yellowbrick_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import modules\n",
    "from warnings import simplefilter\n",
    "simplefilter(action='ignore', category=FutureWarning)\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import yellowbrick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Annual_Income</th>\n",
       "      <th>Num_Bank_Accounts</th>\n",
       "      <th>Num_Credit_Card</th>\n",
       "      <th>Interest_Rate</th>\n",
       "      <th>Num_of_Loan</th>\n",
       "      <th>Delay_from_due_date</th>\n",
       "      <th>Num_of_Delayed_Payment</th>\n",
       "      <th>Changed_Credit_Limit</th>\n",
       "      <th>...</th>\n",
       "      <th>Credit_Mix</th>\n",
       "      <th>Outstanding_Debt</th>\n",
       "      <th>Credit_Utilization_Ratio</th>\n",
       "      <th>Credit_History_Age</th>\n",
       "      <th>Payment_of_Min_Amount</th>\n",
       "      <th>Total_EMI_per_month</th>\n",
       "      <th>Amount_invested_monthly</th>\n",
       "      <th>Payment_Behaviour</th>\n",
       "      <th>Monthly_Balance</th>\n",
       "      <th>Credit_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "      <td>_______</td>\n",
       "      <td>34847.84</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5.42</td>\n",
       "      <td>...</td>\n",
       "      <td>Good</td>\n",
       "      <td>605.03</td>\n",
       "      <td>25.93</td>\n",
       "      <td>27.2</td>\n",
       "      <td>No</td>\n",
       "      <td>18.82</td>\n",
       "      <td>39.68</td>\n",
       "      <td>High_spent_Large_value_payments</td>\n",
       "      <td>485.30</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>34847.84</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5.42</td>\n",
       "      <td>...</td>\n",
       "      <td>Good</td>\n",
       "      <td>605.03</td>\n",
       "      <td>30.12</td>\n",
       "      <td>27.3</td>\n",
       "      <td>No</td>\n",
       "      <td>18.82</td>\n",
       "      <td>251.63</td>\n",
       "      <td>Low_spent_Large_value_payments</td>\n",
       "      <td>303.36</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>Engineer</td>\n",
       "      <td>143162.64</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2.10</td>\n",
       "      <td>...</td>\n",
       "      <td>Good</td>\n",
       "      <td>1303.01</td>\n",
       "      <td>35.69</td>\n",
       "      <td>18.5</td>\n",
       "      <td>No</td>\n",
       "      <td>246.99</td>\n",
       "      <td>453.62</td>\n",
       "      <td>Low_spent_Large_value_payments</td>\n",
       "      <td>788.11</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>Entrepreneur</td>\n",
       "      <td>30689.89</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1.99</td>\n",
       "      <td>...</td>\n",
       "      <td>Good</td>\n",
       "      <td>632.46</td>\n",
       "      <td>39.38</td>\n",
       "      <td>17.9</td>\n",
       "      <td>No</td>\n",
       "      <td>16.42</td>\n",
       "      <td>69.11</td>\n",
       "      <td>High_spent_Medium_value_payments</td>\n",
       "      <td>425.73</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>Entrepreneur</td>\n",
       "      <td>30689.89</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1.99</td>\n",
       "      <td>...</td>\n",
       "      <td>Good</td>\n",
       "      <td>632.46</td>\n",
       "      <td>36.82</td>\n",
       "      <td>18.1</td>\n",
       "      <td>No</td>\n",
       "      <td>16.42</td>\n",
       "      <td>191.89</td>\n",
       "      <td>Low_spent_Medium_value_payments</td>\n",
       "      <td>332.95</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17711</th>\n",
       "      <td>29</td>\n",
       "      <td>Architect</td>\n",
       "      <td>20002.88</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>25</td>\n",
       "      <td>18.31</td>\n",
       "      <td>...</td>\n",
       "      <td>Bad</td>\n",
       "      <td>3571.70</td>\n",
       "      <td>32.39</td>\n",
       "      <td>6.3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>60.96</td>\n",
       "      <td>107.21</td>\n",
       "      <td>Low_spent_Small_value_payments</td>\n",
       "      <td>314.82</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17712</th>\n",
       "      <td>29</td>\n",
       "      <td>Architect</td>\n",
       "      <td>20002.88</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>25</td>\n",
       "      <td>18.31</td>\n",
       "      <td>...</td>\n",
       "      <td>Bad</td>\n",
       "      <td>3571.70</td>\n",
       "      <td>37.53</td>\n",
       "      <td>6.4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>60.96</td>\n",
       "      <td>71.79</td>\n",
       "      <td>Low_spent_Small_value_payments</td>\n",
       "      <td>350.23</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17713</th>\n",
       "      <td>29</td>\n",
       "      <td>_______</td>\n",
       "      <td>20002.88</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
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       "      <td>Bad</td>\n",
       "      <td>3571.70</td>\n",
       "      <td>27.03</td>\n",
       "      <td>6.5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>60.96</td>\n",
       "      <td>50.85</td>\n",
       "      <td>High_spent_Small_value_payments</td>\n",
       "      <td>341.18</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17714</th>\n",
       "      <td>25</td>\n",
       "      <td>Mechanic</td>\n",
       "      <td>39628.99</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>11.50</td>\n",
       "      <td>...</td>\n",
       "      <td>Good</td>\n",
       "      <td>502.38</td>\n",
       "      <td>39.14</td>\n",
       "      <td>32.1</td>\n",
       "      <td>No</td>\n",
       "      <td>35.10</td>\n",
       "      <td>97.60</td>\n",
       "      <td>High_spent_Small_value_payments</td>\n",
       "      <td>463.24</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17715</th>\n",
       "      <td>25</td>\n",
       "      <td>Mechanic</td>\n",
       "      <td>39628.99</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>11.50</td>\n",
       "      <td>...</td>\n",
       "      <td>_</td>\n",
       "      <td>502.38</td>\n",
       "      <td>34.11</td>\n",
       "      <td>32.2</td>\n",
       "      <td>No</td>\n",
       "      <td>35.10</td>\n",
       "      <td>220.46</td>\n",
       "      <td>Low_spent_Medium_value_payments</td>\n",
       "      <td>360.38</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17716 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age    Occupation  Annual_Income  Num_Bank_Accounts  Num_Credit_Card  \\\n",
       "0       28       _______       34847.84                  2                4   \n",
       "1       28       Teacher       34847.84                  2                4   \n",
       "2       35      Engineer      143162.64                  1                5   \n",
       "3       55  Entrepreneur       30689.89                  2                5   \n",
       "4       55  Entrepreneur       30689.89                  2                5   \n",
       "...    ...           ...            ...                ...              ...   \n",
       "17711   29     Architect       20002.88                 10                8   \n",
       "17712   29     Architect       20002.88                 10                8   \n",
       "17713   29       _______       20002.88                 10                8   \n",
       "17714   25      Mechanic       39628.99                  4                6   \n",
       "17715   25      Mechanic       39628.99                  4                6   \n",
       "\n",
       "       Interest_Rate  Num_of_Loan  Delay_from_due_date  \\\n",
       "0                  6            1                    3   \n",
       "1                  6            1                    3   \n",
       "2                  8            3                    6   \n",
       "3                  4            1                    5   \n",
       "4                  4            1                    5   \n",
       "...              ...          ...                  ...   \n",
       "17711             29            5                   33   \n",
       "17712             29            5                   33   \n",
