0 50 ...  2  0  1]\n",
      " [ 0  0 43 ...  1  0  2]\n",
      " [ 1  0 55 ...  1  1  2]] [0 0 0 ... 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "data['result'] = (data['age']<=90) & (data['RevolvingUtilizationOfUnsecuredLines']>=.5) & (data['DebtRatio']<=.5)\n",
    "\n",
    "X = data.iloc[:,1:12].values.astype(int)\n",
    "y = data.iloc[:,12].values.astype(int)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=np.random.seed(7))\n",
    "\n",
    "print(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "60f75101-bc11-4815-be88-886b9516aa2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "# Other imports\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.utils.fixes import loguniform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "eb7cb0d4-7e06-4869-a1a9-9a474fb9411a",
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg = LogisticRegression()\n",
    "\n",
    "# Create a dictionary param_grid = C=loguniform(0.01, 1) as a continuous random variable\n",
    "param_grid = dict(C=loguniform(0.01, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "80154a56-c6f4-4e25-b443-e5cbe8eef52d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 82.11 using {'C': 0.11108015822053222}\n"
     ]
    }
   ],
   "source": [
    "# Instantiate RandomizedSearchCV with the required parameters\n",
    "random_model = RandomizedSearchCV(estimator=logreg, param_distributions=param_grid, cv=5)\n",
    "\n",
    "# Fit random_model to the data\n",
    "random_model_result = random_model.fit(X_train, y_train)\n",
    "\n",
    "# Summarize results\n",
    "best_score, best_params = random_model_result.best_score_, random_model_result.best_params_\n",
    "print(\"Best score: %.2f using %s\" % (best_score*100., best_params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c6fa0334-7ed5-4352-b0f3-3d302b578ed9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8158726199384718"
      ]
     },
     "execution_count": 29,
     "metadata": {},
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   "id": "cb8a14be-51f4-4391-bc65-b4b71b598da6",
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   "outputs": [],
   "source": []
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