Credit_Analysis.ipynb

{
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      "source": [
        "#Credit EDA & Credit Score Calculation\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5wZi20HtMJlw"
      },
      "source": [
        "\n",
        "**Problem statement**\n",
        "\n",
        "To conduct a thorough exploratory data analysis (EDA) and deep analysis of a comprehensive dataset containing basic customer details and extensive credit-related information. The aim is to create new, informative features, calculate a hypothetical credit score, and uncover meaningful patterns, anomalies, and insights within the data.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rynGFlfDOW7f"
      },
      "source": [
        "**Importing Libraries & Loading the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "V2GY6CyZfpYy"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "aDDmBZc9f4Y-"
      },
      "outputs": [],
      "source": [
        "df=pd.read_csv('Credit_score.csv')"
      ]
    },
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            "text/plain": [
              "       ID Customer_ID     Month           Name   Age          SSN Occupation  \\\n",
              "0  0x1602   CUS_0xd40   January  Aaron Maashoh    23  821-00-0265  Scientist   \n",
              "1  0x1603   CUS_0xd40  February  Aaron Maashoh    23  821-00-0265  Scientist   \n",
              "2  0x1604   CUS_0xd40     March  Aaron Maashoh  -500  821-00-0265  Scientist   \n",
              "3  0x1605   CUS_0xd40     April  Aaron Maashoh    23  821-00-0265  Scientist   \n",
              "4  0x1606   CUS_0xd40       May  Aaron Maashoh    23  821-00-0265  Scientist   \n",
              "\n",
              "  Annual_Income  Monthly_Inhand_Salary  Num_Bank_Accounts  ...  \\\n",
              "0      19114.12            1824.843333                  3  ...   \n",
              "1      19114.12                    NaN                  3  ...   \n",
              "2      19114.12                    NaN                  3  ...   \n",
              "3      19114.12                    NaN                  3  ...   \n",
              "4      19114.12            1824.843333                  3  ...   \n",
              "\n",
              "   Num_Credit_Inquiries  Credit_Mix Outstanding_Debt Credit_Utilization_Ratio  \\\n",
              "0                   4.0           _           809.98                26.822620   \n",
              "1                   4.0        Good           809.98                31.944960   \n",
              "2                   4.0        Good           809.98                28.609352   \n",
              "3                   4.0        Good           809.98                31.377862   \n",
              "4                   4.0        Good           809.98                24.797347   \n",
              "\n",
              "      Credit_History_Age Payment_of_Min_Amount Total_EMI_per_month  \\\n",
              "0  22 Years and 1 Months                    No           49.574949   \n",
              "1                    NaN                    No           49.574949   \n",
              "2  22 Years and 3 Months                    No           49.574949   \n",
              "3  22 Years and 4 Months                    No           49.574949   \n",
              "4  22 Years and 5 Months                    No           49.574949   \n",
              "\n",
              "   Amount_invested_monthly                 Payment_Behaviour Monthly_Balance  \n",
              "0              80.41529544   High_spent_Small_value_payments     312.4940887  \n",
              "1              118.2802216    Low_spent_Large_value_payments     284.6291625  \n",
              "2              81.69952126   Low_spent_Medium_value_payments     331.2098629  \n",
              "3              199.4580744    Low_spent_Small_value_payments     223.4513097  \n",
              "4              41.42015309  High_spent_Medium_value_payments      341.489231  \n",
              "\n",
              "[5 rows x 27 columns]"
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              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Customer_ID</th>\n",
              "      <th>Month</th>\n",
              "      <th>Name</th>\n",
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              "      <th>0</th>\n",
              "      <td>0x1602</td>\n",
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              "      <td>January</td>\n",
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              "      <td>312.4940887</td>\n",
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              "      <td>0x1603</td>\n",
              "      <td>CUS_0xd40</td>\n",
              "      <td>February</td>\n",
              "      <td>Aaron Maashoh</td>\n",
              "      <td>23</td>\n",
              "      <td>821-00-0265</td>\n",
              "      <td>Scientist</td>\n",
              "      <td>19114.12</td>\n",
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              "      <td>3</td>\n",
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              "      <td>0x1604</td>\n",
              "      <td>CUS_0xd40</td>\n",
              "      <td>March</td>\n",
              "      <td>Aaron Maashoh</td>\n",
              "      <td>-500</td>\n",
              "      <td>821-00-0265</td>\n",
              "      <td>Scientist</td>\n",
              "      <td>19114.12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Good</td>\n",
              "      <td>809.98</td>\n",
              "      <td>28.609352</td>\n",
              "      <td>22 Years and 3 Months</td>\n",
              "      <td>No</td>\n",
              "      <td>49.574949</td>\n",
              "      <td>81.69952126</td>\n",
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              "      <td>331.2098629</td>\n",
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              "      <th>3</th>\n",
              "      <td>0x1605</td>\n",
              "      <td>CUS_0xd40</td>\n",
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              "      <td>Aaron Maashoh</td>\n",
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              "      <td>19114.12</td>\n",
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              "      <td>31.377862</td>\n",
              "      <td>22 Years and 4 Months</td>\n",
              "      <td>No</td>\n",
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              "      <td>199.4580744</td>\n",
              "      <td>Low_spent_Small_value_payments</td>\n",
              "      <td>223.4513097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0x1606</td>\n",
              "      <td>CUS_0xd40</td>\n",
              "      <td>May</td>\n",
              "      <td>Aaron Maashoh</td>\n",
              "      <td>23</td>\n",
              "      <td>821-00-0265</td>\n",
              "      <td>Scientist</td>\n",
              "      <td>19114.12</td>\n",
              "      <td>1824.843333</td>\n",
              "      <td>3</td>\n",
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              "      <td>809.98</td>\n",
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              "      <td>22 Years and 5 Months</td>\n",
              "      <td>No</td>\n",
              "      <td>49.574949</td>\n",
              "      <td>41.42015309</td>\n",
              "      <td>High_spent_Medium_value_payments</td>\n",
              "      <td>341.489231</td>\n",
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        "df.head()"
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100000, 27)"
            ]
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          "metadata": {},
          "execution_count": 5
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        "df.shape"
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    {
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      "source": [
        "**Observations:**\n",
        "\n",
        "\n",
        "*   The dataset has 1 Lakh rows and 27 columns\n",
        "\n",
        "\n",
        "*   This dataset is about 12,500 people about their income, monthly spendings etc\n",
        "\n",
        "\n"
      ]
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    {
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          "data": {
            "text/plain": [
              "       Monthly_Inhand_Salary  Num_Bank_Accounts  Num_Credit_Card  \\\n",
              "count           84998.000000      100000.000000     100000.00000   \n",
              "mean             4194.170850          17.091280         22.47443   \n",
              "std              3183.686167         117.404834        129.05741   \n",
              "min               303.645417          -1.000000          0.00000   \n",
              "25%              1625.568229           3.000000          4.00000   \n",
              "50%              3093.745000           6.000000          5.00000   \n",
              "75%              5957.448333           7.000000          7.00000   \n",
              "max             15204.633330        1798.000000       1499.00000   \n",
              "\n",
              "       Interest_Rate  Delay_from_due_date  Num_Credit_Inquiries  \\\n",
              "count  100000.000000        100000.000000          98035.000000   \n",
              "mean       72.466040            21.068780             27.754251   \n",
              "std       466.422621            14.860104            193.177339   \n",
              "min         1.000000            -5.000000              0.000000   \n",
              "25%         8.000000            10.000000              3.000000   \n",
              "50%        13.000000            18.000000              6.000000   \n",
              "75%        20.000000            28.000000              9.000000   \n",
              "max      5797.000000            67.000000           2597.000000   \n",
              "\n",
              "       Credit_Utilization_Ratio  Total_EMI_per_month  \n",
              "count             100000.000000        100000.000000  \n",
              "mean                  32.285173          1403.118217  \n",
              "std                    5.116875          8306.041270  \n",
              "min                   20.000000             0.000000  \n",
              "25%                   28.052567            30.306660  \n",
              "50%                   32.305784            69.249473  \n",
              "75%                   36.496663           161.224249  \n",
              "max                   50.000000         82331.000000  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Monthly_Inhand_Salary</th>\n",
              "      <th>Num_Bank_Accounts</th>\n",
              "      <th>Num_Credit_Card</th>\n",
              "      <th>Interest_Rate</th>\n",
              "      <th>Delay_from_due_date</th>\n",
              "      <th>Num_Credit_Inquiries</th>\n",
              "      <th>Credit_Utilization_Ratio</th>\n",
              "      <th>Total_EMI_per_month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>84998.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.00000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>98035.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4194.170850</td>\n",
              "      <td>17.091280</td>\n",
              "      <td>22.47443</td>\n",
              "      <td>72.466040</td>\n",
              "      <td>21.068780</td>\n",
              "      <td>27.754251</td>\n",
              "      <td>32.285173</td>\n",
              "      <td>1403.118217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3183.686167</td>\n",
              "      <td>117.404834</td>\n",
              "      <td>129.05741</td>\n",
              "      <td>466.422621</td>\n",
              "      <td>14.860104</td>\n",
              "      <td>193.177339</td>\n",
              "      <td>5.116875</td>\n",
              "      <td>8306.041270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>303.645417</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1625.568229</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.00000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>28.052567</td>\n",
              "      <td>30.306660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3093.745000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.00000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>32.305784</td>\n",
              "      <td>69.249473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5957.448333</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.00000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>36.496663</td>\n",
              "      <td>161.224249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>15204.633330</td>\n",
              "      <td>1798.000000</td>\n",
              "      <td>1499.00000</td>\n",
              "      <td>5797.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>2597.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>82331.000000</td>\n",