       "17713             29            5                   33   \n",
       "17714              7            2                   21   \n",
       "17715              7            2                   22   \n",
       "\n",
       "       Num_of_Delayed_Payment  Changed_Credit_Limit  ...  Credit_Mix  \\\n",
       "0                           1                  5.42  ...        Good   \n",
       "1                           3                  5.42  ...        Good   \n",
       "2                           3                  2.10  ...        Good   \n",
       "3                           6                  1.99  ...        Good   \n",
       "4                           6                  1.99  ...        Good   \n",
       "...                       ...                   ...  ...         ...   \n",
       "17711                      25                 18.31  ...         Bad   \n",
       "17712                      25                 18.31  ...         Bad   \n",
       "17713                      22                 18.31  ...         Bad   \n",
       "17714                       6                 11.50  ...        Good   \n",
       "17715                       5                 11.50  ...           _   \n",
       "\n",
       "      Outstanding_Debt  Credit_Utilization_Ratio  Credit_History_Age  \\\n",
       "0               605.03                     25.93                27.2   \n",
       "1               605.03                     30.12                27.3   \n",
       "2              1303.01                     35.69                18.5   \n",
       "3               632.46                     39.38                17.9   \n",
       "4               632.46                     36.82                18.1   \n",
       "...                ...                       ...                 ...   \n",
       "17711          3571.70                     32.39                 6.3   \n",
       "17712          3571.70                     37.53                 6.4   \n",
       "17713          3571.70                     27.03                 6.5   \n",
       "17714           502.38                     39.14                32.1   \n",
       "17715           502.38                     34.11                32.2   \n",
       "\n",
       "       Payment_of_Min_Amount Total_EMI_per_month  Amount_invested_monthly  \\\n",
       "0                         No               18.82                    39.68   \n",
       "1                         No               18.82                   251.63   \n",
       "2                         No              246.99                   453.62   \n",
       "3                         No               16.42                    69.11   \n",
       "4                         No               16.42                   191.89   \n",
       "...                      ...                 ...                      ...   \n",
       "17711                    Yes               60.96                   107.21   \n",
       "17712                    Yes               60.96                    71.79   \n",
       "17713                    Yes               60.96                    50.85   \n",
       "17714                     No               35.10                    97.60   \n",
       "17715                     No               35.10                   220.46   \n",
       "\n",
       "                      Payment_Behaviour Monthly_Balance  Credit_Score  \n",
       "0       High_spent_Large_value_payments          485.30          Good  \n",
       "1        Low_spent_Large_value_payments          303.36          Good  \n",
       "2        Low_spent_Large_value_payments          788.11          Poor  \n",
       "3      High_spent_Medium_value_payments          425.73      Standard  \n",
       "4       Low_spent_Medium_value_payments          332.95      Standard  \n",
       "...                                 ...             ...           ...  \n",
       "17711    Low_spent_Small_value_payments          314.82          Poor  \n",
       "17712    Low_spent_Small_value_payments          350.23          Poor  \n",
       "17713   High_spent_Small_value_payments          341.18          Poor  \n",
       "17714   High_spent_Small_value_payments          463.24          Poor  \n",
       "17715   Low_spent_Medium_value_payments          360.38          Poor  \n",
       "\n",
       "[17716 rows x 21 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the CSV file into a Pandas DataFrame\n",
    "credit_data_df = pd.read_csv(\n",
    "    Path('cleaned_data_test.csv')   \n",
    ")\n",
    "\n",
    "# Review the DataFrame\n",
    "credit_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>Occupation</th>\n",
       "      <th>Annual_Income</th>\n",
       "      <th>Num_Bank_Accounts</th>\n",
       "      <th>Num_Credit_Card</th>\n",
       "      <th>Interest_Rate</th>\n",
       "      <th>Num_of_Loan</th>\n",
       "      <th>Delay_from_due_date</th>\n",
       "      <th>Num_of_Delayed_Payment</th>\n",
       "      <th>Changed_Credit_Limit</th>\n",
       "      <th>...</th>\n",
       "      <th>Credit_Mix</th>\n",
       "      <th>Outstanding_Debt</th>\n",
       "      <th>Credit_Utilization_Ratio</th>\n",
       "      <th>Credit_History_Age</th>\n",
       "      <th>Payment_of_Min_Amount</th>\n",
       "      <th>Total_EMI_per_month</th>\n",
       "      <th>Amount_invested_monthly</th>\n",
       "      <th>Payment_Behaviour</th>\n",
       "      <th>Monthly_Balance</th>\n",
       "      <th>Credit_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "      <td>_______</td>\n",
       "      <td>34847.84</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5.42</td>\n",
       "      <td>...</td>\n",
       "      <td>Good</td>\n",
       "      <td>605.03</td>\n",
       "      <td>25.93</td>\n",
       "      <td>27.2</td>\n",
       "      <td>No</td>\n",
       "      <td>18.82</td>\n",
       "      <td>39.68</td>\n",
       "      <td>High_spent_Large_value_payments</td>\n",
       "      <td>485.30</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>34847.84</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5.42</td>\n",
       "      <td>...</td>\n",
       "      <td>Good</td>\n",
       "      <td>605.03</td>\n",
       "      <td>30.12</td>\n",
       "      <td>27.3</td>\n",
       "      <td>No</td>\n",
       "      <td>18.82</td>\n",
       "      <td>251.63</td>\n",
       "      <td>Low_spent_Large_value_payments</td>\n",
       "      <td>303.36</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>Engineer</td>\n",
       "      <td>143162.64</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2.10</td>\n",
       "      <td>...</td>\n",
       "      <td>Good</td>\n",
       "      <td>1303.01</td>\n",
       "      <td>35.69</td>\n",
       "      <td>18.5</td>\n",
       "      <td>No</td>\n",
       "      <td>246.99</td>\n",
       "      <td>453.62</td>\n",
       "      <td>Low_spent_Large_value_payments</td>\n",
       "      <td>788.11</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>Entrepreneur</td>\n",
       "      <td>30689.89</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1.99</td>\n",
       "      <td>...</td>\n",
       "      <td>Good</td>\n",
       "      <td>632.46</td>\n",
       "      <td>39.38</td>\n",
       "      <td>17.9</td>\n",
       "      <td>No</td>\n",
       "      <td>16.42</td>\n",
       "      <td>69.11</td>\n",
       "      <td>High_spent_Medium_value_payments</td>\n",
       "      <td>425.73</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>Entrepreneur</td>\n",
       "      <td>30689.89</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1.99</td>\n",
       "      <td>...</td>\n",
       "      <td>Good</td>\n",
       "      <td>632.46</td>\n",
       "      <td>36.82</td>\n",
       "      <td>18.1</td>\n",
       "      <td>No</td>\n",
       "      <td>16.42</td>\n",
       "      <td>191.89</td>\n",
       "      <td>Low_spent_Medium_value_payments</td>\n",
       "      <td>332.95</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17711</th>\n",
       "      <td>29</td>\n",
       "      <td>Architect</td>\n",
       "      <td>20002.88</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>25</td>\n",
       "      <td>18.31</td>\n",
       "      <td>...</td>\n",
       "      <td>Bad</td>\n",
       "      <td>3571.70</td>\n",
       "      <td>32.39</td>\n",
       "      <td>6.3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>60.96</td>\n",
       "      <td>107.21</td>\n",
       "      <td>Low_spent_Small_value_payments</td>\n",
       "      <td>314.82</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17712</th>\n",
       "      <td>29</td>\n",
       "      <td>Architect</td>\n",
       "      <td>20002.88</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>25</td>\n",
       "      <td>18.31</td>\n",
       "      <td>...</td>\n",
       "      <td>Bad</td>\n",
       "      <td>3571.70</td>\n",
       "      <td>37.53</td>\n",
       "      <td>6.4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>60.96</td>\n",
       "      <td>71.79</td>\n",
       "      <td>Low_spent_Small_value_payments</td>\n",
       "      <td>350.23</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17713</th>\n",
       "      <td>29</td>\n",
       "      <td>_______</td>\n",
       "      <td>20002.88</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>22</td>\n",
       "      <td>18.31</td>\n",
       "      <td>...</td>\n",
       "      <td>Bad</td>\n",
       "      <td>3571.70</td>\n",
       "      <td>27.03</td>\n",
       "      <td>6.5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>60.96</td>\n",
       "      <td>50.85</td>\n",
       "      <td>High_spent_Small_value_payments</td>\n",
       "      <td>341.18</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17714</th>\n",
       "      <td>25</td>\n",
       "      <td>Mechanic</td>\n",
       "      <td>39628.99</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>11.50</td>\n",
       "      <td>...</td>\n",
       "      <td>Good</td>\n",
       "      <td>502.38</td>\n",
       "      <td>39.14</td>\n",
       "      <td>32.1</td>\n",
       "      <td>No</td>\n",
       "      <td>35.10</td>\n",
       "      <td>97.60</td>\n",
       "      <td>High_spent_Small_value_payments</td>\n",
       "      <td>463.24</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17715</th>\n",
       "      <td>25</td>\n",
       "      <td>Mechanic</td>\n",
       "      <td>39628.99</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>11.50</td>\n",
       "      <td>...</td>\n",
       "      <td>_</td>\n",
       "      <td>502.38</td>\n",
       "      <td>34.11</td>\n",
       "      <td>32.2</td>\n",
       "      <td>No</td>\n",
       "      <td>35.10</td>\n",
       "      <td>220.46</td>\n",
       "      <td>Low_spent_Medium_value_payments</td>\n",
       "      <td>360.38</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17711 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age    Occupation  Annual_Income  Num_Bank_Accounts  Num_Credit_Card  \\\n",
       "0       28       _______       34847.84                  2                4   \n",
       "1       28       Teacher       34847.84                  2                4   \n",
       "2       35      Engineer      143162.64                  1                5   \n",
       "3       55  Entrepreneur       30689.89                  2                5   \n",
       "4       55  Entrepreneur       30689.89                  2                5   \n",
       "...    ...           ...            ...                ...              ...   \n",
       "17711   29     Architect       20002.88                 10                8   \n",
       "17712   29     Architect       20002.88                 10                8   \n",
       "17713   29       _______       20002.88                 10                8   \n",
       "17714   25      Mechanic       39628.99                  4                6   \n",
       "17715   25      Mechanic       39628.99                  4                6   \n",
       "\n",
       "       Interest_Rate  Num_of_Loan  Delay_from_due_date  \\\n",
       "0                  6            1                    3   \n",
       "1                  6            1                    3   \n",
       "2                  8            3                    6   \n",
       "3                  4            1                    5   \n",
       "4                  4            1                    5   \n",
       "...              ...          ...                  ...   \n",
       "17711             29            5                   33   \n",
       "17712             29            5                   33   \n",
       "17713             29            5                   33   \n",
       "17714              7            2                   21   \n",
       "17715              7            2                   22   \n",
       "\n",
       "       Num_of_Delayed_Payment  Changed_Credit_Limit  ...  Credit_Mix  \\\n",
       "0                           1                  5.42  ...        Good   \n",
       "1                           3                  5.42  ...        Good   \n",
       "2                           3                  2.10  ...        Good   \n",
       "3                           6                  1.99  ...        Good   \n",
       "4                           6                  1.99  ...        Good   \n",
       "...                       ...                   ...  ...         ...   \n",
       "17711                      25                 18.31  ...         Bad   \n",
       "17712                      25                 18.31  ...         Bad   \n",
       "17713                      22                 18.31  ...         Bad   \n",
       "17714                       6                 11.50  ...        Good   \n",
       "17715                       5                 11.50  ...           _   \n",
       "\n",
       "      Outstanding_Debt  Credit_Utilization_Ratio  Credit_History_Age  \\\n",
       "0               605.03                     25.93                27.2   \n",
       "1               605.03                     30.12                27.3   \n",
       "2              1303.01                     35.69                18.5   \n",
       "3               632.46                     39.38                17.9   \n",
       "4               632.46                     36.82                18.1   \n",
       "...                ...                       ...                 ...   \n",
       "17711          3571.70                     32.39                 6.3   \n",
       "17712          3571.70                     37.53                 6.4   \n",
       "17713          3571.70                     27.03                 6.5   \n",
       "17714           502.38                     39.14                32.1   \n",
       "17715           502.38                     34.11                32.2   \n",
       "\n",
       "       Payment_of_Min_Amount Total_EMI_per_month  Amount_invested_monthly  \\\n",
       "0                         No               18.82                    39.68   \n",
       "1                         No               18.82                   251.63   \n",
       "2                         No              246.99                   453.62   \n",
       "3                         No               16.42                    69.11   \n",
       "4                         No               16.42                   191.89   \n",
       "...                      ...                 ...                      ...   \n",
       "17711                    Yes               60.96                   107.21   \n",
       "17712                    Yes               60.96                    71.79   \n",
       "17713                    Yes               60.96                    50.85   \n",
       "17714                     No               35.10                    97.60   \n",
       "17715                     No               35.10                   220.46   \n",
       "\n",
       "                      Payment_Behaviour Monthly_Balance  Credit_Score  \n",
       "0       High_spent_Large_value_payments          485.30          Good  \n",
       "1        Low_spent_Large_value_payments          303.36          Good  \n",
       "2        Low_spent_Large_value_payments          788.11          Poor  \n",
       "3      High_spent_Medium_value_payments          425.73      Standard  \n",
       "4       Low_spent_Medium_value_payments          332.95      Standard  \n",
       "...                                 ...             ...           ...  \n",