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              "        document.querySelector('#' + key + ' button');\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Monthly_Inhand_Salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28721.020500119503,\n        \"min\": 303.6454167,\n        \"max\": 84998.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4194.170849592996,\n          3093.745,\n          84998.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Num_Bank_Accounts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35262.45571123334,\n        \"min\": -1.0,\n        \"max\": 100000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          17.09128,\n          6.0,\n          100000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Num_Credit_Card\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35274.94819246088,\n        \"min\": 0.0,\n        \"max\": 100000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          22.47443,\n          5.0,\n          100000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Interest_Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35090.285071687635,\n        \"min\": 1.0,\n        \"max\": 100000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          72.46604,\n          13.0,\n          100000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Delay_from_due_date\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35347.57054259866,\n        \"min\": -5.0,\n        \"max\": 100000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          21.06878,\n          18.0,\n          100000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Num_Credit_Inquiries\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34529.0265942048,\n        \"min\": 0.0,\n        \"max\": 98035.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          27.75425103279441,\n          6.0,\n          98035.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_Utilization_Ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35345.02488629802,\n        \"min\": 5.116875070147291,\n        \"max\": 100000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          32.2851725189402,\n          32.30578367,\n          100000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total_EMI_per_month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 41793.58784756242,\n        \"min\": 0.0,\n        \"max\": 100000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1403.1182166160252,\n          69.2494733,\n          100000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "id": "9acSJreXjx1j",
        "outputId": "db0280ac-8846-4dc9-d6e6-10f2038c1a0d"
      },
      "outputs": [
        {
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              "\n",
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            ],
            "text/plain": [
              "            ID Customer_ID    Month    Name        SSN Occupation  \\\n",
              "count   100000      100000   100000  100000     100000     100000   \n",
              "unique  100000       12500        8   10139      12501         16   \n",
              "top     0x1602   CUS_0xd40  January  Langep  #F%$D@*&8    _______   \n",
              "freq         1           8    12500      48       5572       7062   \n",
              "\n",
              "         Type_of_Loan Credit_Mix Outstanding_Debt Payment_of_Min_Amount  \\\n",
              "count          100000     100000           100000                100000   \n",
              "unique           6261          4            13178                     3   \n",
              "top     No_Loan_Taken   Standard          1360.45                   Yes   \n",
              "freq            11408      36479               24                 52326   \n",
              "\n",
              "                     Payment_Behaviour  \n",
              "count                           100000  \n",
              "unique                               7  \n",
              "top     Low_spent_Small_value_payments  \n",
              "freq                             25513  "
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe(include='object')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xl0SdMQmhV4q",
        "outputId": "dc3a74fa-e046-413b-e599-57386096434d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 100000 entries, 0 to 99999\n",
            "Data columns (total 27 columns):\n",
            " #   Column                    Non-Null Count   Dtype  \n",
            "---  ------                    --------------   -----  \n",
            " 0   ID                        100000 non-null  object \n",
            " 1   Customer_ID               100000 non-null  object \n",
            " 2   Month                     100000 non-null  object \n",
            " 3   Name                      90015 non-null   object \n",
            " 4   Age                       100000 non-null  object \n",
            " 5   SSN                       100000 non-null  object \n",
            " 6   Occupation                100000 non-null  object \n",
            " 7   Annual_Income             100000 non-null  object \n",
            " 8   Monthly_Inhand_Salary     84998 non-null   float64\n",
            " 9   Num_Bank_Accounts         100000 non-null  int64  \n",
            " 10  Num_Credit_Card           100000 non-null  int64  \n",
            " 11  Interest_Rate             100000 non-null  int64  \n",
            " 12  Num_of_Loan               100000 non-null  object \n",
            " 13  Type_of_Loan              88592 non-null   object \n",
            " 14  Delay_from_due_date       100000 non-null  int64  \n",
            " 15  Num_of_Delayed_Payment    92998 non-null   object \n",
            " 16  Changed_Credit_Limit      100000 non-null  object \n",
            " 17  Num_Credit_Inquiries      98035 non-null   float64\n",
            " 18  Credit_Mix                100000 non-null  object \n",
            " 19  Outstanding_Debt          100000 non-null  object \n",
            " 20  Credit_Utilization_Ratio  100000 non-null  float64\n",
            " 21  Credit_History_Age        90970 non-null   object \n",
            " 22  Payment_of_Min_Amount     100000 non-null  object \n",
            " 23  Total_EMI_per_month       100000 non-null  float64\n",
            " 24  Amount_invested_monthly   95521 non-null   object \n",
            " 25  Payment_Behaviour         100000 non-null  object \n",
            " 26  Monthly_Balance           98800 non-null   object \n",
            "dtypes: float64(4), int64(4), object(19)\n",
            "memory usage: 20.6+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fh6iAcYDPjdy"
      },
      "source": [
        "**Observations :**\n",
        "\n",
        "\n",
        "*   The dataset has so many errors about incorrectlly identifying the data type etc\n",
        "*   There are lot of null values as well\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zAB3_kOlQEet"
      },
      "source": [
        "**Data Cleaning**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 930
        },
        "id": "LfnKIlMqkOQf",
        "outputId": "25de1ad0-35b9-4d7a-b84a-e2f474df8ef1"
      },
      "outputs": [
        {
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              "      <td>15002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Num_Bank_Accounts</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Num_Credit_Card</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Interest_Rate</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Num_of_Loan</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Type_of_Loan</th>\n",
              "      <td>11408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Delay_from_due_date</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Num_of_Delayed_Payment</th>\n",
              "      <td>7002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Changed_Credit_Limit</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Num_Credit_Inquiries</th>\n",
              "      <td>1965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Credit_Mix</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Outstanding_Debt</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Credit_Utilization_Ratio</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Credit_History_Age</th>\n",
              "      <td>9030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Payment_of_Min_Amount</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total_EMI_per_month</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Amount_invested_monthly</th>\n",
              "      <td>4479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Payment_Behaviour</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Monthly_Balance</th>\n",
              "      <td>1200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ],
            "text/plain": [
              "ID                              0\n",
              "Customer_ID                     0\n",
              "Month                           0\n",
              "Name                         9985\n",
              "Age                             0\n",
              "SSN                             0\n",
              "Occupation                      0\n",
              "Annual_Income                   0\n",
              "Monthly_Inhand_Salary       15002\n",
              "Num_Bank_Accounts               0\n",
              "Num_Credit_Card                 0\n",
              "Interest_Rate                   0\n",
              "Num_of_Loan                     0\n",
              "Type_of_Loan                11408\n",
              "Delay_from_due_date             0\n",
              "Num_of_Delayed_Payment       7002\n",
              "Changed_Credit_Limit            0\n",
              "Num_Credit_Inquiries         1965\n",
              "Credit_Mix                      0\n",
              "Outstanding_Debt                0\n",
              "Credit_Utilization_Ratio        0\n",
              "Credit_History_Age           9030\n",
              "Payment_of_Min_Amount           0\n",
              "Total_EMI_per_month             0\n",
              "Amount_invested_monthly      4479\n",
              "Payment_Behaviour               0\n",
              "Monthly_Balance              1200\n",
              "dtype: int64"
            ]
          },
          "execution_count": 143,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jQ2BQ-YLz0Vy"
      },
      "source": [
        "`Handling Null values `"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "1wksASkPOwI4"
      },
      "outputs": [],
      "source": [
        "df['Name'] = df.groupby('Customer_ID')['Name'].transform(lambda x: x.fillna(x.mode().iloc[0]))\n",
        "df['Monthly_Inhand_Salary']=df.groupby('Customer_ID')['Monthly_Inhand_Salary'].transform(lambda x: x.fillna(x.mean()))\n",
        "df['Type_of_Loan'].fillna('No_Loan_Taken',inplace=True)\n",
        "df['Num_of_Delayed_Payment']=df.groupby('Customer_ID')['Num_of_Delayed_Payment'].transform(lambda x: x.fillna(x.mode().iloc[0]))\n",
        "df['Num_Credit_Inquiries']=df.groupby('Customer_ID')['Num_Credit_Inquiries'].transform(lambda x: x.fillna(x.mode().iloc[0]))\n",
        "df['Credit_History_Age'].replace('NA',np.nan,inplace=True)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "_kYILCHW84cq"
      },
      "outputs": [],
      "source": [
        "df['Credit_Mix'].replace('_',np.nan,inplace=True)\n",
        "df['Credit_Mix']=df.groupby('Customer_ID')['Credit_Mix'].transform(lambda x: x.fillna(x.mode().iloc[0]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "upwN9omk2SkK"
      },
      "outputs": [],
      "source": [
        "def convert_to_datetime(age_str):\n",
        "    # Handle potential non-string values\n",
        "    if not isinstance(age_str, str):\n",
        "        return pd.NaT  # Return NaT (Not a Time) for non-string values\n",
        "\n",
        "    parts = age_str.split(' Years and ')\n",
        "    year = parts[0]\n",
        "\n",
        "    # Calculate total days (approximate) and create Timedelta\n",
        "    year = int(year)   # Approximate days in a month\n",
        "    return year  # Use 'D' for days\n",
        "\n",
        "# Apply the function to the 'Credit_History_Age' column\n",
        "df['Credit_History_Age_in_years'] = df['Credit_History_Age'].apply(convert_to_datetime)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "IPVb5IGA2ioh"
      },
      "outputs": [],
      "source": [
        "df['Credit_History_Age_in_years']=df.groupby('Customer_ID')['Credit_History_Age_in_years'].transform(lambda x: x.fillna(x.max()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "i5LWSAxqGZTY"
      },
      "outputs": [],
      "source": [
        "df['Monthly_Balance'].replace('__-333333333333333333333333333__',np.nan,inplace=True)\n",
        "df['Monthly_Balance']=df['Monthly_Balance'].astype(float)\n",
        "df['Monthly_Balance']=df.groupby('Customer_ID')['Monthly_Balance'].transform(lambda x: x.fillna(x.mean()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "xpUaMM-6G_UF"
      },
      "outputs": [],
      "source": [
        "df['Amount_invested_monthly'].replace('__10000__',np.nan,inplace=True)\n",
        "df['Amount_invested_monthly']=df['Amount_invested_monthly'].astype(float)\n",
        "df['Amount_invested_monthly']=df.groupby('Customer_ID')['Amount_invested_monthly'].transform(lambda x: x.fillna(x.mean()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "2L7eRCBhVAGK"
      },
      "outputs": [],
      "source": [
        "\n",
        "df['Annual_Income'] = df['Annual_Income'].str.extract(r'([\\d.]+)').astype(float)\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1qNCvhmbL9NS"
      },
      "source": [
        "**Note**\n",
        "\n",
        "\n",
        "*   Many Numerical columns have special characters induced in them which can make the analysis difficult\n",
        "*   We have used extract method with regex patterns and removed all the special charactes and changed the data type to int or float from object for numerical columns\n",
        "  \n",