       "17711    Low_spent_Small_value_payments          314.82          Poor  \n",
       "17712    Low_spent_Small_value_payments          350.23          Poor  \n",
       "17713   High_spent_Small_value_payments          341.18          Poor  \n",
       "17714   High_spent_Small_value_payments          463.24          Poor  \n",
       "17715   Low_spent_Medium_value_payments          360.38          Poor  \n",
       "\n",
       "[17711 rows x 21 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  find and remove outlier, typo rows - Monthly_Balance\n",
    "\n",
    "# Define the range of normal values\n",
    "lower_bound = 0\n",
    "upper_bound = 2000\n",
    "\n",
    "column = 'Monthly_Balance'\n",
    "\n",
    "df_train = credit_data_df\n",
    "\n",
    "# testing variable\n",
    "within_range_df = df_train[(df_train[column] >= lower_bound) & (df_train[column] <= upper_bound)]\n",
    "\n",
    "# used this to play with range and dtm where cutoff for normal values were\n",
    "outliers_df = df_train[(df_train[column] < lower_bound) | (df_train[column] > upper_bound)]\n",
    "\n",
    "# Filter the DataFrame to keep only rows with values within the specified range\n",
    "cleaned_df = df_train[(df_train[column] >= lower_bound) & (df_train[column] <= upper_bound)]\n",
    "\n",
    "outliers_df\n",
    "\n",
    "credit_data_df = cleaned_df\n",
    "\n",
    "credit_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/mn/wntm45pd3wsdzqy20b2c2g6r0000gn/T/ipykernel_69445/1143441489.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  credit_data_df.rename(columns={\n"
     ]
    }
   ],
   "source": [
    "# rename columns for graph\n",
    "credit_data_df.rename(columns={\n",
    "    'Age': 'Age',\n",
    "    'Occupation': 'Occupation',\n",
    "    'Annual_Income': 'income',\n",
    "    'Num_Bank_Accounts': '#bank',\n",
    "    'Num_Credit_Card': '#cc',\n",
    "    'Interest_Rate': 'int.rt',\n",
    "    'Num_of_Loan': '#loan',\n",
    "    'Delay_from_due_date': 'dfdd',\n",
    "    'Num_of_Delayed_Payment': '#dp',\n",
    "    'Changed_Credit_Limit': 'ccl',\n",
    "    'Num_Credit_Inquiries': '#ci',\n",
    "    'Credit_Mix': 'Credit_Mix',\n",
    "    'Outstanding_Debt': 'debt',\n",
    "    'Credit_Utilization_Ratio': 'CUR',\n",
    "    'Credit_History_Age': 'CHA',\n",
    "    'Payment_of_Min_Amount': 'Payment_of_Min_Amount',\n",
    "    'Total_EMI_per_month': 'Total_EMI_per_month',\n",
    "    'Amount_invested_monthly': 'AIM',\n",
    "    'Payment_Behaviour': 'Payment_Behaviour',\n",
    "    'Monthly_Balance': 'MB',\n",
    "}, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/mn/wntm45pd3wsdzqy20b2c2g6r0000gn/T/ipykernel_69445/2947341938.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  credit_data_df.drop(columns=['Occupation'], inplace=True)\n",
      "/var/folders/mn/wntm45pd3wsdzqy20b2c2g6r0000gn/T/ipykernel_69445/2947341938.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  credit_data_df.drop(columns=['Total_EMI_per_month'], inplace=True)\n",
      "/var/folders/mn/wntm45pd3wsdzqy20b2c2g6r0000gn/T/ipykernel_69445/2947341938.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  credit_data_df.drop(columns=['Payment_Behaviour'], inplace=True)\n",
      "/var/folders/mn/wntm45pd3wsdzqy20b2c2g6r0000gn/T/ipykernel_69445/2947341938.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  credit_data_df.drop(columns=['Payment_of_Min_Amount'], inplace=True)\n",
      "/var/folders/mn/wntm45pd3wsdzqy20b2c2g6r0000gn/T/ipykernel_69445/2947341938.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  credit_data_df.drop(columns=['Credit_Mix'], inplace=True)\n",
      "/var/folders/mn/wntm45pd3wsdzqy20b2c2g6r0000gn/T/ipykernel_69445/2947341938.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  credit_data_df.drop(columns=['#loan'], inplace=True)\n",
      "/var/folders/mn/wntm45pd3wsdzqy20b2c2g6r0000gn/T/ipykernel_69445/2947341938.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  credit_data_df.drop(columns=['AIM'], inplace=True)\n",
      "/var/folders/mn/wntm45pd3wsdzqy20b2c2g6r0000gn/T/ipykernel_69445/2947341938.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  credit_data_df.drop(columns=['CHA'], inplace=True)\n",
      "/var/folders/mn/wntm45pd3wsdzqy20b2c2g6r0000gn/T/ipykernel_69445/2947341938.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  credit_data_df.drop(columns=['CUR'], inplace=True)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>income</th>\n",
       "      <th>#bank</th>\n",
       "      <th>#cc</th>\n",
       "      <th>int.rt</th>\n",
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       "      <td>5</td>\n",
       "      <td>605.03</td>\n",
       "      <td>303.36</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>143162.64</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2.10</td>\n",
       "      <td>3</td>\n",
       "      <td>1303.01</td>\n",
       "      <td>788.11</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>30689.89</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1.99</td>\n",
       "      <td>4</td>\n",
       "      <td>632.46</td>\n",
       "      <td>425.73</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>30689.89</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1.99</td>\n",
       "      <td>4</td>\n",
       "      <td>632.46</td>\n",
       "      <td>332.95</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17711</th>\n",
       "      <td>29</td>\n",
       "      <td>20002.88</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>33</td>\n",
       "      <td>25</td>\n",
       "      <td>18.31</td>\n",
       "      <td>9</td>\n",
       "      <td>3571.70</td>\n",
       "      <td>314.82</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17712</th>\n",
       "      <td>29</td>\n",
       "      <td>20002.88</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>33</td>\n",
       "      <td>25</td>\n",
       "      <td>18.31</td>\n",
       "      <td>12</td>\n",
       "      <td>3571.70</td>\n",
       "      <td>350.23</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17713</th>\n",
       "      <td>29</td>\n",
       "      <td>20002.88</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>33</td>\n",
       "      <td>22</td>\n",
       "      <td>18.31</td>\n",
       "      <td>12</td>\n",
       "      <td>3571.70</td>\n",
       "      <td>341.18</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17714</th>\n",
       "      <td>25</td>\n",
       "      <td>39628.99</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>11.50</td>\n",
       "      <td>7</td>\n",
       "      <td>502.38</td>\n",
       "      <td>463.24</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17715</th>\n",
       "      <td>25</td>\n",
       "      <td>39628.99</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>11.50</td>\n",
       "      <td>7</td>\n",
       "      <td>502.38</td>\n",
       "      <td>360.38</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17711 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age     income  #bank  #cc  int.rt  dfdd  #dp    ccl  #ci     debt  \\\n",
       "0       28   34847.84      2    4       6     3    1   5.42    5   605.03   \n",
       "1       28   34847.84      2    4       6     3    3   5.42    5   605.03   \n",
       "2       35  143162.64      1    5       8     6    3   2.10    3  1303.01   \n",
       "3       55   30689.89      2    5       4     5    6   1.99    4   632.46   \n",
       "4       55   30689.89      2    5       4     5    6   1.99    4   632.46   \n",
       "...    ...        ...    ...  ...     ...   ...  ...    ...  ...      ...   \n",
       "17711   29   20002.88     10    8      29    33   25  18.31    9  3571.70   \n",
       "17712   29   20002.88     10    8      29    33   25  18.31   12  3571.70   \n",
       "17713   29   20002.88     10    8      29    33   22  18.31   12  3571.70   \n",
       "17714   25   39628.99      4    6       7    21    6  11.50    7   502.38   \n",
       "17715   25   39628.99      4    6       7    22    5  11.50    7   502.38   \n",
       "\n",
       "           MB Credit_Score  \n",
       "0      485.30         Good  \n",
       "1      303.36         Good  \n",
       "2      788.11         Poor  \n",
       "3      425.73     Standard  \n",
       "4      332.95     Standard  \n",
       "...       ...          ...  \n",
       "17711  314.82         Poor  \n",
       "17712  350.23         Poor  \n",
       "17713  341.18         Poor  \n",
       "17714  463.24         Poor  \n",
       "17715  360.38         Poor  \n",
       "\n",
       "[17711 rows x 12 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_data_df.drop(columns=['Occupation'], inplace=True)\n",
    "credit_data_df.drop(columns=['Total_EMI_per_month'], inplace=True)\n",
    "credit_data_df.drop(columns=['Payment_Behaviour'], inplace=True)\n",
    "credit_data_df.drop(columns=['Payment_of_Min_Amount'], inplace=True)\n",
    "credit_data_df.drop(columns=['Credit_Mix'], inplace=True)\n",
    "\n",
    "credit_data_df.drop(columns=['#loan'], inplace=True)\n",
    "credit_data_df.drop(columns=['AIM'], inplace=True)\n",
    "credit_data_df.drop(columns=['CHA'], inplace=True)\n",
    "credit_data_df.drop(columns=['CUR'], inplace=True)\n",
    "\n",
    "# credit_data_df.drop(columns=['income'], inplace=True)\n",
    "# credit_data_df.drop(columns=['#ci'], inplace=True)\n",
    "\n",
    "credit_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seperate the features, X,  from the target variable, y\n",
    "y = credit_data_df['Credit_Score']\n",
    "X = credit_data_df.drop(columns='Credit_Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>350.23</td>\n",
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       "    <tr>\n",
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       "      <td>29</td>\n",
       "      <td>20002.88</td>\n",
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       "      <td>22</td>\n",
       "      <td>18.31</td>\n",
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       "      <td>3571.70</td>\n",
       "      <td>341.18</td>\n",
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       "    <tr>\n",
       "      <th>17714</th>\n",
       "      <td>25</td>\n",
       "      <td>39628.99</td>\n",
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       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>11.50</td>\n",
       "      <td>7</td>\n",
       "      <td>502.38</td>\n",
       "      <td>463.24</td>\n",
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       "      <td>360.38</td>\n",
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       "<p>17711 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age     income  #bank  #cc  int.rt  dfdd  #dp    ccl  #ci     debt  \\\n",
       "0       28   34847.84      2    4       6     3    1   5.42    5   605.03   \n",
       "1       28   34847.84      2    4       6     3    3   5.42    5   605.03   \n",
       "2       35  143162.64      1    5       8     6    3   2.10    3  1303.01   \n",
       "3       55   30689.89      2    5       4     5    6   1.99    4   632.46   \n",
       "4       55   30689.89      2    5       4     5    6   1.99    4   632.46   \n",
       "...    ...        ...    ...  ...     ...   ...  ...    ...  ...      ...   \n",
       "17711   29   20002.88     10    8      29    33   25  18.31    9  3571.70   \n",
       "17712   29   20002.88     10    8      29    33   25  18.31   12  3571.70   \n",
       "17713   29   20002.88     10    8      29    33   22  18.31   12  3571.70   \n",
       "17714   25   39628.99      4    6       7    21    6  11.50    7   502.38   \n",
       "17715   25   39628.99      4    6       7    22    5  11.50    7   502.38   \n",
       "\n",
       "           MB  \n",
       "0      485.30  \n",
       "1      303.36  \n",
       "2      788.11  \n",
       "3      425.73  \n",
       "4      332.95  \n",
       "...       ...  \n",
       "17711  314.82  \n",
       "17712  350.23  \n",
       "17713  341.18  \n",
       "17714  463.24  \n",
       "17715  360.38  \n",
       "\n",
       "[17711 rows x 11 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preview the features data\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['MB'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode the categorical variables using get_dummies\n",
    "X = pd.get_dummies(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4</td>\n",
       "      <td>632.46</td>\n",
       "      <td>332.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age     income  #bank  #cc  int.rt  dfdd  #dp   ccl  #ci     debt      MB\n",
       "0   28   34847.84      2    4       6     3    1  5.42    5   605.03  485.30\n",
       "1   28   34847.84      2    4       6     3    3  5.42    5   605.03  303.36\n",
       "2   35  143162.64      1    5       8     6    3  2.10    3  1303.01  788.11\n",
       "3   55   30689.89      2    5       4     5    6  1.99    4   632.46  425.73\n",
       "4   55   30689.89      2    5       4     5    6  1.99    4   632.46  332.95"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review the features data\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset using train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate a StandardScaler instance\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit the training data to the standard scaler\n",
    "X_scaler = scaler.fit(X_train)\n",
    "\n",
    "# Transform the training data using the scaler\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "\n",
    "# Transform the testing data using the scaler\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the KNeighborsClassifier module from sklearn\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Instantiate the KNeighborsClassifier model with n_neighbors = 7 \n",
    "knn = KNeighborsClassifier(n_neighbors=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=7)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=7)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=7)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model using the training data\n",
    "knn.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create predictions using the testing data\n",
    "y_pred = knn.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Good       0.63      0.70      0.66      1299\n",
      "        Poor       0.74      0.65      0.69      1332\n",
      "    Standard       0.69      0.70      0.69      1797\n",
      "\n",
      "    accuracy                           0.68      4428\n",
      "   macro avg       0.69      0.68      0.68      4428\n",
      "weighted avg       0.69      0.68      0.68      4428\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the classification report comparing the testing data to the model predictions\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 906,  191,  390],\n",
       "       [  75,  837,  217],\n",
       "       [ 341,  303, 1169]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print confusion matrix\n",
    "confusion_matrix(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from yellowbrick.features import rank2d\n",
    "\n",
    "visualizer = rank2d(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from yellowbrick.features import parallel_coordinates\n",
    "\n",
    "visualizer = parallel_coordinates(X, y, normalize=\"standard\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Parallel Coordinates for 14 Features'}>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specify the features of interest and the classes of the target\n",
    "\n",
    "from yellowbrick.features import parallel_coordinates\n",
    "\n",
    "# Instantiate the visualizer\n",
    "visualizer = parallel_coordinates(X, y, normalize=\"standard\", sample=0.2, shuffle=True)\n",
    "\n",
    "# Fit the visualizer and display it\n",
    "visualizer.fit_transform(X, y)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/cashojo/anaconda3/envs/dev/lib/python3.10/site-packages/scipy/stats/_morestats.py:1882: UserWarning: p-value may not be accurate for N > 5000.\n",
      "  warnings.warn(\"p-value may not be accurate for N > 5000.\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from yellowbrick.features import rank1d\n",
    "\n",
    "visualizer = rank1d(X, color=\"r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from yellowbrick.classifier import discrimination_threshold\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "visualizer = discrimination_threshold(\n",