        "*   We have used mode  in most of the cases to fill the null values\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Changing incorrect Datatypes**"
      ],
      "metadata": {
        "id": "KjaVd1Ru9KVI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "l69U6UKrymXh"
      },
      "outputs": [],
      "source": [
        "df['Age'] = df['Age'].str.extract(r'([\\d.]+)').astype(int)\n",
        "df['Num_of_Delayed_Payment']=df['Num_of_Delayed_Payment'].str.extract(r'([\\d.]+)').astype(int)\n",
        "df['Num_of_Loan']=df['Num_of_Loan'].str.extract(r'([\\d.]+)').astype(int)\n",
        "df['Changed_Credit_Limit']=df['Changed_Credit_Limit'].str.extract(r'([\\d.]+)').astype(float)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7K_GNUXX10I8"
      },
      "source": [
        "**Treating Outliers for Numerical Columns**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "Sq6UmpjToFO5"
      },
      "outputs": [],
      "source": [
        "def treat_outliers(df,col,groupby='Customer_ID'):\n",
        "\n",
        "  Q3=np.percentile(df[col],75)\n",
        "  Q1=np.percentile(df[col],25)\n",
        "  IQR=Q3-Q1\n",
        "  lower_bound = Q1 - 1.5 * IQR\n",
        "  upper_bound = Q3 + 1.5 * IQR\n",
        "  df[col]=df.groupby(groupby)[col].transform(lambda x : np.where((x>upper_bound) | (x<lower_bound),x.mode()[0],x))\n",
        "  return None\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "AFS3FBTXmoUw"
      },
      "outputs": [],
      "source": [
        "# clipping outliers using IQR\n",
        "treat_outliers(df,'Age')\n",
        "treat_outliers(df,'Num_of_Delayed_Payment')\n",
        "treat_outliers(df,'Num_of_Loan')\n",
        "treat_outliers(df,'Changed_Credit_Limit')\n",
        "treat_outliers(df,'Num_Credit_Card')\n",
        "treat_outliers(df,'Interest_Rate')\n",
        "treat_outliers(df,'Num_Credit_Inquiries')\n",
        "treat_outliers(df,'Num_Bank_Accounts')\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "5GAAtLOOECyP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "outputId": "064b5a90-b61f-4319-9f92-463e5a40fc8a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Num_Bank_Accounts'>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x700 with 8 Axes>"
            ],
            "image/png": 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BAAAAAAhhMQAAAAAAISwGAAAAACCExQAAAAAAhLAYAAAAAIAQFgMAAAAAEBF1yvvElFJERCxevLjCigGobKt71uoeVhPo10Au0q8BcoN+DZAbytqvyx0WL1myJCIi2rdvX96XAMiaJUuWRJMmTbJdRpXQr4Fcpl8D5Ab9GiA3bKhfF6RyfvxXUlISH3/8cTRq1CgKCgrKXWB1snjx4mjfvn18+OGH0bhx42yXkzXGwRislo/jkFKKJUuWRLt27aJWrZpxJZ7y9ut8WP+WoXqwDNVHLi2Hfl22fp1L67Qi1cTltsw1Y5kjcm+59Wv9uqIZq7IzVmVjnL5V1n5d7jOLa9WqFVtssUV5n16tNW7cuEZvPKsZB2OwWr6NQ00542G1Te3X+bD+LUP1YBmqj1xZDv267HJlnVa0mrjclrnmyKXl1q/LLpfWa7YZq7IzVmVjnMrWr2vGx34AAAAAAKyXsBgAAAAAAGHxdxUVFcWYMWOiqKgo26VklXEwBqsZh5otH9a/ZageLEP1kS/LwX/V1HVaE5fbMtccNXW58531WnbGquyMVdkYp41T7h+4AwAAAAAgfzizGAAAAAAAYTEAAAAAAMJiAAAAAABCWAwAAAAAQNTAsHjs2LGxww47RKNGjaJVq1YxfPjwmD17dqnHLFu2LEaNGhUtWrSIhg0bxn777ReffPJJliquHDfccEP06tUrGjduHI0bN44BAwbEY489lpleE8bg+y655JIoKCiIU045JXNfTRiH8847LwoKCkr9devWLTO9JowBa7ruuutiyy23jHr16kX//v3jpZdeynZJZbahbbq6eu6552Lo0KHRrl27KCgoiEceeaTU9JRS/OY3v4m2bdtGcXFx7LrrrvH2229np9h12NAyHHbYYWusmyFDhmSn2HXIh+OEsizDoEGD1lgXxx57bJYqZlPkcr/eWGXZtvPd2o5X89VHH30UP//5z6NFixZRXFwcPXv2jKlTp2a7rEqzatWqOPfcc6NTp05RXFwcW2+9dVx44YXh9+jzR03q12WVD8e/VSEfjk+rgpyr4tS4sHjixIkxatSoePHFF+Opp56KlStXxm677RZffvll5jG//OUv49FHH40HHnggJk6cGB9//HHsu+++Way64m2xxRZxySWXxLRp02Lq1Knx05/+NIYNGxZvvPFGRNSMMfiul19+OW666abo1atXqftryjj06NEj5s2bl/l74YUXMtNqyhjwX/fdd1+ceuqpMWbMmHjllVeid+/esfvuu8eCBQuyXVqZrW+brq6+/PLL6N27d1x33XVrnX7ZZZfFNddcEzfeeGNMmTIlGjRoELvvvnssW7asiitdtw0tQ0TEkCFDSq2be+65pwor3LB8OE4oyzJERBx11FGl1sVll12WpYopr3zo1xujrNt2vlrX8Wo++uKLL2KnnXaKunXrxmOPPRZvvvlmXHnlldGsWbNsl1ZpLr300rjhhhviD3/4Q8yaNSsuvfTSuOyyy+Laa6/NdmlUgJrWr8sqH45/q0I+HJ9WBTlXBUo13IIFC1JEpIkTJ6aUUlq4cGGqW7dueuCBBzKPmTVrVoqINHny5GyVWSWaNWuWbr311ho3BkuWLEmdO3dOTz31VNpll13SySefnFKqOdvCmDFjUu/evdc6raaMAaXtuOOOadSoUZnbq1atSu3atUtjx47NYlVlt75tOldERHr44Yczt0tKSlKbNm3S5Zdfnrlv4cKFqaioKN1zzz1ZqHDDvr8MKaU0cuTINGzYsKzUU175cJzw/WVIKZXa35G7cr1fb6q1bdv5al3Hq/nqzDPPTD/60Y+yXUaV2muvvdIRRxxR6r599903jRgxIksVUZFqer8ui3w4/q0q+XB8WlVqas61qWrcmcXft2jRooiIaN68eURETJs2LVauXBm77rpr5jHdunWLDh06xOTJk7NSY2VbtWpV3HvvvfHll1/GgAEDatwYjBo1Kvbaa69SyxtRs7aFt99+O9q1axdbbbVVjBgxIj744IOIqFljwLdWrFgR06ZNK7XOa9WqFbvuumtOrfN1bdO5au7cuTF//vxS66VJkybRv3//nFovERETJkyIVq1aRdeuXeO4446Lzz77LNslrVc+HCd8fxlWu/vuu2OzzTaL7bbbLs4+++z46quvslEe5ZQv/XpTrGvbzkfrOl7NV3/7299i++23jwMOOCBatWoVffr0iVtuuSXbZVWqgQMHxvjx42POnDkREfHqq6/GCy+8EHvssUeWK2NT6dflk0/HvxUtH45PK1tNz7k2VZ1sF5BNJSUlccopp8ROO+0U2223XUREzJ8/PwoLC6Np06alHtu6deuYP39+FqqsPDNnzowBAwbEsmXLomHDhvHwww/HtttuGzNmzKgxY3DvvffGK6+8Ei+//PIa02rKttC/f/+44447omvXrjFv3rw4//zz48c//nG8/vrrNWYM+K9PP/00Vq1aFa1bty51f+vWreOtt97KUlUbZ33bdKNGjbJdXrmsfr+tbb3k0ntxyJAhse+++0anTp3i3XffjV/96lexxx57xOTJk6N27drZLm8N+XCcsLZliIj43//93+jYsWO0a9cuXnvttTjzzDNj9uzZ8dBDD2WxWjZGPvTrTbGubTsfre94NV/961//ihtuuCFOPfXU+NWvfhUvv/xynHTSSVFYWBgjR47MdnmV4qyzzorFixdHt27donbt2rFq1aq46KKLYsSIEdkujU1U0/t1eeXL8W9Fy4fj08ok56oYNTosHjVqVLz++us5cS3LytC1a9eYMWNGLFq0KB588MEYOXJkTJw4MdtlVZkPP/wwTj755HjqqaeiXr162S4na757tkKvXr2if//+0bFjx7j//vujuLg4i5VB+axvmz7yyCOzWBkHHXRQ5v979uwZvXr1iq233jomTJgQgwcPzmJla5cPxwnrWoajjz468/89e/aMtm3bxuDBg+Pdd9+NrbfeuqrLhI2WD+/Psqipx6slJSWx/fbbx8UXXxwREX369InXX389brzxxrwNi++///64++67489//nP06NEjZsyYEaecckq0a9cub5cZ2Hg1Zf9XXjU956ooNfYyFCeccEL8/e9/j2effTa22GKLzP1t2rSJFStWxMKFC0s9/pNPPok2bdpUcZWVq7CwMLbZZpvo169fjB07Nnr37h2///3va8wYTJs2LRYsWBB9+/aNOnXqRJ06dWLixIlxzTXXRJ06daJ169Y1Yhy+r2nTptGlS5d45513asy2wH9tttlmUbt27TV+FTaX1/l3t+lctXrs82m9RERstdVWsdlmm1XLdZMPxwnrWoa16d+/f0REtVwXrF0+9uuy2phtO9dt6Hh11apV2S6xUrRt2za23XbbUvd179495y8rtT6nn356nHXWWXHQQQdFz54945BDDolf/vKXMXbs2GyXxiaqyf16U+Tr8e+myIfj08pW03OuilLjwuKUUpxwwgnx8MMPxzPPPBOdOnUqNb1fv35Rt27dGD9+fOa+2bNnxwcffBADBgyo6nKrVElJSSxfvrzGjMHgwYNj5syZMWPGjMzf9ttvHyNGjMj8f00Yh+9bunRpvPvuu9G2bdsasy3wX4WFhdGvX79S67ykpCTGjx+fs+v8u9t0rurUqVO0adOm1HpZvHhxTJkyJWfXS0TEv//97/jss8+q1brJh+OEDS3D2syYMSMiolqtC9YvH/v1hpRn2851GzperY6X8KkIO+20U8yePbvUfXPmzImOHTtmqaLK99VXX0WtWqX/eV67du0oKSnJUkVUlJrYrytCvh7/lkc+HJ9mS03LuSpMVn9eLwuOO+641KRJkzRhwoQ0b968zN9XX32Vecyxxx6bOnTokJ555pk0derUNGDAgDRgwIAsVl3xzjrrrDRx4sQ0d+7c9Nprr6WzzjorFRQUpCeffDKlVDPGYG2+/+vSNWEcTjvttDRhwoQ0d+7cNGnSpLTrrrumzTbbLC1YsCClVDPGgNLuvffeVFRUlO6444705ptvpqOPPjo1bdo0zZ8/P9ullcmGtunqasmSJWn69Olp+vTpKSLSVVddlaZPn57ef//9lFJKl1xySWratGn661//ml577bU0bNiw1KlTp/T1119nufL/Wt8yLFmyJI0ePTpNnjw5zZ07Nz399NOpb9++qXPnzmnZsmXZLj0jH44TNrQM77zzTrrgggvS1KlT09y5c9Nf//rXtNVWW6Wdd945y5WzsXK9X2+ssrw/a4LvH6/mo5deeinVqVMnXXTRRentt99Od999d6pfv37605/+lO3SKs3IkSPT5ptvnv7+97+nuXPnpoceeihtttlm6Ywzzsh2aVSAmtavyyofjn+rQj4cn1YFOVfFqXFhcUSs9W/cuHGZx3z99dfp+OOPT82aNUv169dP++yzT5o3b172iq4ERxxxROrYsWMqLCxMLVu2TIMHD868gVKqGWOwNt8/+K4J43DggQemtm3bpsLCwrT55punAw88ML3zzjuZ6TVhDFjTtddemzp06JAKCwvTjjvumF588cVsl1RmG9qmq6tnn312rfunkSNHppRSKikpSeeee25q3bp1KioqSoMHD06zZ8/ObtHfs75l+Oqrr9Juu+2WWrZsmerWrZs6duyYjjrqqGr3j6R8OE7Y0DJ88MEHaeedd07NmzdPRUVFaZtttkmnn356WrRoUXYLp1xyuV9vrLK8P2uCmhAWp5TSo48+mrbbbrtUVFSUunXrlm6++eZsl1SpFi9enE4++eTUoUOHVK9evbTVVlulc845Jy1fvjzbpVFBalK/Lqt8OP6tCvlwfFoV5FwVpyCllCr+fGUAAAAAAHJJjbtmMQAAAAAAaxIWAwAAAAAgLAYAAAAAQFgMAAAAAEAIiwEAAAAACGExAAAAAAAhLAYAAAAAIITFAFDtnHfeedG6desoKCiIRx55pMrme8cdd0TTpk2rbH7r8t5770VBQUHMmDEj26UA5KVs7WcANiTX+9J5550XP/jBD7JdxjptueWW8bvf/S5zu7LH+/vzK4/qPqb5SFhMlZo8eXLUrl079tprr2yXAuSxww47LAoKCuKSSy4pdf8jjzwSBQUFWaqqbGbNmhXnn39+3HTTTTFv3rzYY4891vv48847LwoKCqKgoCDq1KkTm222Wey8887xu9/9LpYvX15FVWfP6mB59V+LFi1it912i+nTp2e7tApREQfYQMWrafsZ/0gHKsr8+fPjxBNPjK222iqKioqiffv2MXTo0Bg/fny2S8uKxYsXxznnnBPdunWLevXqRZs2bWLXXXeNhx56KFJKlT7/7+4HynPCxob2ES+//HIcffTRm1Tj6NGjS20fhx12WAwfPnyTXpP1ExZTpW677bY48cQT47nnnouPP/442+UAeaxevXpx6aWXxhdffJHtUjbKu+++GxERw4YNizZt2kRRUdEGn9OjR4+YN29efPDBB/Hss8/GAQccEGPHjo2BAwfGkiVLKrvkauHpp5+OefPmxRNPPBFLly6NPfbYIxYuXJjtsoA8VpP2MwAV4b333ot+/frFM888E5dffnnMnDkzHn/88fjJT34So0aNynZ5VW7hwoUxcODAuOuuu+Lss8+OV155JZ577rk48MAD44wzzohFixat9XkrVqyosBoqez/QsmXLqF+//ia9RsOGDaNFixYVVBFlISymyixdujTuu+++OO6442KvvfaKO+64o9T0v/3tb9G5c+eoV69e/OQnP4k777wzCgoKSv1j/4UXXogf//jHUVxcHO3bt4+TTjopvvzyy6pdECAn7LrrrtGmTZsYO3bsWqev7VPw3/3ud7Hllltmbq/+1Priiy+O1q1bR9OmTeOCCy6Ib775Jk4//fRo3rx5bLHFFjFu3Lgy1zVz5sz46U9/GsXFxdGiRYs4+uijY+nSpZmahg4dGhERtWrVKvPZaXXq1Ik2bdpEu3btomfPnnHiiSfGxIkT4/XXX49LL70087jly5fH6NGjY/PNN48GDRpE//79Y8KECet83XfffTeGDRsWrVu3joYNG8YOO+wQTz/9dGb6BRdcENttt90az/vBD34Q5557bub2rbfeGt27d4969epFt27d4vrrry/1+Jdeein69OkT9erVi+23375cZwW3aNEi2rRpE9tvv31cccUV8cknn8SUKVMqZBnKux18+OGH8bOf/SyaNm0azZs3j2HDhsV7772Xmb76da+44opo27ZttGjRIkaNGhUrV66MiIhBgwbF+++/H7/85S8zZ04D1UdN2s+Ud34R355V9v/+3/+LzTbbLJo0aRK77LJLvPLKK6Veo6CgIG699dbYZ599on79+tG5c+f429/+tsm1AdXL8ccfHwUFBfHSSy/FfvvtF126dIkePXrEqaeeGi+++GLmcZ9++uk6+8GqVaviyCOPjE6dOkVxcXF07do1fv/735eaz4aOsSK+PaN2r732iuLi4ujUqVP8+c9/XuMbXQsXLoxf/OIX0bJly2jcuHH89Kc/jVdffbXUvC655JJo3bp1NGrUKI488shYtmxZmcfjV7/6Vbz33nsxZcqUGDlyZGy77bbRpUuXOOqoo2LGjBnRsGHDiPj2m2YXXnhhHHroodG4cePMmbobykcWLFgQQ4cOzSzj3XffvUYN370MRadOnSIiok+fPlFQUBCDBg0q87Ksy9oue3HTTTfF3nvvHfXr14/u3bvH5MmT45133olBgwZFgwYNYuDAgZkPNiNK70/PO++8uPPOO+Ovf/1r5vh4ff+eoXyExVSZ+++/P7p16xZdu3aNn//853H77bdnvlYxd+7c2H///WP48OHx6quvxjHHHBPnnHNOqee/++67MWTIkNhvv/3itddei/vuuy9eeOGFOOGEE7KxOEA1V7t27bj44ovj2muvjX//+9/lfp1nnnkmPv7443juuefiqquuijFjxsTee+8dzZo1iylTpsSxxx4bxxxzTJnm8eWXX8buu+8ezZo1i5dffjkeeOCBePrppzN9bPTo0ZlAYN68eTFv3rxy192tW7fYY4894qGHHsrcd8IJJ8TkyZPj3nvvjddeey0OOOCAGDJkSLz99ttrfY2lS5fGnnvuGePHj4/p06fHkCFDYujQofHBBx9ERMQRRxwRs2bNipdffjnznOnTp8drr70Whx9+eERE3H333fGb3/wmLrroopg1a1ZcfPHFce6558add96Zmcfee+8d2267bUybNi3OO++8GD16dLmXOyKiuLg4Ir4966IiliFi47eDlStXxu677x6NGjWK559/PiZNmhQNGzaMIUOGlDob5Nlnn4133303nn322bjzzjvjjjvuyHyY+tBDD8UWW2wRF1xwwSZvD0DFq+n7mbLMLyJiyZIlMXLkyHjhhRfixRdfjM6dO8eee+65xjdfzj///PjZz34Wr732Wuy5554xYsSI+PzzzzepPqD6+Pzzz+Pxxx+PUaNGRYMGDdaY/t3fzVhfPygpKYktttgiHnjggXjzzTfjN7/5TfzqV7+K+++/v9Trre8YKyLi0EMPjY8//jgmTJgQf/nLX+Lmm2+OBQsWlHqNAw44IBYsWBCPPfZYTJs2Lfr27RuDBw/O1HL//ffHeeedFxdffHFMnTo12rZtu8ZJEetSUlIS9957b4wYMSLatWu3xvSGDRtGnTp1MrevuOKK6N27d0yfPj3OPffcMuUjhx12WHz44Yfx7LPPxoMPPhjXX3/9Gsv4XS+99FJE/Pcbe9/9d0RFWh18z5gxI7p16xb/+7//G8ccc0ycffbZMXXq1EgprTPnGT16dPzsZz+LIUOGZPZjAwcOrJQ6a7QEVWTgwIHpd7/7XUoppZUrV6bNNtssPfvssymllM4888y03XbblXr8OeeckyIiffHFFymllI488sh09NFHl3rM888/n2rVqpW+/vrrSq8fyB0jR45Mw4YNSyml9MMf/jAdccQRKaWUHn744bR61zdmzJjUu3fvUs+7+uqrU8eOHUu9TseOHdOqVasy93Xt2jX9+Mc/ztz+5ptvUoMGDdI999yzwbpuvvnm1KxZs7R06dLMff/4xz9SrVq10vz589eosSzWthyrnXnmmam4uDillNL777+fateunT766KNSjxk8eHA6++yzU0opjRs3LjVp0mS98+vRo0e69tprM7f32GOPdNxxx2Vun3jiiWnQoEGZ21tvvXX685//XOo1LrzwwjRgwICUUko33XRTatGiRak+fsMNN6SISNOnT19vLSmlNHfu3FKP/eKLL9I+++yTGjZsmBnTTV2G8mwHf/zjH1PXrl1TSUlJ5jHLly9PxcXF6Yknnij1ut98803mMQcccEA68MADM7c7duyYrr766g2OA1C17GfKPr/vW7VqVWrUqFF69NFHM/dFRPr1r3+dub106dIUEemxxx4rc51A9TZlypQUEemhhx5a7+PK0w9GjRqV9ttvv8ztDR1jzZo1K0VEevnllzPT33777RQRmeOu559/PjVu3DgtW7as1Ly23nrrdNNNN6WUUhowYEA6/vjjS03v37//Onvmd33yyScpItJVV121wcd27NgxDR8+vNR9G8pHZs+enSIivfTSS5npq5f7u8eWEZEefvjhlNKax9Vlsb59xOravz+/767fyZMnp4hIt912W+a+e+65J9WrV2+d8/juPpjK4cxiqsTs2bPjpZdeioMPPjgivv3K9IEHHhi33XZbZvoOO+xQ6jk77rhjqduvvvpq3HHHHdGwYcPM3+677x4lJSUxd+7cqlkQIOdceumlceedd8asWbPK9fwePXpErVr/3V22bt06evbsmbldu3btaNGixXo/pV9t1qxZ0bt371JnU+y0005RUlISs2fPLld965NSynzFeObMmbFq1aro0qVLqT46ceLEUl/z+q6lS5fG6NGjo3v37tG0adNo2LBhzJo1K3NWbkTEUUcdFffcc08sW7YsVqxYEX/+85/jiCOOiIhvzzh7991348gjjyw1z9/+9reZec6aNSt69eoV9erVy7zmgAEDNnpZBw4cGA0bNoxmzZrFq6++Gvfdd1+0bt16k5dhtY3dDl599dV45513olGjRpnlbt68eSxbtqzUePfo0SNq166dud22bdsybUtA9VGT9zNlmd8nn3wSRx11VHTu3DmaNGkSjRs3jqVLl5bqwxERvXr1yvx/gwYNonHjxvoh5JG0ET/WtqF+cN1110W/fv2iZcuW0bBhw7j55pvX6CnrO8aaPXt21KlTJ/r27ZuZvs0220SzZs0yt1999dVYunRptGjRotRx7Ny5c0sdx/bv37/UfMt6HLsx4xERsf3225e6vaF8ZNasWVGnTp3o169f5jndunUrdQZ3tnx3/bZu3ToiotR+r3Xr1rFs2bJYvHhxldfGt+ps+CGw6W677bb45ptvSn29IqUURUVF8Yc//KFMr7F06dI45phj4qSTTlpjWocOHSqsViC/7LzzzrH77rvH2WefHYcddljm/lq1aq1xkPbd65itVrdu3VK3CwoK1npfSUlJxRVdQWbNmpW59tjSpUujdu3aMW3atFIHzhGRuR7a940ePTqeeuqpuOKKK2KbbbaJ4uLi2H///UtdRmHo0KFRVFQUDz/8cBQWFsbKlStj//33z8wzIuKWW25Z40D6+zVsqvvuuy+23XbbaNGiRamD4E1dhtU2djtYunRp9OvXb63XhmvZsuV6X7c6bkvAutXk/UxZjBw5Mj777LP4/e9/Hx07doyioqIYMGDAGj/QlE/LDKypc+fOUVBQEG+99dYGH7u+fnDvvffG6NGj48orr4wBAwZEo0aN4vLLL48pU6aU+TXKYunSpdG2bdu1Xg+3IgLXli1bRtOmTcs0HhGxxqU7NpSPzJkzZ5NrrCzfXTerT2xZ2332AdkjLKbSffPNN3HXXXfFlVdeGbvttlupacOHD4977rknunbtGv/3f/9Xatp3rx8ZEdG3b9948803Y5tttqn0moH8cskll8QPfvCD6Nq1a+a+li1bxvz580udfTtjxoxKraN79+5xxx13xJdffpk54Js0aVLUqlWrVG0V4a233orHH388zj777Ij49ocqVq1aFQsWLIgf//jHZXqNSZMmxWGHHRb77LNPRHx7UPrdH2iL+PabIiNHjoxx48ZFYWFhHHTQQZlrBrdu3TratWsX//rXv2LEiBFrnUf37t3jj3/8YyxbtixzdvF3f+CkrNq3bx9bb711hS9DefXt2zfuu+++aNWqVTRu3Ljcr1NYWBirVq3apFqAylcT9zNlnd+kSZPi+uuvjz333DMivv3xz08//bTCawGqt+bNm8fuu+8e1113XZx00klrhJ8LFy4sUwg7adKkGDhwYBx//PGZ+9b1Lbl16dq1a3zzzTcxffr0zJm377zzTnzxxReZx/Tt2zfmz58fderUKfXDpN/VvXv3mDJlShx66KGZ+8p6HFurVq046KCD4o9//GOMGTNmjesWL126NOrVq1fqusXftaF8pFu3bvHNN9/EtGnTMt/inj17dixcuHCdNRUWFkZEVPtjT8fHlc9lKKh0f//73+OLL76II488MrbbbrtSf/vtt1/cdtttccwxx8Rbb70VZ555ZsyZMyfuv//+zMXnVx9cn3nmmfHPf/4zTjjhhJgxY0a8/fbb8de//tUP3AEb1LNnzxgxYkRcc801mfsGDRoU//nPf+Kyyy6Ld999N6677rp47LHHKrWOESNGRL169WLkyJHx+uuvx7PPPhsnnnhiHHLIIZmvYJXHN998E/Pnz4+PP/44Zs6cGddee23ssssu8YMf/CBOP/30iIjo0qVLjBgxIg499NB46KGHYu7cufHSSy/F2LFj4x//+MdaX7dz587x0EMPxYwZM+LVV1+N//3f/13rJ/y/+MUv4plnnonHH398jcs3nH/++TF27Ni45pprYs6cOTFz5swYN25cXHXVVRER8b//+79RUFAQRx11VLz55pvxf//3f3HFFVeUeywqYxnKY8SIEbHZZpvFsGHD4vnnn4+5c+fGhAkT4qSTTtqoH8Lacsst47nnnouPPvpIuALVWL7vZ77++uuYMWNGqb933323TPPr3Llz/PGPf4xZs2bFlClTYsSIEZv8gRyQm6677rpYtWpV7LjjjvGXv/wl3n777Zg1a1Zcc801Zb58Q+fOnWPq1KnxxBNPxJw5c+Lcc89d40SzDenWrVvsuuuucfTRR8dLL70U06dPj6OPPjqKi4sz+cOuu+4aAwYMiOHDh8eTTz4Z7733Xvzzn/+Mc845J6ZOnRoRESeffHLcfvvtMW7cuJgzZ06MGTMm3njjjTLXcdFFF0X79u2jf//+cdddd8Wbb74Zb7/9dtx+++3Rp0+fzLf01mZD+UjXrl1jyJAhccwxx8SUKVNi2rRp8Ytf/GK9/bdVq1ZRXFwcjz/+eHzyySexaNGiMi3HuvYRlWXLLbeM1