    "    LogisticRegression(multi_class=\"auto\", solver=\"liblinear\"), X, y\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "ename": "YellowbrickValueError",
     "evalue": "Target needs to be label encoded.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[0;32m~/anaconda3/envs/dev/lib/python3.10/site-packages/yellowbrick/features/projection.py:322\u001b[0m, in \u001b[0;36mProjectionVisualizer._determine_scatter_kwargs\u001b[0;34m(self, y)\u001b[0m\n\u001b[1;32m    321\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 322\u001b[0m     scatter_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mc\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_colors[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclasses_[yi]] \u001b[38;5;28;01mfor\u001b[39;00m yi \u001b[38;5;129;01min\u001b[39;00m y]\n\u001b[1;32m    323\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m:\n",
      "File \u001b[0;32m~/anaconda3/envs/dev/lib/python3.10/site-packages/yellowbrick/features/projection.py:322\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    321\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 322\u001b[0m     scatter_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mc\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_colors[\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclasses_\u001b[49m\u001b[43m[\u001b[49m\u001b[43myi\u001b[49m\u001b[43m]\u001b[49m] \u001b[38;5;28;01mfor\u001b[39;00m yi \u001b[38;5;129;01min\u001b[39;00m y]\n\u001b[1;32m    323\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m:\n",
      "\u001b[0;31mIndexError\u001b[0m: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mYellowbrickValueError\u001b[0m                     Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[53], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01myellowbrick\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfeatures\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pca_decomposition\n\u001b[0;32m----> 3\u001b[0m visualizer \u001b[38;5;241m=\u001b[39m \u001b[43mpca_decomposition\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/dev/lib/python3.10/site-packages/yellowbrick/features/pca.py:619\u001b[0m, in \u001b[0;36mpca_decomposition\u001b[0;34m(X, y, ax, features, classes, scale, projection, proj_features, colors, colormap, alpha, random_state, colorbar, heatmap, show, **kwargs)\u001b[0m\n\u001b[1;32m    617\u001b[0m \u001b[38;5;66;03m# Fit and transform the visualizer (calls draw)\u001b[39;00m\n\u001b[1;32m    618\u001b[0m visualizer\u001b[38;5;241m.\u001b[39mfit(X, y)\n\u001b[0;32m--> 619\u001b[0m \u001b[43mvisualizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    621\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m show:\n\u001b[1;32m    622\u001b[0m     visualizer\u001b[38;5;241m.\u001b[39mshow()\n",
      "File \u001b[0;32m~/anaconda3/envs/dev/lib/python3.10/site-packages/sklearn/utils/_set_output.py:140\u001b[0m, in \u001b[0;36m_wrap_method_output.<locals>.wrapped\u001b[0;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[1;32m    138\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[1;32m    139\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 140\u001b[0m     data_to_wrap \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    141\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_to_wrap, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m    142\u001b[0m         \u001b[38;5;66;03m# only wrap the first output for cross decomposition\u001b[39;00m\n\u001b[1;32m    143\u001b[0m         return_tuple \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    144\u001b[0m             _wrap_data_with_container(method, data_to_wrap[\u001b[38;5;241m0\u001b[39m], X, \u001b[38;5;28mself\u001b[39m),\n\u001b[1;32m    145\u001b[0m             \u001b[38;5;241m*\u001b[39mdata_to_wrap[\u001b[38;5;241m1\u001b[39m:],\n\u001b[1;32m    146\u001b[0m         )\n",
      "File \u001b[0;32m~/anaconda3/envs/dev/lib/python3.10/site-packages/yellowbrick/features/pca.py:323\u001b[0m, in \u001b[0;36mPCA.transform\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m    321\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    322\u001b[0m     Xp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpca_transformer\u001b[38;5;241m.\u001b[39mtransform(X)\n\u001b[0;32m--> 323\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mXp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    324\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m Xp\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m NotFittedError:\n",
      "File \u001b[0;32m~/anaconda3/envs/dev/lib/python3.10/site-packages/yellowbrick/features/pca.py:352\u001b[0m, in \u001b[0;36mPCA.draw\u001b[0;34m(self, Xp, y)\u001b[0m\n\u001b[1;32m    329\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    330\u001b[0m \u001b[38;5;124;03mPlots a scatterplot of points that represented the decomposition,\u001b[39;00m\n\u001b[1;32m    331\u001b[0m \u001b[38;5;124;03m`pca_features_`, of the original features, `X`, projected into either 2 or\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    349\u001b[0m \u001b[38;5;124;03m    Returns the axes that the scatter plot was drawn on.\u001b[39;00m\n\u001b[1;32m    350\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    351\u001b[0m \u001b[38;5;66;03m# Call to super draw which draws the scatter plot.\u001b[39;00m\n\u001b[0;32m--> 352\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mPCA\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mXp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    353\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mproj_features:\n\u001b[1;32m    354\u001b[0m     \u001b[38;5;66;03m# Draws projection features in transformed space.\u001b[39;00m\n\u001b[1;32m    355\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_draw_projection_features(Xp, y)\n",
      "File \u001b[0;32m~/anaconda3/envs/dev/lib/python3.10/site-packages/yellowbrick/features/projection.py:256\u001b[0m, in \u001b[0;36mProjectionVisualizer.draw\u001b[0;34m(self, Xp, y)\u001b[0m\n\u001b[1;32m    237\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw\u001b[39m(\u001b[38;5;28mself\u001b[39m, Xp, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    238\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    239\u001b[0m \u001b[38;5;124;03m    Draws the points described by Xp and colored by the points in y. Can be\u001b[39;00m\n\u001b[1;32m    240\u001b[0m \u001b[38;5;124;03m    called multiple times before finalize to add more scatter plots to the\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    254\u001b[0m \u001b[38;5;124;03m        Returns the axes that the scatter plot was drawn on.\u001b[39;00m\n\u001b[1;32m    255\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 256\u001b[0m     scatter_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_determine_scatter_kwargs\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    258\u001b[0m     \u001b[38;5;66;03m# Draws the layout of the visualizer. It draws the axes for colorbars,\u001b[39;00m\n\u001b[1;32m    259\u001b[0m     \u001b[38;5;66;03m# heatmap, etc.\u001b[39;00m\n\u001b[1;32m    260\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlayout()\n",
      "File \u001b[0;32m~/anaconda3/envs/dev/lib/python3.10/site-packages/yellowbrick/features/projection.py:324\u001b[0m, in \u001b[0;36mProjectionVisualizer._determine_scatter_kwargs\u001b[0;34m(self, y)\u001b[0m\n\u001b[1;32m    322\u001b[0m         scatter_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mc\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_colors[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclasses_[yi]] \u001b[38;5;28;01mfor\u001b[39;00m yi \u001b[38;5;129;01min\u001b[39;00m y]\n\u001b[1;32m    323\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m:\n\u001b[0;32m--> 324\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m YellowbrickValueError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTarget needs to be label encoded.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    326\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_target_color_type \u001b[38;5;241m==\u001b[39m TargetType\u001b[38;5;241m.\u001b[39mCONTINUOUS:\n\u001b[1;32m    327\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[0;31mYellowbrickValueError\u001b[0m: Target needs to be label encoded."