157LWbPnh2ffvrpWr+1w6YRFlPpbrvttth1112jSZMma0zbb7/9YurUqbFkyZJ48MEH46GHHopevXrFDTfcEOecc05ERBQVFUXEt9e1mThxYsyZMyd+/OMfR58+feI3v/nNWn85FOD7LrjgglIhYffu3eP666+P6667Lnr37h0vvfRSjB49ulJrqF+/fjzxxBPx+eefxw477BD7779/DB48uMyX41mXN954I9q2bRsdOnSIQYMGxf333x9nn312PP/886UuMTFu3Lg49NBD47TTTouuXbvG8OHD4+WXX17npXyuuuqqaNasWQwcODCGDh0au+++e6lru63WuXPnGDhwYHTr1m2Ny0384he/iFtvvTXGjRsXPXv2jF122SXuuOOOzOUxGjZsGI8++mjMnDkz+vTpE+ecc05ceumlmzQeFb0M5VG/fv147rnnokOHDrHvvvtG9+7d48gjj4xly5Zt1JnGF1xwQbz33nux9dZbl7p8BVD95PN+Zs6cOdGnT59Sf8ccc0yZ5nfbbbfFF198EX379o1DDjkkTjrppGjVqtWmLiqQg7baaqt45ZVX4ic/+Umcdtppsd1228X/+3//L8aPHx833HBDmV7jmGOOiX333TcOPPDA6N+/f3z22WelzjIuq7vuuitat24dO++8c+yzzz5x1FFHRaNGjTLfdCsoKIj/+7//i5133jkOP/zw6NKlSxx00EHx/vvvZz4MO/DAA+Pcc8+NM844I/r16xfvv/9+HHfccWWuoXnz5vHiiy/Gz3/+8/jtb38bffr0iR//+Mdxzz33xOWXX77WDGW1suQj48aNi3bt2sUuu+wS++67bxx99NHr7b916tSJa665Jm666aZo165dDBs2rEzLsa59RGU56qijomvXrrH99ttHy5YtY9KkSZU2r5qqIG3sVbWhilx00UVx4403xocffpjtUgBYj5RSdO7cOY4//vg49dRTs11OueTDMgAAUD7//ve/o3379vH000/H4MGDs10OZJVrFlNtXH/99bHDDjtEixYtYtKkSXH55Ze7xARANfef//wn7r333pg/f34cfvjh2S6nXPJhGQAAKLtnnnkmli5dGj179ox58+bFGWecEVtuuWXsvPPO2S4Nss5lKKg23n777Rg2bFhsu+22ceGFF8Zpp50W5513XrbLAiiziy++OBo2bLjWvz322KNcr7mu12vYsGE8//zzFbwEG69Vq1ZxwQUXxM033xzNmjWr8Nc/9thj17n8xx57bIXMo7KXAaCi1MT9DEBlWLlyZfzqV7+KHj16xD777BMtW7aMCRMmRN26dStsHvnQX/NhGdh4LkMBABXk888/j88//3yt04qLi2PzzTff6Nd855131jlt8803z/sfCVqwYEEsXrx4rdMaN27supdAjWI/A5A78qG/5sMysPGExQAAAAAAuAwFAAAAAADCYgAAAAAAQlgMAAAAAEBE1CnvE0tKSuLjjz+ORo0aRUFBQUXWBFBpUkqxZMmSaNeuXdSqVTM+L9OvgVykX+vXQG7Qr/VrIDeUtV+XOyz++OOPo3379uV9OkBWffjhh7HFFltku4wqoV8DuUy/BsgN+jVAbthQvy53WNyoUaPMDBo3blzelwGoUosXL4727dtnelhNoF8DuUi/1q+B3KBf69dAbihrvy53WLz6qxaNGzfWHIGcU5O+LqZfA7lMvwbIDfo1QG7YUL+uGRcUAgAAAABgvYTFAAAAAAAIiwEAAAAAEBYDAAAAABDCYgAAAAAAQlgMAAAAAEAIiwEAAAAACGExAAAAAAAhLAYAAAAAIITFAAAAAACEsBgAAAAAgBAWAwAAAAAQwmIAAAAAAEJYDAAAAABACIsBAAAAAAhhMQAAAAAAISwGAAAAACCExQAAAAAAhLAYAAAAAIAQFgMAAAAAEBF1sl0A2ffJJ5/EokWLsl1GTmjSpEm0bt0622UAG0mfyy16LdQ8+vSm0zsBSrNvqVr2Q/lDWFzDffLJJ/HzQw6NlSuWZ7uUnFC3sCj+9Me7NEDIIfpc7tFroWbRpyuG3gnwX/YtVc9+KH8Ii2u4RYsWxcoVy+PrrXaJknpNKmUetb5eGMVzn4uvO+0cJcVNK2UeVaHWskUR/5oYixYt0vwgh1RFn6sK+dJLN0SvhZqnOvTpXO+xeidAadVh31Ieubo/sh/KL8JiIiKipF6TKGmwWeXOo7hppc8DYF2qos9VBb0UyFfVoU/rsQD5pTrsW8rD/ohs8gN3AAAAAAAIiwEAAAAAEBYDAAAAABDCYgAAAAAAQlgMAAAAAEAIiwEAAAAACGExAAAAAAAhLAYAAAAAIITFAAAAAACEsBgAAAAAgBAWAwAAAAAQwmIAAAAAAEJYDAAAAABACIsBAAAAAAhhMQAAAAAAISwGAAAAACCExQAAAAAAhLAYAAAAAIAQFgMAAAAAEMJiAAAAAABCWAwAAAAAQAiLAQAAAAAIYTEAAAAAACEsBgAAAAAghMUAAAAAAISwGAAAAACAEBYDAAAAABDCYgAAAAAAQlgMAAAAAEAIiwEAAAAACGExAAAAAAAhLAYAAAAAIITFAAAAAACEsBgAAAAAgBAWAwAAAAAQwmIAAAAAAEJYDAAAAABACIsBAAAAAAhhMQAAAAAAISwGAAAAACCExQAAAAAAhLAYAAAAAIAQFgMAAAAAEMJiAAAAAABCWAwAAAAAQGQhLF62bFnMmTMnli1bVtWzBvKIXlL5jDFQEfSSymV8IX94P+c36xeoKJXdT6o8LP7ggw/i6KOPjg8++KCqZw3kEb2k8hljoCLoJZXL+EL+8H7Ob9YvUFEqu5+4DAUAAAAAAMJiAAAAAACExQAAAAAAhLAYAAAAAIAQFgMAAAAAEMJiAAAAAABCWAwAAAAAQAiLAQAAAAAIYTEAAAAAACEsBgAAAAAghMUAAAAAAISwGAAAAACAEBYDAAAAABDCYgAAAAAAQlgMAAAAAEAIiwEAAAAACGExAAAAAAAhLAYAAAAAIITFAAAAAACEsBgAAAAAgBAWAwAAAAAQwmIAAAAAAEJYDAAAAABACIsBAAAAAAhhMQAAAAAAISwGAAAAACCExQAAAAAAhLAYAAAAAIAQFgMAAAAAEMJiAAAAAABCWAwAAAAAQAiLAQAAAAAIYTEAAAAAACEsBgAAAAAghMUAAAAAAISwGAAAAACAEBYDAAAAABDCYgAAAAAAQlgMAAAAAEAIiwEAAAAACGExAAAAAAAhLAYAAAAAIITFAAAAAABERJ1sFwAAAACU36BBg9a4b8KECVX2fADyhzOLAQAAIEetLehd3/0V/XwA8ouwGAAAAHLQhgLdyp4OQP4RFgMAAECO+X6QO2HChMzf+h5XUc8HID+V+ZrFy5cvj+XLl2duL168eJNm/P7772/S86kY1sPGM2bVg/Wwbvp1ablef01lveUP63LdKrJf5/I453Lt1Y2xzG2bsv6+H/BOmDBho0LeTX1+vtOvc49xzg7jXjUqe5zLHBaPHTs2zj///Aqb8UUXXVRhrwVVybZLdadfkw9sd9QEFdmvvWeIsB1AZdGvoWxs3/mhzGHx2WefHaeeemrm9uLFi6N9+/blnvE555wTHTt2LPfzqRjvv/++N/NGsu1WD7bdddOvS7Ot5KZc3+74L+/BdavIfp3L7xnbSMXJ5e0A74XqTL/OPd5P2WH7rhqVvX2XOSwuKiqKoqKiCptxx44do0uXLhX2elBVbLtUd/o1+cB2R01Qkf3ae4YI20FNNmjQoFKXktjYS0hs6vPznX4NZWP7zg9lDosBAACA6uH71xVeV8D7/esRV9TzAchPtbJdAAAAALDxNhTkVvZ0APKPsBgAAABy1PrOHK6K5wOQX1yGAgAAAHLYpga7gmEAVnNmMQAAAAAAwmIAAAAAAITFAAAAAACEsBgAAAAAgBAWAwAAAAAQwmIAAAAAAEJYDAAAAABACIsBAAAAAAhhMQAAAAAAISwGAAAAACCExQAAAAAAhLAYAAAAAIAQFgMAAAAAEMJiAAAAAABCWAwAAAAAQAiLAQAAAAAIYTEAAAAAACEsBgAAAAAghMUAAAAAAISwGAAAAACAEBYDAAAAABDCYgAAAAAAQlgMAAAAAEAIiwEAAAAACGExAAAAAAAhLAYAAAAAIITFAAAAAACEsBgAAAAAgBAWAwAAAAAQwmIAAAAAAEJYDAAAAABACIsBAAAAAAhhMQAAAAAAISwGAAAAACCExQAAAAAAhLAYAAAAAIAQFgMAAAAAEMJiAAAAAABCWAwAAAAAQAiLAQAAAAAIYTEAAAAAACEsBgAAAAAghMUAAAAAAEQWwuIOHTrEzTffHB06dKjqWQN5RC+pfMYYqAh6SeUyvpA/vJ/zm/ULVJTK7id1KuVV16NevXrRpUuXqp4tkGf0kspnjIGKoJdULuML+cP7Ob9Zv0BFqex+4jIUAAAAAAAIiwEAAAAAEBYDAAAAABDCYgAAAAAAQlgMAAAAAEAIiwEAAAAACGExAAAAAAAhLAYAAAAAIITFAAAAAACEsBgAAAAAgBAWAwAAAAAQwmIAAAAAAEJYDAAAAABACIsBAAAAAAhhMQAAAAAAISwGAAAAACCExQAAAAAAhLAYAAAAAIAQFgMAAAAAEMJiAAAAAABCWAwAAAAAQAiLAQAAAAAIYTEAAAAAACEsBgAAAAAghMUAAAAAAISwGAAAAACAEBYDAAAAABDCYgAAAAAAQlgMAAAAAEAIiwEAAAAACGExAAAAAAAhLAYAAAAAIITFAAAAAACEsBgAAAAAgBAWAwAAAAAQwmIAAAAAAEJYDAAAAABACIsBAAAAAAhhMQAAAAAAISwGAAAAACCExQAAAAAAhLAYAAAAAIAQFgMAAAAAEMJiAAAAAABCWAwAAAAAQETUyXYBVA+1li2qvNf+emGp/+aqyhwjoPLl+ns4X3rphuT6egLKL5vv/1zvsXonwNrlWn/M1f1Rro0z6ycsruGaNGkSdQuLIv41sdLnVTz3uUqfR2WrW1gUTZo0yXYZwEaoyj5XFfKhl26IXgs1S3Xq07ncY/VOgP+qTvuW8sjF/ZH9UP4QFtdwrVu3jj/98a5YtMinQGXRpEmTaN26dbbLADaCPpd79FqoWfTpiqF3AvyXfUvVsx/KH8JionXr1t7QQF7T5wCqN30agIpm3wLl4wfuAAAAAAAQFgMAAAAAICwGAAAAACCExQAAAAAAhLAYAAAAAIAQFgMAAAAAEMJiAAAAAABCWAwAAAAAQAiLAQAAAAAIYTEAAAAAACEsBgAAAAAghMUAAAAAAISwGAAAAACAEBYDAAAAABDCYgAAAAAAQlgMAAAAAEAIiwEAAAAACGExAAAAAAAhLAYAAAAAIITFAAAAAACEsBgAAAAAgIioU94nppQiImLx4sUVVgxAZVvds1b3sJpAvwZykX4NkBv0a4DcUNZ+Xe6weMmSJRER0b59+/K+BEDWLFmyJJo0aZLtMqqEfg3kMv0aIDfo1wC5YUP9uiCV8+O/kpKS+Pjjj6NRo0ZRUFBQ5uctXrw42rdvHx9++GE0bty4PLOuFixH9WI5qpfqvBwppViyZEm0a9cuatWqGVfiWVu/rs7rKBcZz4pjLCtWLo+nfl224+tcXsfViXHcdMZw0+XqGOrXZc9DKkuubjvqrlrqrlrVse6y9utyn1lcq1at2GKLLcr79GjcuHG1GaxNYTmqF8tRvVTX5agpZzystr5+XV3XUa4ynhXHWFasXB1P/brscnUdVzfGcdMZw02Xi2OoX1cPubjtRKi7qqm7alW3usvSr2vGx34AAAAAAKyXsBgAAAAAgKoPi4uKimLMmDFRVFRU1bOuUJajerEc1Uu+LEc+s44qlvGsOMayYhnP/GcdVwzjuOmM4aYzhpRXrm476q5a6q5auVp3xCb8wB0AAAAAAPnDZSgAAAAAABAWAwAAAAAgLAYAAAAAIITFAAAAAABEFYfF1113XWy55ZZRr1696N+/f7z00ktVOftNNnbs2Nhhhx2iUaNG0apVqxg+fHjMnj0722VtsksuuSQKCgrilFNOyXYp5fLRRx/Fz3/+82jRokUUFxdHz549Y+rUqdkua6OsWrUqzj333OjUqVMUFxfH1ltvHRdeeGFU99+ffO6552Lo0KHRrl27KCgoiEceeaTU9JRS/OY3v4m2bdtGcXFx7LrrrvH2229np1hKyfV+nA2294pVln3qsmXLYtSoUdGiRYto2LBh7LfffvHJJ59kqeLq64YbbohevXpF48aNo3HjxjFgwIB47LHHMtONY37Tz8vvvPPOi4KCglJ/3bp1y3ZZ1Zp94abb0Bgedthha2yXQ4YMyU6x5Ixc2xfkS7aSS1lKLuYmuZKT5OO+scrC4vvuuy9OPfXUGDNmTLzyyivRu3fv2H333WPBggVVVcImmzhxYowaNSpefPHFeOqpp2LlypWx2267xZdffpnt0srt5Zdfjptuuil69eqV7VLK5Ysvvoiddtop6tatG4899li8+eabceWVV0azZs2yXdpGufTSS+OGG26IP/zhDzFr1qy49NJL47LLLotrr70226Wt15dffhm9e/eO6667bq3TL7vssrjmmmvixhtvjClTpkSDBg1i9913j2XLllVxpXxXPvTjbLC9V6yy7FN/+ctfxqOPPhoPPPBATJw4MT7++OPYd999s1h19bTFFlvEJZdcEtOmTYupU6fGT3/60xg2bFi88cYbEWEc85l+vul69OgR8+bNy/y98MIL2S6pWrMv3HQbGsOIiCFDhpTaLu+5554qrJBck4v7gnzIVnIpS8nV3CRXcpK83DemKrLjjjumUaNGZW6vWrUqtWvXLo0dO7aqSqhwCxYsSBGRJk6cmO1SymXJkiWpc+fO6amnnkq77LJLOvnkk7Nd0kY788wz049+9KNsl7HJ9tprr3TEEUeUum/fffdNI0aMyFJFGy8i0sMPP5y5XVJSktq0aZMuv/zyzH0LFy5MRUVF6Z577slChayWj/24qtneK97396kLFy5MdevWTQ888EDmMbNmzUoRkSZPnpytMnNGs2bN0q233moc85x+vmnGjBmTevfune0ycpZ94ab7/himlNLIkSPTsGHDslIPuSkf9gW5lq3kWpaSq7lJLuYk+bJvrJIzi1esWBHTpk2LXXfdNXNfrVq1Ytddd43JkydXRQmVYtGiRRER0bx58yxXUj6jRo2Kvfbaq9R6yTV/+9vfYvvtt48DDjggWrVqFX369Ilbbrkl22VttIEDB8b48eNjzpw5ERHx6quvxgsvvBB77LFHlisrv7lz58b8+fNLbV9NmjSJ/v375/T7Ptflaz/ONtv7pvv+PnXatGmxcuXKUmParVu36NChgzFdj1WrVsW9994bX375ZQwYMMA45jH9vGK8/fbb0a5du9hqq61ixIgR8cEHH2S7pJxlX1hxJkyYEK1atYquXbvGcccdF5999lm2S6Kaypd9Qa5lK7mWpeRqbpIPOUmu7hvrVMVMPv3001i1alW0bt261P2tW7eOt956qypKqHAlJSVxyimnxE477RTbbbddtsvZaPfee2+88sor8fLLL2e7lE3yr3/9K2644YY49dRT41e/+lW8/PLLcdJJJ0VhYWGMHDky2+WV2VlnnRWLFy+Obt26Re3atWPVqlVx0UUXxYgRI7JdWrnNnz8/ImKt7/vV06h6+diPqwPb+6ZZ2z51/vz5UVhYGE2bNi31WGO6djNnzowBAwbEsmXLomHDhvHwww/HtttuGzNmzDCOeUo/33T9+/ePO+64I7p27Rrz5s2L888/P3784x/H66+/Ho0aNcp2eTnHvrBiDBkyJPbdd9/o1KlTvPvuu/GrX/0q9thjj5g8eXLUrl072+VRzeTDviDXspVczFJyNTfJh5wkV/eNVRIW56NRo0bF66+/npPXNfvwww/j5JNPjqeeeirq1auX7XI2SUlJSWy//fZx8cUXR0REnz594vXXX48bb7yxWje977v//vvj7rvvjj//+c/Ro0ePmDFjRpxyyinRrl27nFoOgPLI5X1qddG1a9eYMWNGLFq0KB588MEYOXJkTJw4MdtlQbX23TOTevXqFf3794+OHTvG/fffH0ceeWQWK6MmO+iggzL/37Nnz+jVq1dsvfXWMWHChBg8eHAWK4PKkUvHgbmapeRqbiInyZ4quQzFZpttFrVr117jl7c/+eSTaNOmTVWUUKFOOOGE+Pvf/x7PPvtsbLHFFtkuZ6NNmzYtFixYEH379o06depEnTp1YuLEiXHNNddEnTp1YtWqVdkusczatm0b2267ban7unfvnnNfITz99NPjrLPOioMOOih69uwZhxxySPzyl7+MsWPHZru0clv93s6X932+yLd+XF3Y3stvXfvUNm3axIoVK2LhwoWlHm9M166wsDC22Wab6NevX4wdOzZ69+4dv//9741jHtPPK17Tpk2jS5cu8c4772S7lJxkX1g5ttpqq9hss81sl6xVru8Lci1bydUsJVdzk3zISXJ131glYXFhYWH069cvxo8fn7mvpKQkxo8fHwMGDKiKEipESilOOOGEePjhh+OZZ56JTp06Zbukchk8eHDMnDkzZsyYkfnbfvvtY8SIETFjxoyc+nrTTjvtFLNnzy5135w5c6Jjx45Zqqh8vvrqq6hVq/TbsXbt2lFSUpKlijZdp06dok2bNqXe94sXL44pU6bk1Ps+3+RLP65ubO8bb0P71H79+kXdunVLjens2bPjgw8+MKZlUFJSEsuXLzeOeUw/r3hLly6Nd999N9q2bZvtUnKSfWHl+Pe//x2fffaZ7ZK1ytV9Qa5mK7mapeRqbpIPOUnO7hur6pf07r333lRUVJTuuOOO9Oabb6ajjz46NW3aNM2fP7+qSthkxx13XGrSpEmaMGFCmjdvXubvq6++ynZpmywXfsFzbV566aVUp06ddNFFF6W333473X333al+/frpT3/6U7ZL2ygjR45Mm2++efr73/+e5s6dmx566KG02WabpTPOOCPbpa3XkiVL0vTp09P06dNTRKSrrroqTZ8+Pb3//vsppZQuueSS1LRp0/TXv/41vfbaa2nYsGGpU6dO6euvv85y5TVbPvTjbLC9V6yy7FOPPfbY1KFDh/TMM8+kqVOnpgEDBqQBAwZkserq6ayzzkoTJ05Mc+fOTa+99lo666yzUkFBQXryySdTSsYxn+nnm+a0005LEyZMSHPnzk2TJk1Ku+66a9pss83SggULsl1atWVfuOnWN4ZLlixJo0ePTpMnT05z585NTz/9dOrbt2/q3LlzWrZsWbZLp5rKxX1BPmUruZCl5Gpukis5ST7uG6ssLE4ppWuvvTZ16NAhFRYWph133DG9+OKLVTn7TRYRa/0bN25ctkvbZLnQ4Nbl0UcfTdttt10qKipK3bp1SzfffHO2S9poixcvTieffHLq0KFDqlevXtpqq63SOeeck5YvX57t0tbr2WefXet7YuTIkSmllEpKStK5556bWrdunYqKitLgwYPT7Nmzs1s0KaXc78fZYHuvWGXZp3799dfp+OOPT82aNUv169dP++yzT5o3b172iq6mjjjiiNSxY8dUWFiYWrZsmQYPHpwJilMyjvlOPy+/Aw88MLVt2zYVFhamzTffPB144IHpnXfeyXZZ1Zp94aZb3xh+9dVXabfddkstW7ZMdevWTR07dkxHHXVUtQ79qB5ybV+QT9lKrmQpuZib5EpOko/7xoKUUqr485UBAAAAAMglVXLNYgAAAAAAqjdhMQAAAAAAwmIAAAAAAITFAAAAAACEsBgAAAAAgBAWAwAAAAAQwmIAAAAAAEJYTI664447omnTppnb5513XvzgBz/IWj3lMWHChCgoKIiFCxdmuxQAAPJIPhwrrzZo0KA45ZRT1vuY9957LwoKCmLGjBlVUhNARdC7qK6ExTnksMMOi4KCgrjkkktK3f/II49EQUFBlqr6r2effTb23HPPaNGiRdSvXz+23XbbOO200+Kjjz6q9HmPHj06xo8fn7l92GGHxfDhwzf6dbK5DAARG9+/CgoK4pFHHqm0ejbGlltuGb/73e/K/PjVH5qt/mvZsmXsueeeMXPmzI2a7/dDEaBmcqy8bhVxrJyt/c1DDz0UF1544Xof0759+5g3b15st912VVQVsKmqc8/+7vFpnTp1okOHDnHqqafG8uXLs1rXuowdOzZq164dl19+ebZLKZdc/kAzXwmLc0y9evXi0ksvjS+++CLbpZRy0003xa677hpt2rSJv/zlL/Hmm2/GjTfeGIsWLYorr7xyrc9ZtWpVlJSUVMj8GzZsGC1atNik1yjPMpTFihUrNqkugKqwcuXKrM179uzZMW/evHjiiSdi+fLlsddee+mdQLk4Vl67ijhWzpbmzZtHo0aN1jl9xYoVUbt27WjTpk3UqVOnCisDNlV17dkREePGjYt58+bF3Llz4/rrr48//vGP8dvf/jbbZa3V7bffHmeccUbcfvvt2S6FfJHIGSNHjkx777136tatWzr99NMz9z/88MNp9aocM2ZM6t27d6nnXX311aljx46lXmfYsGHpoosuSq1atUpNmjRJ559/flq5cmUaPXp0atasWdp8883T7bffXqa6Pvzww1RYWJhOOeWUtU7/4osvUkopjRs3LjVp0iT99a9/Td27d0+1a9dOc+fOTcuWLUunnXZaateuXapfv37acccd07PPPlvqNcaNG5fat2+fiouL0/Dhw9MVV1yRmjRpkpn+3eUeM2ZMiohSf99/vfIuw6effpoOOuig1K5du1RcXJy222679Oc//7nUY3fZZZc0atSodPLJJ6cWLVqkQYMGpZRS+sc//pE6d+6c6tWrlwYNGpTGjRuXIiLz2gAp/bdHp/RtPznxxBPT6aefnpo1a5Zat26dxowZk3lsx44dS/W67/b6Rx55JPXp0ycVFRWlTp06pfPOOy+tXLkyMz0i0vXXX5+GDh2a6tevn3nd9T2vpKQkjRkzJrVv3z4VFhamtm3bphNPPDFT6/d774Y8++yza/TBv/3tbyki0quvvpq578orr0zbbbddql+/ftpiiy3Scccdl5YsWVLqNb77t3pZyrJ/AfKHY+XKO1ZO6dv9xsMPP5xSSmnu3LkpItJf/vKXNGjQoFRcXJx69eqV/vnPf25UXd/d56128sknp1122SVze5dddkknn3xy5nbHjh3TBRdckA455JDUqFGjNHLkyEw906dPzzxu5syZaciQIalBgwapVatW6ec//3n6z3/+k5n+wAMPpO222y7Vq1cvNW/ePA0ePDgtXbp0g+MAVIzq2rNTKt3vVjvyyCPTnnvumbn9zjvvpP/5n/9JrVq1Sg0aNEjbb799euqpp0o9p2PHjumiiy5Khx9+eGrYsGFq3759uummmzLTv9+7vvnmm3T44Yenrl27pvfff79MtU6YMCFtvvnmacWKFaldu3Zp0qRJpaavWrUqXXrppWnrrbdOhYWFqX379um3v/1tZvqHH36YDjrooNSsWbNUv3791K9fv/Tiiy9mpl9//fVpq622SnXr1k1dunRJd9111zrrT+nbfdp39yurj9Wffvrp1K9fv1RcXJwGDBiQ3nrrrZRSyuQi3/0bN27cev/dQeVzZnGOqV27dlx88cVx7bXXxr///e9yv84zzzwTH3/8cTz33HNx1VVXxZgxY2LvvfeOZs2axZQpU+LYY4+NY445pkzzeOCBB2LFihVxxhlnrHX6d78a/NVXX8Wll14at956a7zxxhvRqlWrOOGEE2Ly5Mlx7733xmuvvRYHHHBADBkyJN5+++2IiJgyZUoceeSRccIJJ8SMGTPiJz/5yXo/0Rs9enT87Gc/iyFDhsS8efNi3rx5MXDgwApZhmXLlkW/fv3iH//4R7z++utx9NFHxyGHHBIvvfRSqcffeeedUVhYGJMmTYobb7wxPvzww9h3331j6NChMWPGjPjFL34RZ5111nprAoj4tp80aNAgpkyZEpdddllccMEF8dRTT0VExMsvvxwR/z3zYfXt559/Pg499NA4+eST480334ybbrop7rjjjrjoootKvfZ5550X++yzT8ycOTOOOOKIDT7vL3/5S1x99dVx0003xdtvvx2PPPJI9OzZMyK+/ZrwFltsERdccEGm926sRYsWxb333hsREYWFhZn7a9WqFddcc0288cYbceedd8YzzzyT6dcDBw6M3/3ud9G4cePMfEePHh0RscH9C5B/HCtXzrHyupxzzjkxevTomDFjRnTp0iUOPvjg+Oabb8pV18a44ooronfv3jF9+vQ499xz15i+cOHC+OlPfxp9+vSJqVOnxuOPPx6ffPJJ/OxnP4uIiHnz5sXBBx8cRxxxRMyaNSsmTJgQ++67b6SUKqQ+oGyqY89emzlz5sQzzzwT/fv3z9y3dOnS2HPPPWP8+PExffr0GDJkSAwdOjQ++OCDUs+98sorY/vtt4/p06fH8ccfH8cdd1zMnj17jXksX748DjjggJgxY0Y8//zz0aFDhzLVdtttt8XBBx8cdevWjYMPPjhuu+22UtPPPvvsuOSSS+Lcc8+NN998M/785z9H69atM8uwyy67xEcffRR/+9vf4tVXX40zzjgj862Whx9+OE4++eQ47bTT4vXXX49jjjkmDj/88Hj22Wc3avwivt1fXHnllTF16tSoU6dOHHHEERERceCBB8Zpp50WPXr0yOyTDjzwwPX+u4MqkO20mrL77ifvP/zhD9MRRxyRUirfJ28dO3ZMq1atytzXtWvX9OMf/zhz+5tvvkkNGjRI99xzzwbrOu6441Ljxo03+LjVnxjNmDEjc9/777+fateunT766KNSjx08eHA6++yzU0opHXzwwaU+wUsppQMPPHCdZ0usXsbvn6VQEcuwNnvttVc67bTTMrd32WWX1KdPn1KPOfvss9O2225b6r4zzzzTmcXAGr5/ZvGPfvSjUtN32GGHdOaZZ2Zux1rOfBg8eHC6+OKLS933xz/+MbVt27bU875/ltuGnnfllVemLl26pBUrVqy19o4dO6arr756g8u42uozDRo0aJAaNGiQOZvgf/7nf9b7vAceeCC1aNEic3v12XjfVZb9C5BfHCv/V0UfK6e09jOLb7311sz0N954I0VEmjVrVpnrKu+ZxcOHDy/1nO+f3XbhhRem3XbbrdRjPvzwwxQRafbs2WnatGkpItJ77723ESMAVKTq2rNT+rbf1atXLzVo0CAVFRWliEh77733Oo+BV+vRo0e69tprM7c7duyYfv7zn2dul5SUpFatWqUbbrghpfTf3vX888+nwYMHpx/96Edp4cKFZaoxpZQWLVqUiouLM/uN6dOnp4YNG2a+gbd48eJUVFSUbrnllrU+/6abbkqNGjVKn3322VqnDxw4MB111FGl7jvggAMyvX1jzyxe7R//+EeKiPT111+nlNa+njf07w4qlzOLc9Sll14ad955Z8yaNatcz+/Ro0fUqvXf1d+6detSn9LUrl07WrRoEQsWLNjga6WUynwB+sLCwujVq1fm9syZM2PVqlXRpUuXaNiwYeZv4sSJ8e6770ZExKxZs0p9ghcRMWDAgDLNr6zKugyrVq2KCy+8MHr27BnNmzePhg0bxhNPPLHGp4f9+vUrdbsqlgHIT9/tmRERbdu23WBvfvXVV+OCCy4o1VePOuqomDdvXnz11VeZx22//fYb9bwDDjggvv7669hqq63iqKOOiocffjhzBtmmeP7552PatGlxxx13RJcuXeLGG28sNf3pp5+OwYMHx+abbx6NGjWKQw45JD777LNSy/J9Zdm/APnLsXLVHGd+t9a2bdtGRGTGpDLr+v7+6/teffXVePbZZ0uNWbdu3SIi4t13343evXvH4MGDo2fPnnHAAQfELbfcUi2vmQo1RXXq2atdffXVMWPGjHj11Vfj73//e8yZMycOOeSQzPSlS5fG6NGjo3v37tG0adNo2LBhzJo1a41s4Lt9sqCgINq0abNGHQcffHB8+eWX8eSTT0aTJk3KXOM999wTW2+9dfTu3TsiIn7wgx9Ex44d47777ouIb/vw8uXLY/DgwWt9/owZM6JPnz7RvHnztU6fNWtW7LTTTqXu22mnncq1nta3v1ibyvp3B2XjFwBy1M477xy77757nH322XHYYYdl7q9Vq9YaX59a248W1a1bt9TtgoKCtd5Xlh/V6NKlSyxatCjmzZuXedOvS3FxcamD5aVLl0bt2rVj2rRpUbt27VKPbdiw4QbnXVHKugyXX355/P73v4/f/e530bNnz2jQoEGccsopa/wQU4MGDSq7ZKCGKE9vXrp0aZx//vmx7777rjGtXr16mf//fq/a0PPat28fs2fPjqeffjqeeuqpOP744+Pyyy+PiRMnrlHnxujUqVM0bdo0unbtGgsWLIgDDzwwnnvuuYiIeO+992LvvfeO4447Li666KJo3rx5vPDCC3HkkUfGihUron79+uscg+qwfwGyw7Fy1fjumKyue2N+lK+s6+P7NnSsvXTp0hg6dGhceumla0xr27Zt1K5dO5566qn45z//GU8++WRce+21cc4558SUKVOiU6dOZa4fqBjVqWev1qZNm9hmm20iIqJr166xZMmSOPjgg+O3v/1tbLPNNjF69Oh46qmn4oorrohtttkmiouLY//9918jGyhLHXvuuWf86U9/ismTJ8dPf/rTMtd42223xRtvvFHqxz1LSkri9ttvjyOPPDKKi4vX+/wNTd+Q1QH9d9fRunr4xu4vKuvfHZSNM4tz2CWXXBKPPvpoTJ48OXNfy5YtY/78+aXerDNmzKjUOvbff/8oLCyMyy67bK3TFy5cuM7n9unTJ1atWhULFiyIbbbZptRfmzZtIiKie/fuMWXKlFLPe/HFF9dbU2FhYaxatarCl2HSpEkxbNiw+PnPfx69e/eOrbbaKubMmbPB1+/evfsa1zXe0DIAlEXdunXX6Hd9+/aN2bNnr9FXt9lmm1JnXXxfWZ5XXFwcQ4cOjWuuuSYmTJgQkydPjpkzZ0bExvfetRk1alS8/vrr8fDDD0dExLRp06KkpCSuvPLK+OEPfxhdunSJjz/+uNRz1jbfsuxfgPzmWHndKqJfb0hZ6mrZsuUa17iviPXRt2/feOONN2LLLbdcY9xWB80FBQWx0047xfnnnx/Tp0+PwsLCzL4HqHrVpWevy+oP7L7++uuI+DYbOOyww2KfffaJnj17Rps2beK9994r12sfd9xxcckll8T//M//xMSJE8v0nJkzZ8bUqVNjwoQJMWPGjMzf6uPzt956Kzp37hzFxcUxfvz4tb5Gr169YsaMGfH555+vdXr37t1j0qRJpe6bNGlSbLvtthHx7fqJiFJ9vDzrZ137pPX9u4PK5cziHNazZ88YMWJEXHPNNZn7Bg0aFP/5z3/isssui/333z8ef/zxeOyxx6Jx48aVVkf79u3j6quvjhNOOCEWL14chx56aGy55Zbx73//O+66665o2LBhXHnllWt9bpcuXWLEiBFx6KGHxpVXXhl9+vSJ//znPzF+/Pjo1atX7LXXXnHSSSfFTjvtFFdccUUMGzYsnnjiiXj88cfXW9OWW24ZTzzxRMyePTtatGgRTZo0We+nT2Vdhs6dO8eDDz4Y//znP6NZs2Zx1VVXxSeffJJpluty7LHHxpVXXhmnn356/OIXv8h83RpgU2255ZYxfvz42GmnnaKoqCiaNWsWv/nNb2LvvfeODh06xP777x+1atWKV199NV5//fX1/rjQhp53xx13xKpVq6J///5Rv379+NOf/hTFxcXRsWPHTC3PPfdcHHTQQVFUVBSbbbbZRi9P/fr146ijjooxY8bE8OHDY5tttomVK1fGtddeG0OHDs38cOj3x2Dp0qUxfvz46N27d9SvX79M+xcgvzlWXreNPVYuj7LU9dOf/jQuv/zyuOuuu2LAgAHxpz/9KV5//fXo06fPJs171KhRccstt8TBBx8cZ5xxRjRv3jzeeeeduPfee+PWW2+NqVOnxvjx42O33XaLVq1axZQpU+I///lPdO/efZPmC5RfdenZqy1cuDDmz58fJSUl8fbbb8cFF1wQXbp0yfSJzp07x0MPPRRDhw6NgoKCOPfcczfqzOXvO/HEE2PVqlWx9957x2OPPRY/+tGP1vv42267LXbcccfYeeed15i2ww47xG233RaXX355nHnmmXHGGWdEYWFh7LTTTvGf//wn3njjjTjyyCPj4IMPjosvvjiGDx8eY8eOjbZt28b06dOjXbt2MWDAgDj99NPjZz/7WfTp0yd23XXXePTRR+Ohhx6Kp59+OiK+DXN/+MMfxiWXXBKdOnWKBQsWxK9//euNXvYtt9wy5s6dGzNmzIgtttgiGjVqFPfcc896/91B5XJmcY674IILSjWk7t27x/XXXx/XXXdd9O7dO1566aXMr8JXpuOPPz6efPLJ+Oijj2KfffaJbt26xS9+8Yto3LjxBuc/bty4OPTQQ+O0006Lrl27xvDhw+Pll1/O/PrnD3/4w7jlllvi97//ffTu3TuefPLJDTago446Krp27Rrbb799tGzZco1Pw8q7DL/+9a+jb9++sfvuu8egQYOiTZs2MXz48A2+docOHeIvf/lLPPLII9G7d++48cYb4+KLL97g8wA25Morr4ynnnoq2rdvn/nH9e677x5///vf48knn4wddtghfvjDH8bVV1+9wYOrDT2vadOmccstt8ROO+0UvXr1iqeffjoeffTRaNGiRUR8u0967733Yuutt86caVAeJ5xwQsyaNSseeOCB6N27d1x11VVx6aWXxnbbbRd33313jB07ttTjBw4cGMcee2wceOCB0bJly8zZexvavwD5z7Hy2pXnWHljlaWu3XffPc4999w444wzYocddoglS5bEoYceusnzbteuXUyaNClWrVoVu+22W/Ts2TNOOeWUaNq0adSqVSsaN24czz33XOy5557RpUuX+PWvfx1XXnll7LHHHps8b6D8qkvPjog4/PDDo23btrHFFlvEwQcfHD169IjHHnssc8mHq666Kpo1axYDBw6MoUOHxu677x59+/bdpHmecsopcf7558eee+4Z//znP9f5uBUrVsSf/vSn2G+//dY6fb/99ou77rorVq5cGeeee26cdtpp8Zvf/Ca6d+8eBx54YOZawYWFhfHkk09Gq1atYs8994yePXvGJZdckjmLevjw4fH73/8+rrjiiujRo0fcdNNNMW7cuBg0aFBmXrfffnt888030a9fvzjllFPWe2LKuuy3334xZMiQ+MlPfhItW7aMe+65Z4P/7qByFaTvXwAGAAAAKtgdd9wRp5xyynovvQEAZJcziwEAAAAAEBazYRdffHE0bNhwrX+58lWtfFgGgFyzxx57rLP3uhQPkC/y4TgzH5YBoCxypd/dfffd66yzR48e2S6PPOcyFGzQ559/vs5fxywuLo7NN9+8iivaePmwDAC55qOPPsr8YvT3NW/ePJo3b17FFQFUvHw4zsyHZQAoi1zpd0uWLIlPPvlkrdPq1q3rh96oVMJiAAAAAABchgIAAAAAAGExAAAAAAAhLAYAAAAAIITFAAAAAACEsBgAAAAAgBAWAwAAAAAQwmIAAAAAAEJYDAAAAABARPx/g399r8kCMmEAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Final Check for Outliers\n",
        "fig,axes=plt.subplots(nrows=2,ncols=4,figsize=(18,7))\n",
        "sns.boxplot(x=df['Age'],ax=axes[0,0])\n",
        "sns.boxplot(x=df['Num_of_Delayed_Payment'],ax=axes[0,1])\n",
        "sns.boxplot(x=df['Num_of_Loan'],ax=axes[0,2])\n",
        "sns.boxplot(x=df['Changed_Credit_Limit'],ax=axes[0,3])\n",
        "sns.boxplot(x=df['Num_Credit_Card'],ax=axes[1,0])\n",
        "sns.boxplot(x=df['Interest_Rate'],ax=axes[1,1])\n",
        "sns.boxplot(x=df['Num_Credit_Inquiries'],ax=axes[1,2])\n",
        "sns.boxplot(x=df['Num_Bank_Accounts'],ax=axes[1,3])\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note:**\n",
        "1. We chose not to clip the values for Annual Income and Monthly Inhand Salary, as they may not represent errors or outliers, and altering them could impact future calculations\n",
        "2. There were many numercial columns with incorrect data types, we have corrected it also.\n",
        "3. There were many garbage values, we have corrected it using regex patterns"
      ],
      "metadata": {
        "id": "SIuHrZKN95Pp"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8w9Nyddjqdl9"
      },
      "source": [
        "**Note:**\n",
        "All the Data preprocessing has been done.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "*   We changed data types\n",
        "*   We have handele Null values\n",
        "*   we have handled outliers as well\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Checking the Distribution of Data**"
      ],
      "metadata": {
        "id": "OlKaY-LB90O9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Annual_Income_log']=np.log(df['Annual_Income'])\n",
        "sns.distplot(df['Annual_Income_log'])\n",
        "plt.title('Annual Income Distribution')\n",
        "plt.show()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "tuFWQJsl9zhS",
        "outputId": "3e3c984d-24bb-4a37-9383-b2b7879b0ae1"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note:**\n",
        "Since the range of values of Annual income are very high we have taken log of the column to show the distribution of data"
      ],
      "metadata": {
        "id": "N7stjR2K_0xW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(df['Monthly_Inhand_Salary'])\n",
        "plt.title('Monthly Inhand Salary Distribution')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "ce8FY7nCAR2r",
        "outputId": "04e2bdc9-ba5d-41db-aab7-e8ef19c0b97d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(data=df,x=df['Annual_Income'],y=df['Monthly_Inhand_Salary'])\n",
        "plt.title('Annual Income vs Monthly Inhand Salary')\n",
        "#plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "HjW0uQbGAvyc",
        "outputId": "353632cb-e73c-499a-d35f-18c4db007ff1"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Annual Income vs Monthly Inhand Salary')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Insights:**\n",
        "The scatter plot doesn't show a clear linear or non-linear pattern, suggesting that the Monthly Inhand Salary may not strongly depend on Annual Income in a straightforward manner. Let us try to understand this better using heatmap and further analysis"
      ],
      "metadata": {
        "id": "wiu06DxdBp0i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x=df['Num_Bank_Accounts'],y=df['Num_Credit_Card'])\n",
        "plt.title('Num of Loan vs Num of Credit cards')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "7EVTr5dKHncf",
        "outputId": "2c967d6c-11ab-4a0a-8458-2a2a3045a291"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x=df['Num_of_Loan'],y=df['Num_Credit_Card'])\n",
        "plt.title('Num_Bank_Loan vs Num of Credit cards')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "hWQIAotxJIdi",
        "outputId": "36d92be2-e888-44ce-95bc-aeb5778e2358"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))\n",
        "sns.countplot(x=df['Age'])\n",
        "plt.title('Age Distribution')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "_NuoEByyB2q7",
        "outputId": "d3ec5e0f-79d8-4e95-ca8d-fe517fb2fcec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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dq06dOkmSZs2apcaNG2vTpk26/fbbtXLlSu3Zs0erVq1SVFSUWrZsqRdeeEGjR4/W+PHjFRwcrOnTp6tevXqaPHmyJKlx48basGGDpkyZosTExCv+nAEAAAAA8Kdy8+F5+fn5mjdvnnJzcxUfH6+tW7fq/Pnz6tKli3NNo0aNVLduXaWnp0uS0tPT1axZM0VFRTnXJCYmKicnx3nUPz093aVG4ZrCGkXJy8tTTk6Oyw0AAAAAgPKozIP9rl27VKVKFYWEhGjo0KFatGiRmjRposzMTAUHB6tatWou66OiopSZmSlJyszMdAn1hfsL95W0JicnR2fOnCmyp4kTJyo8PNx5i42N9cdTBQAAAADA78o82Dds2FDbt2/Xl19+qWHDhql///7as2dPmfY0ZswYnThxwnn76aefyrQfAAAAAACKU+ZfdxccHKwGDRpIklq3bq2vvvpKb775pnr37q1z584pOzvb5ah9VlaWoqOjJUnR0dHavHmzS73CT82/eM2ln6SflZWlsLAwVaxYscieQkJCFBIS4pfnBwAAAABAaSrzI/aXKigoUF5enlq3bq2goCCtXr3auW/v3r06ePCg4uPjJUnx8fHatWuXjhw54lyTmpqqsLAwNWnSxLnm4hqFawprAAAAAABgZ2V6xH7MmDHq1q2b6tatq5MnT2ru3Llat26dVqxYofDwcA0aNEgjRoxQjRo1FBYWpscff1zx8fG6/fbbJUkJCQlq0qSJ+vbtq0mTJikzM1PPPvuskpOTnUfchw4dqr/97W96+umnNXDgQK1Zs0YLFizQsmXLyvKpAwCuEq1HfeBzja2v9vNDJwAA4FpVpsH+yJEj6tevnw4fPqzw8HA1b95cK1as0G9+8xtJ0pQpUxQQEKBevXopLy9PiYmJevvtt50/HxgYqKVLl2rYsGGKj49X5cqV1b9/f02YMMG5pl69elq2bJmeeuopvfnmm6pTp47ee+89vuoOAAAAAHBVKNNgP2PGjBL3h4aGaurUqZo6dWqxa+Li4vTZZ5+VWKdjx47atm2bpR4B2J8/jqhKHFUFriTmFgAAz5W7a+wBAAAAAIDnyvxT8QEArrhmGwAAAN4g2AMAAJ/wyygAAMoWwR4AAKAc4nMGAACe4hp7AAAAAABsjCP2AABcIzhlHgCAqxNH7AEAAAAAsDGCPQAAAAAANsap+MA1gNNvAQAoXXzYIYCyxBF7AAAAAABsjGAPAAAAAICNEewBAAAAALAxrrHHVY/rywEAAABczQj2AAAA8Bm/SAeAssOp+AAAAAAA2BjBHgAAAAAAG+NUfMACvqsWAAAAQHlBsAcAANcMrgMHAFyNOBUfAAAAAAAb44g9AMAyjn4CAACUPYI9AKDc4RcGAEoT/48BcLXhVHwAAAAAAGyMYA8AAAAAgI1xKj5QjnBqIAAAAABvEewBlCv8cgMAAADwDqfiAwAAAABgYwR7AAAAAABsjGAPAAAAAICNEewBAAAAALAxgj0AAAAAADZGsAcAAAAAwMYI9gAAAAAA2BjfYw/AEn9837zEd84DAAAAvuKIPQAAAAAANsYRewAAAKCc8scZcpwdB1z9OGIPAAAAAICNEewBAAAAALAxTsUHAAC4hnBqNwBcfThiDwAAAACAjRHsAQAAAACwMYI9AAAAAAA2RrAHAAAAAMDGCPYAAAAAANgYn4oPAACAcolP8AcAz3DEHgAAAAAAGyPYAwAAAABgYwR7AAAAAABsjGvsAQAoZ/xxXbHEtcUAAFwrOGIPAAAAAICNEewBAAAAALAxgj0AAAAAADZGsAcAAAAAwMYI9gAAAAAA2BjBHgAAAAAAGyPYAwAAAABgYwR7AAAAAABsjGAPAAAAAICNEewBAAAAALAxgj0AAAAAADZGsAcAAAAAwMYI9gAAAAAA2FiZBvuJEyeqTZs2qlq1qiIjI9WjRw/t3bvXZU3Hjh3lcDhcbkOHDnVZc/DgQSUlJalSpUqKjIzUqFGjdOHCBZc169atU6tWrRQSEqIGDRpo9uzZpf30AAAAAAAodWUa7NPS0pScnKxNmzYpNTVV58+fV0JCgnJzc13WDR48WIcPH3beJk2a5NyXn5+vpKQknTt3Tl988YXef/99zZ49W2PHjnWuycjIUFJSku6++25t375dw4cP1yOPPKIVK1ZcsecKAAAAAEBpqFCWD758+XKX+7Nnz1ZkZKS2bt2q9u3bO7dXqlRJ0dHRRdZYuXKl9uzZo1WrVikqKkotW7bUCy+8oNGjR2v8+PEKDg7W9OnTVa9ePU2ePFmS1LhxY23YsEFTpkxRYmJi6T1BAAAAAABKWbm6xv7EiROSpBo1arhsnzNnjmrVqqWbb75ZY8aM0enTp5370tPT1axZM0VFRTm3JSYmKicnR7t373au6dKli0vNxMREpaenF9lHXl6ecnJyXG4AAAAAAJRHZXrE/mIFBQUaPny42rVrp5tvvtm5/YEHHlBcXJxiYmK0c+dOjR49Wnv37tXHH38sScrMzHQJ9ZKc9zMzM0tck5OTozNnzqhixYou+yZOnKjnn3/e788RAAAAAAB/KzfBPjk5WV9//bU2bNjgsn3IkCHO/27WrJlq166tzp0768CBA7rhhhtKpZcxY8ZoxIgRzvs5OTmKjY0tlccCAAAAAMAX5eJU/JSUFC1dulRr165VnTp1Slzbtm1bSdL+/fslSdHR0crKynJZU3i/8Lr84taEhYVddrRekkJCQhQWFuZyAwAAAACgPCrTYG+MUUpKihYtWqQ1a9aoXr16bn9m+/btkqTatWtLkuLj47Vr1y4dOXLEuSY1NVVhYWFq0qSJc83q1atd6qSmpio+Pt5PzwQAAAAAgLJRpsE+OTlZH374oebOnauqVasqMzNTmZmZOnPmjCTpwIEDeuGFF7R161b98MMPWrJkifr166f27durefPmkqSEhAQ1adJEffv21Y4dO7RixQo9++yzSk5OVkhIiCRp6NCh+v777/X000/r22+/1dtvv60FCxboqaeeKrPnDgAAAACAP5RpsJ82bZpOnDihjh07qnbt2s7b/PnzJUnBwcFatWqVEhIS1KhRI40cOVK9evXSp59+6qwRGBiopUuXKjAwUPHx8XrooYfUr18/TZgwwbmmXr16WrZsmVJTU9WiRQtNnjxZ7733Hl91BwAAAACwvTL98DxjTIn7Y2NjlZaW5rZOXFycPvvssxLXdOzYUdu2bfOqPwAAAAAAyrty8eF5AAAAAADAGoI9AAAAAAA2Vm6+xx5oPeoDn2tsfbWfHzoBAAAAAPvgiD0AAAAAADZGsAcAAAAAwMYI9gAAAAAA2BjBHgAAAAAAGyPYAwAAAABgYwR7AAAAAABsjGAPAAAAAICNEewBAAAAALAxgj0AAAAAADZGsAcAAAAAwMYI9gAAAAAA2BjBHgAAAAAAGyPYAwAAAABgYwR7AAAAAABsjGAPAAAAAICNEewBAAAAALAxgj0AAAAAADZGsAcAAAAAwMYI9gAAAAAA2BjBHgAAAAAAGyPYAwAAAABgYwR7AAAAAABsjGAPAAAAAICNEewBAAAAALAxgj0AAAAAADZGsAcAAAAAwMYI9gAAAAAA2BjBHgAAAAAAGyPYAwAAAABgYwR7AAAAAABsjGAPAAAAAICNEewBAAAAALAxgj0AAAAAADZGsAcAAAAAwMYI9gAAAAAA2BjBHgAAAAAAGyPYAwAAAABgYwR7AAAAAABsjGAPAAAAAICNEewBAAAAALAxgj0AAAAAADZGsAcAAAAAwMYI9gAAAAAA2BjBHgAAAAAAGyPYAwAAAABgYwR7AAAAAABsjGAPAAAAAICNEewBAAAAALAxgj0AAAAAADZGsAcAAAAAwMYI9gAAAAAA2BjBHgAAAAAAGyPYAwAAAABgYwR7AAAAAABsjGAPAAAAAICNEewBAAAAALAxgj0AAAAAADZWpsF+4sSJatOmjapWrarIyEj16NFDe/fudVlz9uxZJScnq2bNmqpSpYp69eqlrKwslzUHDx5UUlKSKlWqpMjISI0aNUoXLlxwWbNu3Tq1atVKISEhatCggWbPnl3aTw8AAAAAgFJXpsE+LS1NycnJ2rRpk1JTU3X+/HklJCQoNzfXueapp57Sp59+qoULFyotLU2HDh1Sz549nfvz8/OVlJSkc+fO6YsvvtD777+v2bNna+zYsc41GRkZSkpK0t13363t27dr+PDheuSRR7RixYor+nwBAAAAAPC3CmX54MuXL3e5P3v2bEVGRmrr1q1q3769Tpw4oRkzZmju3Lnq1KmTJGnWrFlq3LixNm3apNtvv10rV67Unj17tGrVKkVFRally5Z64YUXNHr0aI0fP17BwcGaPn266tWrp8mTJ0uSGjdurA0bNmjKlClKTEy84s8bAAAAAAB/KVfX2J84cUKSVKNGDUnS1q1bdf78eXXp0sW5plGjRqpbt67S09MlSenp6WrWrJmioqKcaxITE5WTk6Pdu3c711xco3BNYY1L5eXlKScnx+UGAAAAAEB5VG6CfUFBgYYPH6527drp5ptvliRlZmYqODhY1apVc1kbFRWlzMxM55qLQ33h/sJ9Ja3JycnRmTNnLutl4sSJCg8Pd95iY2P98hwBAAAAAPC3chPsk5OT9fXXX2vevHll3YrGjBmjEydOOG8//fRTWbcEAAAAAECRyvQa+0IpKSlaunSp1q9frzp16ji3R0dH69y5c8rOznY5ap+VlaXo6Gjnms2bN7vUK/zU/IvXXPpJ+llZWQoLC1PFihUv6yckJEQhISF+eW4AAAAAAJSmMj1ib4xRSkqKFi1apDVr1qhevXou+1u3bq2goCCtXr3auW3v3r06ePCg4uPjJUnx8fHatWuXjhw54lyTmpqqsLAwNWnSxLnm4hqFawprAAAAAABgV2V6xD45OVlz587VJ598oqpVqzqviQ8PD1fFihUVHh6uQYMGacSIEapRo4bCwsL0+OOPKz4+XrfffrskKSEhQU2aNFHfvn01adIkZWZm6tlnn1VycrLzqPvQoUP1t7/9TU8//bQGDhyoNWvWaMGCBVq2bFmZPXcAAAAAAPyhTI/YT5s2TSdOnFDHjh1Vu3Zt523+/PnONVOmTNHvfvc79erVS+3bt1d0dLQ+/vhj5/7AwEAtXbpUgYGBio+P10MPPaR+/fppwoQJzjX16tXTsmXLlJqaqhYtWmjy5Ml67733+Ko7AAAAAIDtlekRe2OM2zWhoaGaOnWqpk6dWuyauLg4ffbZZyXW6dixo7Zt2+Z1jwAAAAAAlGfl5lPxAQAAAACA9wj2AAAAAADYGMEeAAAAAAAbI9gDAAAAAGBjBHsAAAAAAGyMYA8AAAAAgI0R7AEAAAAAsDGCPQAAAAAANkawBwAAAADAxgj2AAAAAADYGMEeAAAAAAAbI9gDAAAAAGBjBHsAAAAAAGyMYA8AAAAAgI0R7AEAAAAAsDGCPQAAAAAANkawBwAAAADAxgj2AAAAAADYGMEeAAAAAAAbI9gDAAAAAGBjBHsAAAAAAGyMYA8AAAAAgI0R7AEAAAAAsDGCPQAAAAAANkawBwAAAADAxgj2AAAAAADYGMEeAAAAAAAbI9gDAAAAAGBjBHsAAAAAAGyMYA8AAAAAgI0R7AEAAAAAsDGCPQAAAAAANmYp2Hfq1EnZ2dmXbc/JyVGnTp187QkAAAAAAHjIUrBft26dzp07d9n2s2fP6t///rfPTQEAAAAAAM9U8Gbxzp07nf+9Z88eZWZmOu/n5+dr+fLluu666/zXHQAAAAAAKJFXwb5ly5ZyOBxyOBxFnnJfsWJFvfXWW35rDgAAAAAAlMyrYJ+RkSFjjOrXr6/NmzcrIiLCuS84OFiRkZEKDAz0e5MAAAAAAKBoXgX7uLg4SVJBQUGpNAMAAAAAALzjVbC/2L59+7R27VodOXLksqA/duxYnxsDAAAAAADuWQr27777roYNG6ZatWopOjpaDofDuc/hcBDsAQAAAAC4QiwF+xdffFEvvfSSRo8e7e9+AAAAAJSy1qM+8LnG1lf7+aETAP5g6Xvsjx8/rj/+8Y/+7gUAAAAAAHjJUrD/4x//qJUrV/q7FwAAAAAA4CVLp+I3aNBAzz33nDZt2qRmzZopKCjIZf8TTzzhl+YAAAAAAEDJLAX7v//976pSpYrS0tKUlpbmss/hcBDsAQAAAAC4QiwF+4yMDH/3AQAAAAAALLB0jT0AAAAAACgfLB2xHzhwYIn7Z86caakZAAAAAADgHUvB/vjx4y73z58/r6+//lrZ2dnq1KmTXxoDAAAAAADuWQr2ixYtumxbQUGBhg0bphtuuMHnpq5FrUd94HONra/280MnAAAAAAA78ds19gEBARoxYoSmTJnir5IAAAAAAMANv3543oEDB3ThwgV/lgQAAAAAACWwdCr+iBEjXO4bY3T48GEtW7ZM/fv390tjAAAAAADAPUvBftu2bS73AwICFBERocmTJ7v9xHwAAAAAAOA/loL92rVr/d0HAAAAAACwwFKwL/Trr79q7969kqSGDRsqIiLCL00BAAAAAADPWPrwvNzcXA0cOFC1a9dW+/bt1b59e8XExGjQoEE6ffq0v3sEAAAAAADFsPzheWlpafr000/Vrl07SdKGDRv0xBNPaOTIkZo2bZpfm0T50nrUB36ps/XVfn6pAwAAAADXMkvB/qOPPtK//vUvdezY0bntt7/9rSpWrKj77ruPYA8AAAAAwBVi6VT806dPKyoq6rLtkZGRnIoPAAAAAMAVZCnYx8fHa9y4cTp79qxz25kzZ/T8888rPj7eb80BAAAAAICSWQr2b7zxhjZu3Kg6deqoc+fO6ty5s2JjY7Vx40a9+eabHtdZv369unfvrpiYGDkcDi1evNhl/8MPPyyHw+Fy69q1q8uaY8eO6cEHH1RYWJiqVaumQYMG6dSpUy5rdu7cqbvuukuhoaGKjY3VpEmTrDxtAAAAAADKHUvX2Ddr1kz79u3TnDlz9O2330qS+vTpowcffFAVK1b0uE5ubq5atGihgQMHqmfPnkWu6dq1q2bNmuW8HxIS4rL/wQcf1OHDh5Wamqrz589rwIABGjJkiObOnStJysnJUUJCgrp06aLp06dr165dGjhwoKpVq6YhQ4Z4+9QBAAAAAChXLAX7iRMnKioqSoMHD3bZPnPmTP36668aPXq0R3W6deumbt26lbgmJCRE0dHRRe775ptvtHz5cn311Ve69dZbJUlvvfWWfvvb3+q1115TTEyM5syZo3PnzmnmzJkKDg5W06ZNtX37dr3++usEewAAAACA7VkK9u+8847ziPjFmjZtqvvvv9/jYO+JdevWKTIyUtWrV1enTp304osvqmbNmpKk9PR0VatWzRnqJalLly4KCAjQl19+qXvvvVfp6elq3769goODnWsSExP1yiuv6Pjx46pevfplj5mXl6e8vDzn/ZycHL89nyvNH19Nx9fSAQAAAED5Zeka+8zMTNWuXfuy7RERETp8+LDPTRXq2rWrPvjgA61evVqvvPKK0tLS1K1bN+Xn5zv7iIyMdPmZChUqqEaNGsrMzHSuufQT/AvvF6651MSJExUeHu68xcbG+u05AQAAAADgT5aO2Bd+UF69evVctm/cuFExMTF+aUyS7r//fud/N2vWTM2bN9cNN9ygdevWqXPnzn57nEuNGTNGI0aMcN7Pyckh3AMAAAAAyiVLwX7w4MEaPny4zp8/r06dOkmSVq9eraefflojR470a4MXq1+/vmrVqqX9+/erc+fOio6O1pEjR1zWXLhwQceOHXNelx8dHa2srCyXNYX3i7t2PyQk5LIP6QMAAAAAoDyyFOxHjRqlo0eP6rHHHtO5c+ckSaGhoRo9erTGjBnj1wYv9vPPP+vo0aPOywDi4+OVnZ2trVu3qnXr1pKkNWvWqKCgQG3btnWu+fOf/6zz588rKChIkpSamqqGDRsWeX09AAAAAAB2Yukae4fDoVdeeUW//vqrNm3apB07dujYsWMaO3asV3VOnTql7du3a/v27ZKkjIwMbd++XQcPHtSpU6c0atQobdq0ST/88INWr16te+65Rw0aNFBiYqIkqXHjxuratasGDx6szZs3a+PGjUpJSdH999/vvCTggQceUHBwsAYNGqTdu3dr/vz5evPNN11OtQcAAAAAwK4sHbEvVKVKFbVp08byz2/ZskV33323835h2O7fv7+mTZumnTt36v3331d2drZiYmKUkJCgF154weU0+Tlz5iglJUWdO3dWQECAevXqpb/+9a/O/eHh4Vq5cqWSk5PVunVr1apVS2PHjuWr7gAAAAAAVwWfgr2vOnbsKGNMsftXrFjhtkaNGjWK/Oq9izVv3lz//ve/ve4PAAAAAIDyztKp+AAAAAAAoHwg2AMAAAAAYGMEewAAAAAAbIxgDwAAAACAjZXph+cBAAAAQElaj/rA5xpbX+3nh06A8otgDwAAAOCawy8McDXhVHwAAAAAAGyMI/YAAAAAgGuOP87akMrHmRsEewAAAACAX3CJQ9kg2AMAAADANeZqOloNrrEHAAAAAMDWCPYAAAAAANgYwR4AAAAAABsj2AMAAAAAYGMEewAAAAAAbIxgDwAAAACAjfF1dwAAAACAcs0fX893NX81H8EeAAAAAMoxQi3c4VR8AAAAAABsjGAPAAAAAICNEewBAAAAALAxrrEHAAAAAD/gWniUFY7YAwAAAABgYwR7AAAAAABsjGAPAAAAAICNEewBAAAAALAxgj0AAAAAADZGsAcAAAAAwMb4ujsAAAAAPvPHV71JfN0bYAVH7AEAAAAAsDGCPQAAAAAANkawBwAAAADAxgj2AAAAAADYGMEeAAAAAAAbI9gDAAAAAGBjfN2dBf74Kg++xgMAAAAA4A8csQcAAAAAwMYI9gAAAAAA2BjBHgAAAAAAGyPYAwAAAABgYwR7AAAAAABsjGAPAAAAAICNEewBAAAAALAxgj0AAAAAADZGsAcAAAAAwMYI9gAAAAAA2BjBHgAAAAAAGyPYAwAAAABgYwR7AAAAAABsjGAPAAAAAICNEewBAAAAALAxgj0AAAAAADZGsAcAAAAAwMYI9gAAAAAA2BjBHgAAAAAAGyPYAwAAAABgYwR7AAAAAABsjGAPAAAAAICNEewBAAAAALAxgj0AAAAAADZGsAcAAAAAwMbKNNivX79e3bt3V0xMjBwOhxYvXuyy3xijsWPHqnbt2qpYsaK6dOmiffv2uaw5duyYHnzwQYWFhalatWoaNGiQTp065bJm586duuuuuxQaGqrY2FhNmjSptJ8aAAAAAABXRJkG+9zcXLVo0UJTp04tcv+kSZP017/+VdOnT9eXX36pypUrKzExUWfPnnWuefDBB7V7926lpqZq6dKlWr9+vYYMGeLcn5OTo4SEBMXFxWnr1q169dVXNX78eP39738v9ecHAAAAAEBpq1CWD96tWzd169atyH3GGL3xxht69tlndc8990iSPvjgA0VFRWnx4sW6//779c0332j58uX66quvdOutt0qS3nrrLf32t7/Va6+9ppiYGM2ZM0fnzp3TzJkzFRwcrKZNm2r79u16/fXXXX4BcLG8vDzl5eU57+fk5Pj5mQMAAAAA4B/l9hr7jIwMZWZmqkuXLs5t4eHhatu2rdLT0yVJ6enpqlatmjPUS1KXLl0UEBCgL7/80rmmffv2Cg4Odq5JTEzU3r17dfz48SIfe+LEiQoPD3feYmNjS+MpAgAAAADgs3Ib7DMzMyVJUVFRLtujoqKc+zIzMxUZGemyv0KFCqpRo4bLmqJqXPwYlxozZoxOnDjhvP3000++PyEAAAAAAEpBmZ6KX16FhIQoJCSkrNsAAAAAAMCtcnvEPjo6WpKUlZXlsj0rK8u5Lzo6WkeOHHHZf+HCBR07dsxlTVE1Ln4MAAAAAADsqtwG+3r16ik6OlqrV692bsvJydGXX36p+Ph4SVJ8fLyys7O1detW55o1a9aooKBAbdu2da5Zv369zp8/71yTmpqqhg0bqnr16lfo2QAAAAAAUDrKNNifOnVK27dv1/bt2yX97wPztm/froMHD8rhcGj48OF68cUXtWTJEu3atUv9+vVTTEyMevToIUlq3LixunbtqsGDB2vz5s3auHGjUlJSdP/99ysmJkaS9MADDyg4OFiDBg3S7t27NX/+fL355psaMWJEGT1rAAAAAAD8p0yvsd+yZYvuvvtu5/3CsN2/f3/Nnj1bTz/9tHJzczVkyBBlZ2frzjvv1PLlyxUaGur8mTlz5iglJUWdO3dWQECAevXqpb/+9a/O/eHh4Vq5cqWSk5PVunVr1apVS2PHji32q+4AAAAAALCTMg32HTt2lDGm2P0Oh0MTJkzQhAkTil1To0YNzZ07t8THad68uf79739b7hMAAAAAgPKq3F5jDwAAAAAA3CPYAwAAAABgYwR7AAAAAABsjGAPAAAAAICNEewBAAAAALAxgj0AAAAAADZGsAcAAAAAwMYI9gAAAAAA2BjBHgAAAAAAGyPYAwAAAABgYwR7AAAAAABsjGAPAAAAAICNEewBAAAAALAxgj0AAAAAADZGsAcAAAAAwMYI9gAAAAAA2BjBHgAAAAAAGyPYAwAAAABgYwR7AAAAAABsjGAPAAAAAICNEewBAAAAALAxgj0AAAAAADZGsAcAAAAAwMYI9gAAAAAA2BjBHgAAAAAAGyPYAwAAAABgYwR7AAAAAABsjGAPAAAAAICNEewBAAAAALAxgj0AAAAAADZGsAcAAAAAwMYI9gAAAAAA2BjBHgAAAAAAGyPYAwAAAABgYwR7AAAAAABsjGAPAAAAAICNEewBAAAAALAxgj0AAAAAADZGsAcAAAAAwMYI9gAAAAAA2BjBHgAAAAAAGyPYAwAAAABgYwR7AAAAAABsjGAPAAAAAICNEewBAAAAALAxgj0AAAAAADZGsAcAAAAAwMYI9gAAAAAA2BjBHgAAAAAAGyPYAwAAAABgYwR7AAAAAABsjGAPAAAAAICNEewBAAAAALAxgj0AAAAAADZGsAcAAAAAwMYI9gAAAAAA2BjBHgAAAAAAGyPYAwAAAABgYwR7AAAAAABsjGAPAAAAAICNletgP378eDkcDpdbo0aNnPvPnj2r5ORk1axZU1WqVFGvXr2UlZXlUuPgwYNKSkpSpUqVFBkZqVGjRunChQtX+qkAAAAAAFAqKpR1A+40bdpUq1atct6vUOH/t/zUU09p2bJlWrhwocLDw5WSkqKePXtq48aNkqT8/HwlJSUpOjpaX3zxhQ4fPqx+/fopKChIf/nLX674cwEAAAAAwN/KfbCvUKGCoqOjL9t+4sQJzZgxQ3PnzlWnTp0kSbNmzVLjxo21adMm3X777Vq5cqX27NmjVatWKSoqSi1bttQLL7yg0aNHa/z48QoODr7STwcAAAAAAL8q16fiS9K+ffsUExOj+vXr68EHH9TBgwclSVu3btX58+fVpUsX59pGjRqpbt26Sk9PlySlp6erWbNmioqKcq5JTExUTk6Odu/eXexj5uXlKScnx+UGAAAAAEB5VK6Dfdu2bTV79mwtX75c06ZNU0ZGhu666y6dPHlSmZmZCg4OVrVq1Vx+JioqSpmZmZKkzMxMl1BfuL9wX3EmTpyo8PBw5y02Nta/TwwAAAAAAD8p16fid+vWzfnfzZs3V9u2bRUXF6cFCxaoYsWKpfa4Y8aM0YgRI5z3c3JyCPcAAAAAgHKpXB+xv1S1atV00003af/+/YqOjta5c+eUnZ3tsiYrK8t5TX50dPRln5JfeL+o6/YLhYSEKCwszOUGAAAAAEB5ZKtgf+rUKR04cEC1a9dW69atFRQUpNWrVzv37927VwcPHlR8fLwkKT4+Xrt27dKRI0eca1JTUxUWFqYmTZpc8f4BAAAAAPC3cn0q/p/+9Cd1795dcXFxOnTokMaNG6fAwED16dNH4eHhGjRokEaMGKEaNWooLCxMjz/+uOLj43X77bdLkhISEtSkSRP17dtXkyZNUmZmpp599lklJycrJCSkjJ8dAAAAAAC+K9fB/ueff1afPn109OhRRURE6M4779SmTZsUEREhSZoyZYoCAgLUq1cv5eXlKTExUW+//bbz5wMDA7V06VINGzZM8fHxqly5svr3768JEyaU1VMCAAAAAMCvynWwnzdvXon7Q0NDNXXqVE2dOrXYNXFxcfrss8/83RoAAAAAAOWCra6xBwAAAAAArgj2AAAAAADYGMEeAAAAAAAbI9gDAAAAAGBjBHsAAAAAAGyMYA8AAAAAgI0R7AEAAAAAsDGCPQAAAAAANkawBwAAAADAxgj2AAAAAADYGMEeAAAAAAAbq1DWDQAAAAAAcLVoPeoDn2tsfbWfV+s5Yg8AAAAAgI0R7AEAAAAAsDGCPQAAAAAANkawBwAAAADAxgj2AAAAAADYGMEeAAAAAAAbI9gDAAAAAGBjBHsAAAAAAGyMYA8AAAAAgI0R7AEAAAAAsDGCPQAAAAAANkawBwAAAADAxgj2AAAAAADYGMEeAAAAAAAbI9gDAAAAAGBjBHsAAAAAAGyMYA8AAAAAgI0R7AEAAAAAsDGCPQAAAAAANkawBwAAAADAxgj2AAAAAADYGMEeAAAAAAAbI9gDAAAAAGBjBHsAAAAAAGyMYA8AAAAAgI0R7AEAAAAAsDGCPQAAAAAANkawBwAAAADAxgj2AAAAAADYGMEeAAAAAAAbI9gDAAAAAGBjBHsAAAAAAGyMYA8AAAAAgI0R7AEAAAAAsDGCPQAAAAAANkawBwAAAADAxgj2AAAAAADYGMEeAAAAAAAbI9gDAAAAAGBjBHsAAAAAAGyMYA8AAAAAgI0R7AEAAAAAsDGCPQAAAAAANkawBwAAAADAxgj2AAAAAADYGMEeAAAAAAAbI9gDAAAAAGBjBHsAAAAAAGyMYA8AAAAAgI1dU8F+6tSpuv766xUaGqq2bdtq8+bNZd0SAAAAAAA+uWaC/fz58zVixAiNGzdO//nPf9SiRQslJibqyJEjZd0aAAAAAACWXTPB/vXXX9fgwYM1YMAANWnSRNOnT1elSpU0c+bMsm4NAAAAAADLKpR1A1fCuXPntHXrVo0ZM8a5LSAgQF26dFF6evpl6/Py8pSXl+e8f+LECUlSTk6OJCk/74zPPRXWKlQaNUurrj9qllZdXgNeg9KqWVp1+fPiNSitmqVVlz8vXoOiapZW3Wut19Kqy2tgr9fATr2WVl1eg//VLKxrjHG73mE8WWVzhw4d0nXXXacvvvhC8fHxzu1PP/200tLS9OWXX7qsHz9+vJ5//vkr3SYAAAAAAC5++ukn1alTp8Q118QRe2+NGTNGI0aMcN4vKCjQsWPHVLNmTTkcjhJ/NicnR7Gxsfrpp58UFhbmt55Ko66dei2tunbqtbTq0qu96tqp19Kqa6deS6suvdqrrp16La269GqvunbqtbTq0qu96tqpV2/qGmN08uRJxcTEuK15TQT7WrVqKTAwUFlZWS7bs7KyFB0dfdn6kJAQhYSEuGyrVq2aV48ZFhbm1z/80qxrp15Lq66dei2tuvRqr7p26rW06tqp19KqS6/2qmunXkurLr3aq66dei2tuvRqr7p26tXTuuHh4R7VuiY+PC84OFitW7fW6tWrndsKCgq0evVql1PzAQAAAACwm2viiL0kjRgxQv3799ett96q2267TW+88YZyc3M1YMCAsm4NAAAAAADLrplg37t3b/36668aO3asMjMz1bJlSy1fvlxRUVF+fZyQkBCNGzfuslP5y2NdO/VaWnXt1Gtp1aVXe9W1U6+lVddOvZZWXXq1V1079VpadenVXnXt1Gtp1aVXe9W1U6+lVfea+FR8AAAAAACuVtfENfYAAAAAAFytCPYAAAAAANgYwR4AAAAAABsj2AMAAAAAYGMEe4vWr1+v7t27KyYmRg6HQ4sXLy527dChQ+VwOPTGG2/4XPfhhx+Ww+FwuXXt2tXnXr/55hv9/ve/V3h4uCpXrqw2bdro4MGDPtW9tM/C26uvvmq55qlTp5SSkqI6deqoYsWKatKkiaZPn15in57UzcrK0sMPP6yYmBhVqlRJXbt21b59+0qsOXHiRLVp00ZVq1ZVZGSkevToob1797qsOXv2rJKTk1WzZk1VqVJFvXr1UlZWls91//73v6tjx44KCwuTw+FQdna2TzWPHTumxx9/XA0bNlTFihVVt25dPfHEEzpx4oTPvT766KO64YYbVLFiRUVEROiee+7Rt99+63PdQsYYdevWze0celKzY8eOl/19HTp0qF96TU9PV6dOnVS5cmWFhYWpffv2OnPmjOW6P/zwQ7EztnDhQsu9ZmZmqm/fvoqOjlblypXVqlUrffTRRz6/BgcOHNC9996riIgIhYWF6b777nM7C9OmTVPz5s0VFhamsLAwxcfH6/PPP3futzJf7mp6O1ue1LU6X+56tTJbntQt5OlseVrXynx50qu3s+WurpXZ8rRfK/PlrqaV2brUyy+/LIfDoeHDhzu3WZkvT+panbGS6lqdMXe9Wp0xd3ULeTtj7upamTFPerUyYyXV9WXGSurVynx5UtfKjI0fP/6y59aoUSPnfqvz5a6ulfkqqaYvs+WuV6vz5a5uIW/my11Nq7PlSa++ztfFCPYW5ebmqkWLFpo6dWqJ6xYtWqRNmzYpJibGb3W7du2qw4cPO2///Oc/fap54MAB3XnnnWrUqJHWrVunnTt36rnnnlNoaKhPdS/u8fDhw5o5c6YcDod69eplueaIESO0fPlyffjhh/rmm280fPhwpaSkaMmSJZZ7NcaoR48e+v777/XJJ59o27ZtiouLU5cuXZSbm1tszbS0NCUnJ2vTpk1KTU3V+fPnlZCQ4PIzTz31lD799FMtXLhQaWlpOnTokHr27Flir57UPX36tLp27ar/+7//K7GWpzUPHTqkQ4cO6bXXXtPXX3+t2bNna/ny5Ro0aJDPvbZu3VqzZs3SN998oxUrVsgYo4SEBOXn5/tUt9Abb7whh8Ph82tQaPDgwS5/bydNmuRz3fT0dHXt2lUJCQnavHmzvvrqK6WkpCggoPj/BburGxsbe9mMPf/886pSpYq6detmudd+/fpp7969WrJkiXbt2qWePXvqvvvu07Zt2yz3mpubq4SEBDkcDq1Zs0YbN27UuXPn1L17dxUUFBRbt06dOnr55Ze1detWbdmyRZ06ddI999yj3bt3S7I2X+5qejtbntS1Ol/uerUyW57ULeTpbHlT19v5clfTymy5q2tltjzt18p8lVTT6mxd7KuvvtI777yj5s2bu2y3Ml+e1LU6YyXVtTpj7nq1OmPu6hbydsY8qevtjLmraXXGSqrry4yV1KuV+XJX15cZa9q0qctz3LBhg3OfL/NVUl2r81VcTV9nq6RefZmvkuoW8na+3NW0Olsl1fV1vi5j4DNJZtGiRZdt//nnn811111nvv76axMXF2emTJnic93+/fube+65x6+99u7d2zz00EOWaxZX91L33HOP6dSpk081mzZtaiZMmOCyrVWrVubPf/6z5bp79+41kszXX3/t3Jafn28iIiLMu+++63HdI0eOGEkmLS3NGGNMdna2CQoKMgsXLnSu+eabb4wkk56ebrnuxdauXWskmePHj3tcz13NQgsWLDDBwcHm/Pnzfq27Y8cOI8ns37/f57rbtm0z1113nTl8+LBHfwfd1ezQoYN58sknPa7had22bduaZ5991u91L9WyZUszcOBAn2pWrlzZfPDBBy7ratSo4dMsrFixwgQEBJgTJ04412RnZxuHw2FSU1M9rmuMMdWrVzfvvfee3+br4poXszpb7uoWsjJf7mpama3i6voyW8XV9cd8XVrTH7NVVN1LeTtbxdX1x3xdXNPX2Tp58qS58cYbTWpqqsufj6/zVVzdi1mZMU/qFvJ0xryp6c2MuatrdcZKqmt1xkqq6cuMefPaejpjJdX0Zb6Kq2t1xsaNG2datGhR5D5f5qukuhfzZr48rVnI09nytq6n8+VJXW/ny11Nq7Plrq4/38OMMYYj9qWkoKBAffv21ahRo9S0aVO/1l63bp0iIyPVsGFDDRs2TEePHrVcq6CgQMuWLdNNN92kxMRERUZGqm3btl6fEuZOVlaWli1b5vFv+Ipzxx13aMmSJfrll19kjNHatWv13XffKSEhwXLNvLw8SXI5QyEgIEAhISFF/gawOIWnJdWoUUOStHXrVp0/f15dunRxrmnUqJHq1q2r9PR0y3X9wZOaJ06cUFhYmCpUqOC3urm5uZo1a5bq1aun2NhYn+qePn1aDzzwgKZOnaro6GiPa7nrdc6cOapVq5ZuvvlmjRkzRqdPn/ap7pEjR/Tll18qMjJSd9xxh6KiotShQwev/m6V1G+hrVu3avv27V7NWFE177jjDs2fP1/Hjh1TQUGB5s2bp7Nnz6pjx46W6+bl5cnhcCgkJMS5JjQ0VAEBAR6/Dvn5+Zo3b55yc3MVHx/vl/m6tKa/eFLX2/lyV9PqbBVV19fZKqlfX+br0pr+mi13r62V2Squrq/zdWlNX2crOTlZSUlJLnMk+f7+VVxdX3lT19MZ87SmtzNWUl1fZsxdv1ZmrLiavs6Yp6+tNzNWUk1f5qu4ur7M2L59+xQTE6P69evrwQcfdF7i6ut8FVfXF97U9Ob9y9O63s5XSXWtzpe7Xq2+fxVX11/vYS789iuCa5iK+E3QX/7yF/Ob3/zGFBQUGGOM347Y//Of/zSffPKJ2blzp1m0aJFp3LixadOmjblw4YKlmoW/yapUqZJ5/fXXzbZt28zEiRONw+Ew69at86nXi73yyiumevXq5syZMz7VPHv2rOnXr5+RZCpUqGCCg4PN+++/73HNouqeO3fO1K1b1/zxj380x44dM3l5eebll182kkxCQoJHNfPz801SUpJp166dc9ucOXNMcHDwZWvbtGljnn76act1L2bliIe7msYY8+uvv5q6deua//u///NL3alTp5rKlSsbSaZhw4ZeHVEsru6QIUPMoEGDnPfd/R30pOY777xjli9fbnbu3Gk+/PBDc91115l7773Xp17T09ONJFOjRg0zc+ZM85///McMHz7cBAcHm++++86nfi82bNgw07hxY596NcaY48ePm4SEBOeMhYWFmRUrVvhU98iRIyYsLMw8+eSTJjc315w6dcqkpKQYSWbIkCEl1tu5c6epXLmyCQwMNOHh4WbZsmXGGN/mq7iaF7MyW57UNca7+XJX0+pslVTXl9kqqa7V+Squpq+z5emfl7ezVVJdq/NVXE1fZuuf//ynufnmm53vyxcfkfJlvkqqezFvZ8zTusZ4PmOe1LQyY+7qWp0xd3WtzFhJNX2ZMW/+vDydMXc1rc5XSXWtzthnn31mFixYYHbs2GGWL19u4uPjTd26dU1OTo5P81VS3Yt5M1+e1jTGu/cvT+pamS93da3Ml7uaVt+/Sqrrj38fXopg7weX/oXZsmWLiYqKMr/88otzm7+C/aUOHDhgJJlVq1ZZqvnLL78YSaZPnz4u67p3727uv/9+v/XasGFDk5KS4nG94mq++uqr5qabbjJLliwxO3bsMG+99ZapUqWKV6fzFlV3y5YtpkWLFkaSCQwMNImJiaZbt26ma9euHtUcOnSoiYuLMz/99JNzmz+CfVF1L2YlfLireeLECXPbbbeZrl27mnPnzvmlbnZ2tvnuu+9MWlqa6d69u2nVqpXHv+Qpqu4nn3xiGjRoYE6ePOnc5k34cPcaFFq9erVXpzYXVXfjxo1GkhkzZozL2mbNmplnnnnGL/2ePn3ahIeHm9dee82jeiXVTElJMbfddptZtWqV2b59uxk/frwJDw83O3fu9KnuihUrTP369Y3D4TCBgYHmoYceMq1atTJDhw4tsV5eXp7Zt2+f2bJli3nmmWdMrVq1zO7du32ar+JqXszKbHlS19v5clfT6mwVV9fX2fLkNSjk6XwVV9PX2fKkVyuzVVJdq/NVUk0rs3Xw4EETGRlpduzY4dzmj2Dvru7FvJkxb+p6OmOe1vR2xtzVtTpj3rwGhdzNmLuaVmfMm149nTFPalqZL0/qWn3/utjx48dNWFiYee+99/zy78Oi6l7Ml8vJiqtp9d+HJdX15d+HRdX19T2spF4v5u2/D4uq649/H16KYO8Hl/6FmTJlinP4C2+STEBAgImLi7Nctzi1atUy06dPt1QzLy/PVKhQwbzwwgsu655++mlzxx13+KXX9evXG0lm+/btHtcrqubp06dNUFCQWbp0qcu6QYMGmcTERL/0mp2dbY4cOWKMMea2224zjz32mNt6ycnJpk6dOub777932V449Jf+T7Vu3brm9ddft1z3Yt7+j9tdzZycHBMfH286d+7s1f9YPem1UF5enqlUqZKZO3eu5bpPPvlksTPWoUMHv/V66tQpI8ksX77ccq/ff/+9kWT+8Y9/uGy/7777zAMPPGC57sU++OADExQU5Py7a7Xm/v37L/u8CWOM6dy5s3n00Uf90uuvv/7q/PsaFRVlJk2a5FHPF/cyZMgQn+erqJoX88c19pfWtTpf7not5M1sFVfXl9nytl9v5quomr7Olie9ejtbJdX1db7c9erNbC1atMj5i+yL/5wL/+xXrVplab7c1b347EJvZszTut7MmDe9FvJkxtzVTUlJsTRjVvp1N2Puahb+nfV2xrzp1dMZ87RXb+fLm159ff+69dZbzTPPPOPX96+L617M1/ewS2v64/2ruF4L+fIeVljXn+9hJfVq9f3r4rr+fg8zxhjPL56Fx/r27XvZNTqJiYnq27evBgwY4NfH+vnnn3X06FHVrl3b0s8HBwerTZs2l3011Xfffae4uDh/tKgZM2aodevWatGihU91zp8/r/Pnz1/2SZGBgYEef/qvO+Hh4ZL+dz3Mli1b9MILLxS71hijxx9/XIsWLdK6detUr149l/2tW7dWUFCQVq9e7fwmgL179+rgwYMlXs/rrq4VntTMyclRYmKiQkJCtGTJErffimC1V/O/Xyg6P9vASt1nnnlGjzzyiMu2Zs2aacqUKerevbvfet2+fbsklThf7upef/31iomJKXLGSvr0X2/6nTFjhn7/+98rIiKixOfjrmbh9WLezpg3vdaqVUuStGbNGh05ckS///3vS+z5UgUFBcrLy7M8XyXV9LeL61qZL3c1L+XJbLmr+/zzz3s9W1b79WS+Sqppdba86dXT2fKkrtX58rRXb2arc+fO2rVrl8u2AQMGqFGjRho9erRiY2MtzZe7uoGBgV49T2/qejtjVnr1ZMbc1a1Vq5YeffRRl/2ezJiVft3NmLua9evXtzRj3vTq6Yy5q2l1vrzp1Zf3r1OnTunAgQPq27evX9+/Lq7rL5fW9Nf7l7terb6HXVz3vvvu88t7mLterb5/XVzX3+9hkrjG3qqTJ0+abdu2mW3bthlJzuvTf/zxxyLXe3oqfkl1T548af70pz+Z9PR0k5GRYVatWmVatWplbrzxRnP27FnLvX788ccmKCjI/P3vfzf79u0zb731lgkMDDT//ve/fX4NTpw4YSpVqmSmTZvm9rl7UrNDhw6madOmZu3ateb77783s2bNMqGhoebtt9/2qe6CBQvM2rVrzYEDB8zixYtNXFyc6dmzZ4k1hw0bZsLDw826devM4cOHnbfTp0871wwdOtTUrVvXrFmzxmzZssXEx8eb+Ph4n+sePnzYbNu2zbz77rtGklm/fr3Ztm2bOXr0qKWaJ06cMG3btjXNmjUz+/fvd1lT0uc3uKt74MAB85e//MVs2bLF/Pjjj2bjxo2me/fupkaNGiYrK8un1+BScnOGi7ua+/fvNxMmTDBbtmwxGRkZ5pNPPjH169c37du3L7amp71OmTLFhIWFmYULF5p9+/aZZ5991oSGhpZ4Cpenr8G+ffuMw+Ewn3/+eYl9elLz3LlzpkGDBuauu+4yX375pdm/f7957bXXjMPhKPb6Y097nTlzpklPTzf79+83//jHP0yNGjXMiBEjSuz3mWeeMWlpaSYjI8Ps3LnTPPPMM8bhcJiVK1caY6zNl7ua3s6WJ3WtzldJNa3OlievwaXczZYnda3Ol7tercyWp6+BN7PlSV2r8+WuVyuzVZRLTz+2Ml+e1LU6YyXVtTpjJdX0ZcZKqlsUT2espLpWZ8xdr1ZnzF1dY6zNWHE1rc6XJ71ambGRI0eadevWmYyMDLNx40bTpUsXU6tWLeeZCVbny11dK/NVUk1fZqukur7Ml7vX4FKezFdJNX2ZLXe9+mu+nM/V0k/BeYrLpbf+/fsXud7TYF9S3dOnT5uEhAQTERFhgoKCTFxcnBk8eLDJzMz0udcZM2aYBg0amNDQUNOiRQuzePFiv7wG77zzjqlYsaLJzs52W8+TmocPHzYPP/ywiYmJMaGhoaZhw4Zm8uTJzg8ptFr3zTffNHXq1DFBQUGmbt265tlnnzV5eXkl1iyqniQza9Ys55ozZ86Yxx57zFSvXt1UqlTJ3Hvvvebw4cM+1x03bpzbNd7ULO71kWQyMjIs9/rLL7+Ybt26mcjISBMUFGTq1KljHnjgAfPtt9/6/BoU9TMl/Y/bXc2DBw+a9u3bmxo1apiQkBDToEEDM2rUKJevuPGl14kTJ5o6deqYSpUqmfj4eLe/OPO07pgxY0xsbKzJz88vsZ6nNb/77jvTs2dPExkZaSpVqmSaN29+2dcHWak7evRoExUVZYKCgsyNN97o0dwOHDjQxMXFmeDgYBMREWE6d+7sEryszJe7mt7Olid1rc5XSTWtzpYnr8GlPA0dJdW1Ol+e9OrtbHla15vZ8rSulflyV9PKbBXl0jBjZb48qWt1xkqqa3XGSqrpy4yVVLco/gj2VmfMk16tzJgnda3MWEk1rcyXJ3WtzFjv3r1N7dq1TXBwsLnuuutM7969XcKa1flyV9fKfJVU05fZKqmuL/Pl7jW4lCfzVVJNX2bLk179MV+FHMYYIwAAAAAAYEt8jz0AAAAAADZGsAcAAAAAwMYI9gAAAAAA2BjBHgAAAAAAGyPYAwAAAABgYwR7AAAAAABsjGAPAAAAAICNEewBAAAAALAxgj0AAAAAADZGsAcAAJakp6crMDBQSUlJZd0KAADXNIcxxpR1EwAAwH4eeeQRValSRTNmzNDevXsVExNT1i0BAHBN4og9AADw2qlTpzR//nwNGzZMSUlJmj17tsv+JUuW6MYbb1RoaKjuvvtuvf/++3I4HMrOznau2bBhg+666y5VrFhRsbGxeuKJJ5Sbm3tlnwgAAFcBgj0AAPDaggUL1KhRIzVs2FAPPfSQZs6cqcKTADMyMvSHP/xBPXr00I4dO/Too4/qz3/+s8vPHzhwQF27dlWvXr20c+dOzZ8/Xxs2bFBKSkpZPB0AAGyNU/EBAIDX2rVrp/vuu09PPvmkLly4oNq1a2vhwoXq2LGjnnnmGS1btky7du1yrn/22Wf10ksv6fjx46pWrZoeeeQRBQYG6p133nGu2bBhgzp06KDc3FyFhoaWxdMCAMCWOGIPAAC8snfvXm3evFl9+vSRJFWoUEG9e/fWjBkznPvbtGnj8jO33Xaby/0dO3Zo9uzZqlKlivOWmJiogoICZWRkXJknAgDAVaJCWTcAAADsZcaMGbpw4YLLh+UZYxQSEqK//e1vHtU4deqUHn30UT3xxBOX7atbt67fegUA4FpAsAcAAB67cOGCPvjgA02ePFkJCQku+3r06KF//vOfatiwoT777DOXfV999ZXL/VatWmnPnj1q0KBBqfcMAMDVjmvsAQCAxxYvXqzevXvryJEjCg8Pd9k3evRorVmzRgsWLFDDhg311FNPadCgQdq+fbtGjhypn3/+WdnZ2QoPD9fOnTt1++23a+DAgXrkkUdUuXJl7dmzR6mpqR4f9QcAAP/DNfYAAMBjM2bMUJcuXS4L9ZLUq1cvbdmyRSdPntS//vUvffzxx2revLmmTZvm/FT8kJAQSVLz5s2Vlpam7777TnfddZduueUWjR071uX0fgAA4BmO2AMAgFL30ksvafr06frpp5/KuhUAAK46XGMPAAD87u2331abNm1Us2ZNbdy4Ua+++irfUQ8AQCkh2AMAAL/bt2+fXnzxRR07dkx169bVyJEjNWbMmLJuCwCAqxKn4gMAAAAAYGN8eB4AAAAAADZGsAcAAAAAwMYI9gAAAAAA2BjBHgAAAAAAGyPYAwAAAABgYwR7AAAAAABsjGAPAAAAAICNEewBAAAAALCx/wetVrxEiIfJfwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "c3QoGsMrDJJm",
        "outputId": "e506956c-d74b-4580-e90f-cd9e2b3995fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       ID Customer_ID     Month           Name  Age          SSN Occupation  \\\n",
              "0  0x1602   CUS_0xd40   January  Aaron Maashoh   23  821-00-0265  Scientist   \n",
              "1  0x1603   CUS_0xd40  February  Aaron Maashoh   23  821-00-0265  Scientist   \n",
              "2  0x1604   CUS_0xd40     March  Aaron Maashoh   23  821-00-0265  Scientist   \n",
              "3  0x1605   CUS_0xd40     April  Aaron Maashoh   23  821-00-0265  Scientist   \n",
              "4  0x1606   CUS_0xd40       May  Aaron Maashoh   23  821-00-0265  Scientist   \n",
              "\n",
              "  Annual_Income  Monthly_Inhand_Salary  Num_Bank_Accounts  ...  Credit_Mix  \\\n",
              "0      19114.12            1824.843333                  3  ...        Good   \n",
              "1      19114.12            1824.843333                  3  ...        Good   \n",
              "2      19114.12            1824.843333                  3  ...        Good   \n",
              "3      19114.12            1824.843333                  3  ...        Good   \n",
              "4      19114.12            1824.843333                  3  ...        Good   \n",
              "\n",
              "   Outstanding_Debt  Credit_Utilization_Ratio     Credit_History_Age  \\\n",
              "0            809.98                 26.822620  22 Years and 1 Months   \n",
              "1            809.98                 31.944960                    NaN   \n",
              "2            809.98                 28.609352  22 Years and 3 Months   \n",
              "3            809.98                 31.377862  22 Years and 4 Months   \n",
              "4            809.98                 24.797347  22 Years and 5 Months   \n",
              "\n",
              "   Payment_of_Min_Amount  Total_EMI_per_month  Amount_invested_monthly  \\\n",
              "0                     No            49.574949              80.41529544   \n",
              "1                     No            49.574949              118.2802216   \n",
              "2                     No            49.574949              81.69952126   \n",
              "3                     No            49.574949              199.4580744   \n",
              "4                     No            49.574949              41.42015309   \n",
              "\n",
              "                  Payment_Behaviour Monthly_Balance  \\\n",
              "0   High_spent_Small_value_payments      312.494089   \n",
              "1    Low_spent_Large_value_payments      284.629163   \n",
              "2   Low_spent_Medium_value_payments      331.209863   \n",
              "3    Low_spent_Small_value_payments      223.451310   \n",
              "4  High_spent_Medium_value_payments      341.489231   \n",
              "\n",
              "  Credit_History_Age_in_years  \n",
              "0                          22  \n",
              "1                          22  \n",
              "2                          22  \n",
              "3                          22  \n",
              "4                          22  \n",
              "\n",
              "[5 rows x 28 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7d731bc2-a0b1-4d53-a990-edbf58815832\" class=\"colab-df-container\">\n",
              "    <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>ID</th>\n",
              "      <th>Customer_ID</th>\n",
              "      <th>Month</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>SSN</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Annual_Income</th>\n",
              "      <th>Monthly_Inhand_Salary</th>\n",
              "      <th>Num_Bank_Accounts</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_History_Age_in_years</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0x1602</td>\n",
              "      <td>CUS_0xd40</td>\n",
              "      <td>January</td>\n",
              "      <td>Aaron Maashoh</td>\n",
              "      <td>23</td>\n",
              "      <td>821-00-0265</td>\n",
              "      <td>Scientist</td>\n",
              "      <td>19114.12</td>\n",
              "      <td>1824.843333</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>Good</td>\n",
              "      <td>809.98</td>\n",
              "      <td>26.822620</td>\n",
              "      <td>22 Years and 1 Months</td>\n",
              "      <td>No</td>\n",
              "      <td>49.574949</td>\n",
              "      <td>80.41529544</td>\n",
              "      <td>High_spent_Small_value_payments</td>\n",
              "      <td>312.494089</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0x1603</td>\n",
              "      <td>CUS_0xd40</td>\n",
              "      <td>February</td>\n",
              "      <td>Aaron Maashoh</td>\n",
              "      <td>23</td>\n",
              "      <td>821-00-0265</td>\n",
              "      <td>Scientist</td>\n",
              "      <td>19114.12</td>\n",
              "      <td>1824.843333</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>Good</td>\n",
              "      <td>809.98</td>\n",
              "      <td>31.944960</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>49.574949</td>\n",
              "      <td>118.2802216</td>\n",
              "      <td>Low_spent_Large_value_payments</td>\n",
              "      <td>284.629163</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0x1604</td>\n",
              "      <td>CUS_0xd40</td>\n",
              "      <td>March</td>\n",
              "      <td>Aaron Maashoh</td>\n",
              "      <td>23</td>\n",
              "      <td>821-00-0265</td>\n",
              "      <td>Scientist</td>\n",
              "      <td>19114.12</td>\n",
              "      <td>1824.843333</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>Good</td>\n",
              "      <td>809.98</td>\n",
              "      <td>28.609352</td>\n",
              "      <td>22 Years and 3 Months</td>\n",
              "      <td>No</td>\n",
              "      <td>49.574949</td>\n",
              "      <td>81.69952126</td>\n",
              "      <td>Low_spent_Medium_value_payments</td>\n",
              "      <td>331.209863</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0x1605</td>\n",
              "      <td>CUS_0xd40</td>\n",
              "      <td>April</td>\n",
              "      <td>Aaron Maashoh</td>\n",
              "      <td>23</td>\n",
              "      <td>821-00-0265</td>\n",
              "      <td>Scientist</td>\n",
              "      <td>19114.12</td>\n",
              "      <td>1824.843333</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>Good</td>\n",
              "      <td>809.98</td>\n",
              "      <td>31.377862</td>\n",
              "      <td>22 Years and 4 Months</td>\n",
              "      <td>No</td>\n",
              "      <td>49.574949</td>\n",
              "      <td>199.4580744</td>\n",
              "      <td>Low_spent_Small_value_payments</td>\n",
              "      <td>223.451310</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0x1606</td>\n",
              "      <td>CUS_0xd40</td>\n",
              "      <td>May</td>\n",
              "      <td>Aaron Maashoh</td>\n",
              "      <td>23</td>\n",
              "      <td>821-00-0265</td>\n",
              "      <td>Scientist</td>\n",
              "      <td>19114.12</td>\n",
              "      <td>1824.843333</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>Good</td>\n",
              "      <td>809.98</td>\n",
              "      <td>24.797347</td>\n",
              "      <td>22 Years and 5 Months</td>\n",
              "      <td>No</td>\n",
              "      <td>49.574949</td>\n",
              "      <td>41.42015309</td>\n",
              "      <td>High_spent_Medium_value_payments</td>\n",
              "      <td>341.489231</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 28 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7d731bc2-a0b1-4d53-a990-edbf58815832')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7d731bc2-a0b1-4d53-a990-edbf58815832 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7d731bc2-a0b1-4d53-a990-edbf58815832');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-525f702c-c567-41c7-9b0b-9e182ed59372\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-525f702c-c567-41c7-9b0b-9e182ed59372')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-525f702c-c567-41c7-9b0b-9e182ed59372 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#fig,axes=plt.subplots(nrows=1,ncols=2,figsize=(18,6))\n",
        "#sns.countplot(df['Num_of_Delayed_Payment'],ax=axes[0])\n",
        "#sns.countplot(df['Num_Credit_Inquiries'],ax=axes[0])"
      ],
      "metadata": {
        "id": "0ywJtkidJJEp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig,axes=plt.subplots(nrows=2,ncols=2,figsize=(12,7))\n",
        "sns.countplot(x=df['Num_Bank_Accounts'],ax=axes[0,0])\n",
        "sns.countplot(x=df['Num_Credit_Card'],ax=axes[0,1])\n",
        "sns.countplot(x=df['Num_of_Loan'],ax=axes[1,0])\n",
        "sns.countplot(x=df['Payment_of_Min_Amount'],ax=axes[1,1])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 636
        },
        "id": "O9xzafsBF7p2",
        "outputId": "e1c0517c-bc0e-40b3-db86-86dda3230723"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Payment_of_Min_Amount', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Insights:**\n",
        "1. Monthly salary follows a right skewed distribution. Which can be made to Gaussian distribution if need for any analysis\n",
        "2. Most of the people have 3 to 8 bank accounts and use 3-7 credit cards\n",
        "3. There appears to be a positive trend between the number of bank accounts and the number of credit cards. This suggests that individuals are likely obtaining credit cards from the banks where they hold their accounts. In many cases, having three bank accounts corresponds to holding approximately three credit cards. This correlation indicates a pattern where people tend to acquire credit cards from their existing banking relationships.\n",
        "4. There suggests a positive relationship between the number of bank loans and the number of credit cards, with people who have more loans generally holding more credit cards. However, the significant variability also indicates that the relationship is influenced by individual financial behavior, with some people managing both loans and credit cards actively while others may limit their credit card use as their number of loans increases."