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from yellowbrick.features import pca_decomposition\n",
    "\n",
    "visualizer = pca_decomposition(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Age</th>\n",
       "      <th>income</th>\n",
       "      <th>#bank</th>\n",
       "      <th>#cc</th>\n",
       "      <th>int.rt</th>\n",
       "      <th>#loan</th>\n",
       "      <th>dfdd</th>\n",
       "      <th>#dp</th>\n",
       "      <th>ccl</th>\n",
       "      <th>#ci</th>\n",
       "      <th>debt</th>\n",
       "      <th>CUR</th>\n",
       "      <th>CHA</th>\n",
       "      <th>AIM</th>\n",
       "      <th>Credit_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>19114.12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>11.27</td>\n",
       "      <td>4</td>\n",
       "      <td>809.98</td>\n",
       "      <td>26.82</td>\n",
       "      <td>22.1</td>\n",
       "      <td>80.42</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>19114.12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6.27</td>\n",
       "      <td>4</td>\n",
       "      <td>809.98</td>\n",
       "      <td>31.38</td>\n",
       "      <td>22.3</td>\n",
       "      <td>199.46</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23</td>\n",
       "      <td>19114.12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>11.27</td>\n",
       "      <td>4</td>\n",
       "      <td>809.98</td>\n",
       "      <td>22.54</td>\n",
       "      <td>22.6</td>\n",
       "      <td>178.34</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>34847.84</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5.42</td>\n",
       "      <td>2</td>\n",
       "      <td>605.03</td>\n",
       "      <td>24.46</td>\n",
       "      <td>26.6</td>\n",
       "      <td>104.29</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>34847.84</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7.42</td>\n",
       "      <td>2</td>\n",
       "      <td>605.03</td>\n",
       "      <td>38.55</td>\n",
       "      <td>26.7</td>\n",
       "      <td>40.39</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age    income  #bank  #cc  int.rt  #loan  dfdd  #dp    ccl  #ci    debt  \\\n",
       "0   23  19114.12      3    4       3      4     3    7  11.27    4  809.98   \n",
       "1   23  19114.12      3    4       3      4     5    4   6.27    4  809.98   \n",
       "2   23  19114.12      3    4       3      4     3    8  11.27    4  809.98   \n",
       "3   28  34847.84      2    4       6      1     3    4   5.42    2  605.03   \n",
       "4   28  34847.84      2    4       6      1     7    1   7.42    2  605.03   \n",
       "\n",
       "     CUR   CHA     AIM Credit_Score  \n",
       "0  26.82  22.1   80.42         Good  \n",
       "1  31.38  22.3  199.46         Good  \n",
       "2  22.54  22.6  178.34         Good  \n",
       "3  24.46  26.6  104.29     Standard  \n",
       "4  38.55  26.7   40.39         Good  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a df from the scaled data, take original column names\n",
    "df_credit_scaled = pd.DataFrame(credit_data_df, columns=credit_data_df.columns)\n",
    "\n",
    "# Set the coinid column as index\n",
    "df_credit_scaled.index = credit_data_df.index\n",
    "\n",
    "# Display sample data\n",
    "df_credit_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]\n"
     ]
    }
   ],
   "source": [
    "# Create a list with the number of k-values from 1 to 11\n",
    "k = list(range(1, 12))\n",
    "print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "# Create an empty list to store the inertia values\n",
    "inertia = []\n",
    "\n",
    "# Create a for loop to compute the inertia with each possible value of k\n",
    "# Inside the loop:\n",
    "# 1. Create a KMeans model using the loop counter for the n_clusters\n",
    "# 2. Fit the model to the data using `df_market_data_scaled`\n",
    "# 3. Append the model.inertia_ to the inertia list\n",
    "for i in k:\n",
    "    k_model = KMeans(n_clusters=i, random_state=1)\n",
    "    k_model.fit(X)\n",
    "    inertia.append(k_model.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary with the data to plot the Elbow curve\n",
    "elbow_data = {\"k\": k, \"inertia\": inertia}\n",
    "\n",
    "# Create a df with the data to plot the Elbow curve\n",
    "df_elbow = pd.DataFrame(elbow_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       ":Curve   [k]   (inertia)"
      ]
     },
     "execution_count": 37,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1002"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import hvplot.pandas\n",
    "# Plot a line chart with all the inertia values computed with \n",
    "# the different values of k to visually identify the optimal value for k.\n",
    "df_elbow.hvplot.line(\n",
    "    x=\"k\", \n",
    "    y=\"inertia\", \n",
    "    title=\"Elbow Curve\", \n",
    "    xticks=k\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "KMeans(n_clusters=2, random_state=1)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize the K-Means model using the best value for k\n",
    "model = KMeans(n_clusters=2, random_state=1)\n",
    "\n",
    "# Print the model\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "KMeans(n_clusters=2, random_state=1)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the scaled data to the instance of the model\n",
    "model.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49523\n"
     ]
    }
   ],
   "source": [
    "# Make predictions about the data clusters using the trained model\n",
    "# Predict the clusters to group the cryptocurrencies using the scaled data\n",
    "predictions = model.predict(X)\n",
    "\n",
    "# Print the resulting array of cluster values\n",
    "print(len(predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_credit_predictions = X.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>income</th>\n",
       "      <th>#bank</th>\n",
       "      <th>#cc</th>\n",
       "      <th>int.rt</th>\n",
       "      <th>#loan</th>\n",
       "      <th>dfdd</th>\n",
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       "      <th>ccl</th>\n",
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       "      <th>AIM</th>\n",
       "      <th>predicted_cluster</th>\n",
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       "      <th>0</th>\n",
       "      <td>23</td>\n",
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       "      <td>11.27</td>\n",
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       "      <td>809.98</td>\n",
       "      <td>26.82</td>\n",
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       "      <td>19114.12</td>\n",
       "      <td>3</td>\n",
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       "      <td>31.38</td>\n",
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       "      <td>23</td>\n",
       "      <td>19114.12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
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       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>11.27</td>\n",
       "      <td>4</td>\n",
       "      <td>809.98</td>\n",
       "      <td>22.54</td>\n",
       "      <td>22.6</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>34847.84</td>\n",
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       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5.42</td>\n",
       "      <td>2</td>\n",
       "      <td>605.03</td>\n",
       "      <td>24.46</td>\n",
       "      <td>26.6</td>\n",
       "      <td>104.29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>34847.84</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7.42</td>\n",
       "      <td>2</td>\n",
       "      <td>605.03</td>\n",
       "      <td>38.55</td>\n",
       "      <td>26.7</td>\n",
       "      <td>40.39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49518</th>\n",
       "      <td>28</td>\n",
       "      <td>20002.88</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>25</td>\n",
       "      <td>18.31</td>\n",
       "      <td>9</td>\n",
       "      <td>3571.70</td>\n",
       "      <td>28.51</td>\n",
       "      <td>6.0</td>\n",
       "      <td>213.98</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49519</th>\n",
       "      <td>29</td>\n",
       "      <td>20002.88</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>25</td>\n",
       "      <td>18.31</td>\n",
       "      <td>9</td>\n",
       "      <td>3571.70</td>\n",
       "      <td>37.14</td>\n",
       "      <td>6.2</td>\n",
       "      <td>34.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49520</th>\n",
       "      <td>25</td>\n",
       "      <td>39628.99</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>9.50</td>\n",
       "      <td>3</td>\n",
       "      <td>502.38</td>\n",
       "      <td>39.32</td>\n",
       "      <td>31.4</td>\n",
       "      <td>140.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49521</th>\n",
       "      <td>25</td>\n",
       "      <td>39628.99</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>11.50</td>\n",
       "      <td>3</td>\n",
       "      <td>502.38</td>\n",
       "      <td>34.66</td>\n",
       "      <td>31.5</td>\n",
       "      <td>60.97</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49522</th>\n",
       "      <td>25</td>\n",
       "      <td>39628.99</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>11.50</td>\n",
       "      <td>3</td>\n",
       "      <td>502.38</td>\n",
       "      <td>40.57</td>\n",
       "      <td>31.6</td>\n",
       "      <td>54.19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49523 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age    income  #bank  #cc  int.rt  #loan  dfdd  #dp    ccl  #ci  \\\n",
       "0       23  19114.12      3    4       3      4     3    7  11.27    4   \n",
       "1       23  19114.12      3    4       3      4     5    4   6.27    4   \n",
       "2       23  19114.12      3    4       3      4     3    8  11.27    4   \n",
       "3       28  34847.84      2    4       6      1     3    4   5.42    2   \n",
       "4       28  34847.84      2    4       6      1     7    1   7.42    2   \n",
       "...    ...       ...    ...  ...     ...    ...   ...  ...    ...  ...   \n",
       "49518   28  20002.88     10    8      29      5    33   25  18.31    9   \n",
       "49519   29  20002.88     10    8      29      5    33   25  18.31    9   \n",
       "49520   25  39628.99      4    6       7      2    20    6   9.50    3   \n",
       "49521   25  39628.99      4    6       7      2    23    7  11.50    3   \n",
       "49522   25  39628.99      4    6       7      2    18    7  11.50    3   \n",
       "\n",
       "          debt    CUR   CHA     AIM  predicted_cluster  \n",
       "0       809.98  26.82  22.1   80.42                  0  \n",
       "1       809.98  31.38  22.3  199.46                  0  \n",
       "2       809.98  22.54  22.6  178.34                  0  \n",
       "3       605.03  24.46  26.6  104.29                  0  \n",
       "4       605.03  38.55  26.7   40.39                  0  \n",
       "...        ...    ...   ...     ...                ...  \n",
       "49518  3571.70  28.51   6.0  213.98                  0  \n",
       "49519  3571.70  37.14   6.2   34.66                  0  \n",
       "49520   502.38  39.32  31.4  140.58                  0  \n",
       "49521   502.38  34.66  31.5   60.97                  0  \n",
       "49522   502.38  40.57  31.6   54.19                  0  \n",
       "\n",
       "[49523 rows x 15 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a new column to the DataFrame with the predicted clusters\n",
    "df_credit_predictions['predicted_cluster'] = predictions\n",
    "\n",
    "# Display sample data\n",
    "df_credit_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.64239863e+05, -5.74477857e+02, -1.01508573e+01],\n",
       "       [-1.64239863e+05, -5.78211783e+02, -2.08880918e+01],\n",
       "       [-1.64239863e+05, -5.77540134e+02, -1.89831494e+01],\n",
       "       [-1.48506142e+05, -7.80047421e+02, -1.33437846e+01],\n",
       "       [-1.48506142e+05, -7.78025959e+02, -7.57760681e+00]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the PCA model with `fit_transform` to reduce to 3 principal components\n",
    "pca_transformed = pca.fit_transform(df_credit_predictions)\n",
    "\n",
    "# View the first 5 rows of the df\n",
    "pca_transformed[:5]\n",
    "\n",
    "\n",
    "# pca2 contains the transformed data after applying PCA with 3 principal components\n",
    "# each row in the df represents an observation (a crypto) from the original dataset, transformed into the new lower-dimensional space spanned by the principal components\n",
    "# the values in each column represent the coordinates of the transformed data points along the corresponding principal component axis\n",
    "# these values indicate how much each observation contributes to that particular principal component"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9.99999369e-01 5.98798635e-07 1.63690252e-08]\n"
     ]
    }
   ],
   "source": [
    "# display the explained variance ratios - represents the proportion of variance explained by each principal component\n",
    "explained_variance_ratio = pca.explained_variance_ratio_\n",
    "\n",
    "print(explained_variance_ratio)"
   ]
  }
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