      ],
      "metadata": {
        "id": "o1iLw6jcG6DH"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y1k57ndSq4XN"
      },
      "source": [
        "**Computing the Credit Score**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zf6tnYxL-dYC"
      },
      "outputs": [],
      "source": [
        "def payment_history_score(row):\n",
        "\n",
        "\n",
        "\n",
        "  base_score=100\n",
        "  if row['Num_of_Loan']<=0:\n",
        "   base_score=base_score\n",
        "  elif row['Num_of_Loan']<=7:\n",
        "   base_score=base_score-5\n",
        "  else:\n",
        "   base_score= base_score-10\n",
        "\n",
        "  if row['Num_of_Delayed_Payment']==0:\n",
        "   base_score=base_score\n",
        "  elif 1<=row['Num_of_Delayed_Payment']<=3:\n",
        "   base_score=base_score-5\n",
        "  elif 4<=row['Num_of_Delayed_Payment']<=7:\n",
        "   base_score=base_score-10\n",
        "  else:\n",
        "   base_score=base_score-15\n",
        "\n",
        "\n",
        "\n",
        "  if row['Delay_from_due_date']==0:\n",
        "   base_score=base_score\n",
        "  elif 1<=row['Delay_from_due_date']<=3:\n",
        "   base_score=base_score-2\n",
        "  elif 4<= row['Delay_from_due_date']<=7:\n",
        "   base_score=base_score-3\n",
        "  else:\n",
        "   base_score=base_score-5\n",
        "\n",
        "\n",
        "  return base_score\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fzDbQXH_GaNN"
      },
      "outputs": [],
      "source": [
        "df['Payment_history_score']=df.apply(payment_history_score,axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hs2PRG6cHoQl"
      },
      "outputs": [],
      "source": [
        "def credit_Utilization(col):\n",
        "  if col < 10 :\n",
        "    return 100\n",
        "  elif 10< col < 30:\n",
        "    return 95\n",
        "  elif 30 <col <50:\n",
        "    return 85\n",
        "  elif 50<col<70:\n",
        "    return 75\n",
        "  else:\n",
        "    return 55"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2K6FDtv44vkT"
      },
      "outputs": [],
      "source": [
        "bins = [0.0,9.0,30.0,50.0,70.0,100.0]\n",
        "labels=[100,95,85,70,55]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x0V-u8l_y_13"
      },
      "outputs": [],
      "source": [
        "df['Credit_Utilization_score']=df['Credit_Utilization_Ratio'].apply(credit_Utilization)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LtP6sSYD7O12"
      },
      "outputs": [],
      "source": [
        "credit_mix_mapping={\n",
        "    'Good': 80,\n",
        "    'Standard' : 60,\n",
        "    'Bad' : 30\n",
        "    }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "353PAalC8Pp_"
      },
      "outputs": [],
      "source": [
        "df['Credit_Mix_score']=df['Credit_Mix'].map(credit_mix_mapping)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pv9rT9QMEADg"
      },
      "outputs": [],
      "source": [
        "def credit_history_length(years):\n",
        "  if years < 10 :\n",
        "    return 30\n",
        "  elif 10< years < 20:\n",
        "    return 60\n",
        "  else:\n",
        "    return 90"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dfUx1mhzEgbj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "d4a4dd43-c749-4cfa-d48d-cbe8b53f4c9b"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "'<' not supported between instances of 'NaTType' and 'int'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-78-8944bf7adf10>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Credit_length_score'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Credit_History_Age_in_years'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcredit_history_length\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, arg, na_action)\u001b[0m\n\u001b[1;32m   4395\u001b[0m         \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4396\u001b[0m         \"\"\"\n\u001b[0;32m-> 4397\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_map_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_action\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_action\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4398\u001b[0m         return self._constructor(new_values, index=self.index, copy=False).__finalize__(\n\u001b[1;32m   4399\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"map\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m_map_values\u001b[0;34m(self, mapper, na_action)\u001b[0m\n\u001b[1;32m    922\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    923\u001b[0m         \u001b[0;31m# mapper is a function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 924\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap_f\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    925\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    926\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnew_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m<ipython-input-74-ad728db76eb3>\u001b[0m in \u001b[0;36mcredit_history_length\u001b[0;34m(years)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcredit_history_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myears\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m   \u001b[0;32mif\u001b[0m \u001b[0myears\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m10\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;36m30\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0;32melif\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m<\u001b[0m \u001b[0myears\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;36m60\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: '<' not supported between instances of 'NaTType' and 'int'"
          ]
        }
      ],
      "source": [
        "df['Credit_length_score']=df['Credit_History_Age_in_years'].map(credit_history_length)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Uw2D0B3o8yuP"
      },
      "outputs": [],
      "source": [
        "df['Outstanding_Debt']=df['Outstanding_Debt'].str.extract(r'([\\d.]+)').astype(float)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-gZs2UpG55y2"
      },
      "outputs": [],
      "source": [
        "df['Debt_Income_Ratio']=(df['Outstanding_Debt']/df['Monthly_Inhand_Salary'])*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "05jy9i-IBRjz",
        "outputId": "23286b46-c7ad-4a29-800a-b7ce83a2fc18"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1276.2422821438645"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "df['Debt_Income_Ratio'].max()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bE2EM6sYBB1w"
      },
      "outputs": [],
      "source": [
        "def DTI_Score(col):\n",
        "  if col<30:\n",
        "    return 100\n",
        "  elif 31<col<40:\n",
        "    return 85\n",
        "  elif 41<col< 55:\n",
        "    return 70\n",
        "  else:\n",
        "    return 50"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Pq37AAj5B-c6"
      },
      "outputs": [],
      "source": [
        "df['Debt_to_income_score']=df['Debt_Income_Ratio'].apply(DTI_Score)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "m8qhc3MrEzF1",
        "outputId": "72821bb6-f892-49ef-d2bb-107b240c4eea"
      },
      "outputs": [
        {
          "data": {
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              "type": "dataframe",
              "variable_name": "df"
            },
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Customer_ID</th>\n",
              "      <th>Month</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>SSN</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Annual_Income</th>\n",
              "      <th>Monthly_Inhand_Salary</th>\n",
              "      <th>Num_Bank_Accounts</th>\n",
              "      <th>...</th>\n",
              "      <th>Amount_invested_monthly</th>\n",
              "      <th>Payment_Behaviour</th>\n",
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              "      <th>Credit_Utilization_score</th>\n",
              "      <th>Credit_Mix_score</th>\n",
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              "      <th>Debt_Income_Ratio</th>\n",
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              "      <td>Aaron Maashoh</td>\n",
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              "      <td>312.494089</td>\n",
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              "      <td>Aaron Maashoh</td>\n",
              "      <td>23</td>\n",
              "      <td>821-00-0265</td>\n",
              "      <td>Scientist</td>\n",
              "      <td>19114.12</td>\n",
              "      <td>1824.843333</td>\n",
              "      <td>3</td>\n",
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              "      <td>118.280222</td>\n",
              "      <td>Low_spent_Large_value_payments</td>\n",
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              "      <td>CUS_0xd40</td>\n",
              "      <td>March</td>\n",
              "      <td>Aaron Maashoh</td>\n",
              "      <td>23</td>\n",
              "      <td>821-00-0265</td>\n",
              "      <td>Scientist</td>\n",
              "      <td>19114.12</td>\n",
              "      <td>1824.843333</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>81.699521</td>\n",
              "      <td>Low_spent_Medium_value_payments</td>\n",
              "      <td>331.209863</td>\n",
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              "      <th>3</th>\n",
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              "      <td>April</td>\n",
              "      <td>Aaron Maashoh</td>\n",
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              "      <td>19114.12</td>\n",
              "      <td>1824.843333</td>\n",
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              "      <td>223.451310</td>\n",
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              "      <td>CUS_0xd40</td>\n",
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              "      <td>Aaron Maashoh</td>\n",
              "      <td>23</td>\n",
              "      <td>821-00-0265</td>\n",
              "      <td>Scientist</td>\n",
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              "      <td>1824.843333</td>\n",
              "      <td>3</td>\n",
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              "      <td>41.420153</td>\n",
              "      <td>High_spent_Medium_value_payments</td>\n",
              "      <td>341.489231</td>\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-efc79e51-ef26-47d7-8bd8-f1ae42133972 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       ID Customer_ID     Month           Name  Age          SSN Occupation  \\\n",
              "0  0x1602   CUS_0xd40   January  Aaron Maashoh   23  821-00-0265  Scientist   \n",
              "1  0x1603   CUS_0xd40  February  Aaron Maashoh   23  821-00-0265  Scientist   \n",
              "2  0x1604   CUS_0xd40     March  Aaron Maashoh   23  821-00-0265  Scientist   \n",
              "3  0x1605   CUS_0xd40     April  Aaron Maashoh   23  821-00-0265  Scientist   \n",
              "4  0x1606   CUS_0xd40       May  Aaron Maashoh   23  821-00-0265  Scientist   \n",
              "\n",
              "   Annual_Income  Monthly_Inhand_Salary  Num_Bank_Accounts  ...  \\\n",
              "0       19114.12            1824.843333                  3  ...   \n",
              "1       19114.12            1824.843333                  3  ...   \n",
              "2       19114.12            1824.843333                  3  ...   \n",
              "3       19114.12            1824.843333                  3  ...   \n",
              "4       19114.12            1824.843333                  3  ...   \n",
              "\n",
              "   Amount_invested_monthly                 Payment_Behaviour  Monthly_Balance  \\\n",
              "0                80.415295   High_spent_Small_value_payments       312.494089   \n",
              "1               118.280222    Low_spent_Large_value_payments       284.629163   \n",
              "2                81.699521   Low_spent_Medium_value_payments       331.209863   \n",
              "3               199.458074    Low_spent_Small_value_payments       223.451310   \n",
              "4                41.420153  High_spent_Medium_value_payments       341.489231   \n",
              "\n",
              "  Credit_History_Age_in_years  Payment_history_score  \\\n",
              "0                          22                     83   \n",
              "1                          22                     80   \n",
              "2                          22                     83   \n",
              "3                          22                     82   \n",
              "4                          22                     82   \n",
              "\n",
              "   Credit_Utilization_score  Credit_Mix_score  Credit_length_score  \\\n",
              "0                        95                80                   90   \n",
              "1                        85                80                   90   \n",
              "2                        95                80                   90   \n",
              "3                        85                80                   90   \n",
              "4                        95                80                   90   \n",
              "\n",
              "  Debt_Income_Ratio  Debt_to_income_score  \n",
              "0         44.386276                    70  \n",
              "1         44.386276                    70  \n",
              "2         44.386276                    70  \n",
              "3         44.386276                    70  \n",
              "4         44.386276                    70  \n",
              "\n",
              "[5 rows x 34 columns]"
            ]
          },
          "execution_count": 82,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_1uwU3SJRln"
      },
      "source": [
        "Creating **'CENT_SCORE'**(personalized credit score).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fh9KiJDxFngm"
      },
      "outputs": [],
      "source": [
        "df['CENT_value']=(df['Payment_history_score'] * 0.35) + (df['Credit_Utilization_score'] *0.30) + (df['Credit_length_score']*0.15) + (df['Credit_Mix_score']*0.10) + (df['Debt_to_income_score']*0.10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lIyAP4OwHLdJ"
      },
      "outputs": [],
      "source": [
        "df['CENT_SCORE']=df['CENT_value']*8.5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 957
        },
        "id": "nemJJCAtVVA6",
        "outputId": "f40b2e44-7d4a-4025-bb6f-2fc4013ab126"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            ID Customer_ID    Month            Name  Age          SSN  \\\n",
              "8108    0x4582  CUS_0x88f2      May  Himank Sharmag   26  460-29-2746   \n",
              "8110    0x4584  CUS_0x88f2     July  Himank Sharmag   26  460-29-2746   \n",
              "30920   0xcb2e  CUS_0x2767  January      Krisztinaq   44  749-94-3595   \n",
              "39904   0xffd2  CUS_0x3b36  January           Nater   43  814-35-2546   \n",
              "39911   0xffd9  CUS_0x3b36   August           Nater   44  814-35-2546   \n",
              "49380  0x13756  CUS_0x7699      May        Miyoungx   22  152-69-5817   \n",
              "54618  0x15608  CUS_0x8fa8    March          Marjad   51  774-87-0981   \n",
              "54623  0x1560d  CUS_0x8fa8   August          Marjad   51  774-87-0981   \n",
              "65463  0x19591  CUS_0x39ae   August      Rudegeairu   45  267-92-4521   \n",
              "66840  0x19da6   CUS_0xb4f  January       Maltezouf   50  910-76-8436   \n",
              "74535  0x1cab9  CUS_0x614f   August           Rhysj   46  622-65-0067   \n",
              "89212  0x220ba  CUS_0x9da4      May       Carolineb   18  853-63-1222   \n",
              "89215  0x220bd  CUS_0x9da4   August       Carolineb   18  853-63-1222   \n",
              "\n",
              "          Occupation Annual_Income  Monthly_Inhand_Salary  Num_Bank_Accounts  \\\n",
              "8108   Media_Manager      17028.31            1663.025833                  4   \n",
              "8110         _______      17028.31            1663.025833                  4   \n",
              "30920  Media_Manager    19251.265_            1445.272083                  0   \n",
              "39904        _______      79205.36            6520.446667                  1   \n",
              "39911      Scientist      79205.36            6520.446667                  1   \n",
              "49380      Developer     157299.48           13009.290000                  3   \n",
              "54618        _______      40639.06            3572.588333                  1   \n",
              "54623  Media_Manager      40639.06            3572.588333                  1   \n",
              "65463      Developer      88203.74            7415.311667                  4   \n",
              "66840     Journalist       56622.3            4469.525000                  1   \n",
              "74535        Manager      44624.66            3696.721667                  1   \n",
              "89212      Architect      77380.48            6618.373333                  5   \n",
              "89215      Architect      77380.48            6229.819072                  5   \n",
              "\n",
              "       ...  Monthly_Balance  Credit_History_Age_in_years  \\\n",
              "8108   ...       285.591219                           30   \n",
              "8110   ...       386.143826                           30   \n",
              "30920  ...       381.223691                           27   \n",
              "39904  ...       833.600635                           24   \n",
              "39911  ...       477.969791                           25   \n",
              "49380  ...       479.626724                           33   \n",
              "54618  ...       380.297736                           23   \n",
              "54623  ...       521.965855                           23   \n",
              "65463  ...       379.171036                           22   \n",
              "66840  ...       516.530490                           28   \n",
              "74535  ...       549.895219                           33   \n",
              "89212  ...       795.730890                           20   \n",
              "89215  ...       802.340394                           20   \n",
              "\n",
              "       Payment_history_score Credit_Utilization_score  Credit_Mix_score  \\\n",
              "8108                      98                       95                80   \n",
              "8110                      98                       95                80   \n",
              "30920                     98                       95                80   \n",
              "39904                     98                       95                80   \n",
              "39911                    100                       95                80   \n",
              "49380                     98                       95                80   \n",
              "54618                    100                       95                80   \n",
              "54623                    100                       95                80   \n",
              "65463                     98                       95                80   \n",
              "66840                     98                       95                80   \n",
              "74535                     98                       95                80   \n",
              "89212                    100                       95                80   \n",
              "89215                    100                       95                80   \n",
              "\n",
              "       Credit_length_score  Debt_Income_Ratio  Debt_to_income_score  \\\n",
              "8108                    90           2.586851                   100   \n",
              "8110                    90           2.586851                   100   \n",
              "30920                   90          15.388106                   100   \n",
              "39904                   90          21.151005                   100   \n",
              "39911                   90          21.151005                   100   \n",
              "49380                   90           5.004654                   100   \n",
              "54618                   90          23.901998                   100   \n",
              "54623                   90          23.901998                   100   \n",
              "65463                   90          15.176031                   100   \n",
              "66840                   90           2.465363                   100   \n",
              "74535                   90           5.055560                   100   \n",
              "89212                   90           7.348331                   100   \n",
              "89215                   90           7.806647                   100   \n",
              "\n",
              "      CENT_value  CENT_SCORE  \n",
              "8108        94.3      801.55  \n",
              "8110        94.3      801.55  \n",
              "30920       94.3      801.55  \n",
              "39904       94.3      801.55  \n",
              "39911       95.0      807.50  \n",
              "49380       94.3      801.55  \n",
              "54618       95.0      807.50  \n",
              "54623       95.0      807.50  \n",
              "65463       94.3      801.55  \n",
              "66840       94.3      801.55  \n",
              "74535       94.3      801.55  \n",
              "89212       95.0      807.50  \n",
              "89215       95.0      807.50  \n",
              "\n",
              "[13 rows x 36 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4d2fcdcc-af6f-4e3c-867a-78a88cd27d06\" class=\"colab-df-container\">\n",
              "    <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>ID</th>\n",
              "      <th>Customer_ID</th>\n",
              "      <th>Month</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>SSN</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Annual_Income</th>\n",
              "      <th>Monthly_Inhand_Salary</th>\n",
              "      <th>Num_Bank_Accounts</th>\n",
              "      <th>...</th>\n",
              "      <th>Monthly_Balance</th>\n",
              "      <th>Credit_History_Age_in_years</th>\n",
              "      <th>Payment_history_score</th>\n",
              "      <th>Credit_Utilization_score</th>\n",
              "      <th>Credit_Mix_score</th>\n",
              "      <th>Credit_length_score</th>\n",
              "      <th>Debt_Income_Ratio</th>\n",
              "      <th>Debt_to_income_score</th>\n",
              "      <th>CENT_value</th>\n",
              "      <th>CENT_SCORE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8108</th>\n",
              "      <td>0x4582</td>\n",
              "      <td>CUS_0x88f2</td>\n",
              "      <td>May</td>\n",
              "      <td>Himank Sharmag</td>\n",
              "      <td>26</td>\n",
              "      <td>460-29-2746</td>\n",
              "      <td>Media_Manager</td>\n",
              "      <td>17028.31</td>\n",
              "      <td>1663.025833</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>285.591219</td>\n",
              "      <td>30</td>\n",
              "      <td>98</td>\n",
              "      <td>95</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>2.586851</td>\n",
              "      <td>100</td>\n",
              "      <td>94.3</td>\n",
              "      <td>801.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8110</th>\n",
              "      <td>0x4584</td>\n",
              "      <td>CUS_0x88f2</td>\n",
              "      <td>July</td>\n",
              "      <td>Himank Sharmag</td>\n",
              "      <td>26</td>\n",
              "      <td>460-29-2746</td>\n",
              "      <td>_______</td>\n",
              "      <td>17028.31</td>\n",
              "      <td>1663.025833</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>386.143826</td>\n",
              "      <td>30</td>\n",
              "      <td>98</td>\n",
              "      <td>95</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>2.586851</td>\n",
              "      <td>100</td>\n",
              "      <td>94.3</td>\n",
              "      <td>801.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30920</th>\n",
              "      <td>0xcb2e</td>\n",
              "      <td>CUS_0x2767</td>\n",
              "      <td>January</td>\n",
              "      <td>Krisztinaq</td>\n",
              "      <td>44</td>\n",
              "      <td>749-94-3595</td>\n",
              "      <td>Media_Manager</td>\n",
              "      <td>19251.265_</td>\n",
              "      <td>1445.272083</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>381.223691</td>\n",
              "      <td>27</td>\n",
              "      <td>98</td>\n",
              "      <td>95</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>15.388106</td>\n",
              "      <td>100</td>\n",
              "      <td>94.3</td>\n",
              "      <td>801.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39904</th>\n",
              "      <td>0xffd2</td>\n",
              "      <td>CUS_0x3b36</td>\n",
              "      <td>January</td>\n",
              "      <td>Nater</td>\n",
              "      <td>43</td>\n",
              "      <td>814-35-2546</td>\n",
              "      <td>_______</td>\n",
              "      <td>79205.36</td>\n",
              "      <td>6520.446667</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>833.600635</td>\n",
              "      <td>24</td>\n",
              "      <td>98</td>\n",
              "      <td>95</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>21.151005</td>\n",
              "      <td>100</td>\n",
              "      <td>94.3</td>\n",
              "      <td>801.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39911</th>\n",
              "      <td>0xffd9</td>\n",
              "      <td>CUS_0x3b36</td>\n",
              "      <td>August</td>\n",
              "      <td>Nater</td>\n",
              "      <td>44</td>\n",
              "      <td>814-35-2546</td>\n",
              "      <td>Scientist</td>\n",
              "      <td>79205.36</td>\n",
              "      <td>6520.446667</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>477.969791</td>\n",
              "      <td>25</td>\n",
              "      <td>100</td>\n",
              "      <td>95</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>21.151005</td>\n",
              "      <td>100</td>\n",
              "      <td>95.0</td>\n",
              "      <td>807.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49380</th>\n",
              "      <td>0x13756</td>\n",
              "      <td>CUS_0x7699</td>\n",
              "      <td>May</td>\n",
              "      <td>Miyoungx</td>\n",
              "      <td>22</td>\n",
              "      <td>152-69-5817</td>\n",
              "      <td>Developer</td>\n",
              "      <td>157299.48</td>\n",
              "      <td>13009.290000</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>479.626724</td>\n",
              "      <td>33</td>\n",
              "      <td>98</td>\n",
              "      <td>95</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>5.004654</td>\n",
              "      <td>100</td>\n",
              "      <td>94.3</td>\n",
              "      <td>801.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54618</th>\n",
              "      <td>0x15608</td>\n",
              "      <td>CUS_0x8fa8</td>\n",
              "      <td>March</td>\n",
              "      <td>Marjad</td>\n",
              "      <td>51</td>\n",
              "      <td>774-87-0981</td>\n",
              "      <td>_______</td>\n",
              "      <td>40639.06</td>\n",
              "      <td>3572.588333</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>380.297736</td>\n",
              "      <td>23</td>\n",
              "      <td>100</td>\n",
              "      <td>95</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>23.901998</td>\n",
              "      <td>100</td>\n",
              "      <td>95.0</td>\n",
              "      <td>807.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54623</th>\n",
              "      <td>0x1560d</td>\n",
              "      <td>CUS_0x8fa8</td>\n",
              "      <td>August</td>\n",
              "      <td>Marjad</td>\n",
              "      <td>51</td>\n",
              "      <td>774-87-0981</td>\n",
              "      <td>Media_Manager</td>\n",
              "      <td>40639.06</td>\n",
              "      <td>3572.588333</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>521.965855</td>\n",
              "      <td>23</td>\n",
              "      <td>100</td>\n",
              "      <td>95</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>23.901998</td>\n",
              "      <td>100</td>\n",
              "      <td>95.0</td>\n",
              "      <td>807.50</td>\n",
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              "    <tr>\n",
              "      <th>65463</th>\n",
              "      <td>0x19591</td>\n",
              "      <td>CUS_0x39ae</td>\n",
              "      <td>August</td>\n",
              "      <td>Rudegeairu</td>\n",
              "      <td>45</td>\n",
              "      <td>267-92-4521</td>\n",
              "      <td>Developer</td>\n",
              "      <td>88203.74</td>\n",
              "      <td>7415.311667</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>379.171036</td>\n",
              "      <td>22</td>\n",
              "      <td>98</td>\n",
              "      <td>95</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>15.176031</td>\n",
              "      <td>100</td>\n",
              "      <td>94.3</td>\n",
              "      <td>801.55</td>\n",
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              "    <tr>\n",
              "      <th>66840</th>\n",
              "      <td>0x19da6</td>\n",
              "      <td>CUS_0xb4f</td>\n",
              "      <td>January</td>\n",
              "      <td>Maltezouf</td>\n",
              "      <td>50</td>\n",
              "      <td>910-76-8436</td>\n",
              "      <td>Journalist</td>\n",
              "      <td>56622.3</td>\n",
              "      <td>4469.525000</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>516.530490</td>\n",
              "      <td>28</td>\n",
              "      <td>98</td>\n",
              "      <td>95</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>2.465363</td>\n",
              "      <td>100</td>\n",
              "      <td>94.3</td>\n",
              "      <td>801.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74535</th>\n",
              "      <td>0x1cab9</td>\n",
              "      <td>CUS_0x614f</td>\n",
              "      <td>August</td>\n",
              "      <td>Rhysj</td>\n",
              "      <td>46</td>\n",
              "      <td>622-65-0067</td>\n",
              "      <td>Manager</td>\n",
              "      <td>44624.66</td>\n",
              "      <td>3696.721667</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>549.895219</td>\n",
              "      <td>33</td>\n",
              "      <td>98</td>\n",
              "      <td>95</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>5.055560</td>\n",
              "      <td>100</td>\n",
              "      <td>94.3</td>\n",
              "      <td>801.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89212</th>\n",
              "      <td>0x220ba</td>\n",
              "      <td>CUS_0x9da4</td>\n",
              "      <td>May</td>\n",
              "      <td>Carolineb</td>\n",
              "      <td>18</td>\n",
              "      <td>853-63-1222</td>\n",
              "      <td>Architect</td>\n",
              "      <td>77380.48</td>\n",
              "      <td>6618.373333</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>795.730890</td>\n",
              "      <td>20</td>\n",
              "      <td>100</td>\n",
              "      <td>95</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>7.348331</td>\n",
              "      <td>100</td>\n",
              "      <td>95.0</td>\n",
              "      <td>807.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89215</th>\n",
              "      <td>0x220bd</td>\n",
              "      <td>CUS_0x9da4</td>\n",
              "      <td>August</td>\n",
              "      <td>Carolineb</td>\n",
              "      <td>18</td>\n",
              "      <td>853-63-1222</td>\n",
              "      <td>Architect</td>\n",
              "      <td>77380.48</td>\n",
              "      <td>6229.819072</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>802.340394</td>\n",
              "      <td>20</td>\n",
              "      <td>100</td>\n",
              "      <td>95</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>7.806647</td>\n",
              "      <td>100</td>\n",
              "      <td>95.0</td>\n",
              "      <td>807.50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "df[df['CENT_SCORE']>800]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M-OJxeDXdIpM"
      },
      "source": [
        "**Outline for Calculation of CENTSCORE**\n",
        "\n",
        "**Note**: CENT score is a personalized score ispired by FICO score. The difference b/w FICO score and CENT score is FICO score  does not take Debt to income ratio while CENT does and also weight system is been adjusted.\n",
        "\n",
        "CENT SCORE IS calculated based on five primary factors, each with its own weight in the scoring model.Here's a breakdown of these factors:\n",
        "\n",
        "1. Payment History (35%)\n",
        "2.Credit Utilization (30%)\n",
        "3.Length of Credit History(15%)\n",
        "4.Credi Mix (10%)\n",
        "5.Debt to Income Ratio (10%)\n",
        "\n",
        "***`Step1 : Assigning scores***`\n",
        "\n",
        "--> Each of this Factor is given score based upon different thresholds.\n",
        "1.For Payment History Base score is set to 100 and factors such as [No of Loan take, No of Delayed payments , Delay from Due date] are taken into considertaion and score has been adjusted to each user based upon the defaults *made*\n",
        "2. For Credit utilization, score has been allocated based upon creditutilization ratio for example if a person has 24% he would be get a score of 90\n",
        "3. Similarly scores have been adjusted for other factors as well\n",
        "\n",
        "\n",
        "**`Step 2: Calculating cent value`**\n",
        "We have calculated cent value using weighted avg method.Different weights have been assigned to different factors\n",
        "\n",
        "\n",
        "CENT_value = Payment History Score*(0.35) +\n",
        "            Credit utilization score * (0.30) +\n",
        "            Credit length score * (1.15)\n",
        "            Credit Mix Score  * (0.10)+\n",
        "            Debt to Income Ratio Score * (0.10)\n",
        "\n",
        "***`Step 3: Calculating CENT Score`***\n",
        "\n",
        "We got a cent value bu, it is not actual credit score to get actual score we have to normalize the cent. The formuala used here to so do is\n",
        "\n",
        "** CENT SCORE = cent value * (850/100) **\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OejcEsB9soOU"
      },
      "source": [
        "**Understanding the Correlation between different Numerical Vriables**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qhqYNRIWrP0e"
      },
      "outputs": [],
      "source": [
        "    df_new=df[['Annual_Income','Monthly_Inhand_Salary','Num_of_Loan','CENT_SCORE','Payment_history_score','Credit_Utilization_score','Credit_length_score','Credit_Mix_score','Debt_to_income_score']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "VfCzORj0q9b9",
        "outputId": "f7351363-e09e-44f2-b867-e088ede2f378"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "could not convert string to float: '34847.84_'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-51-542de67d563f>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcorrelation_matrix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf_new\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheatmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcorrelation_matrix\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mannot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"YlGnBu\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mcorr\u001b[0;34m(self, method, min_periods, numeric_only)\u001b[0m\n\u001b[1;32m  10052\u001b[0m         \u001b[0mcols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10053\u001b[0m         \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m> 10054\u001b[0;31m         \u001b[0mmat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m  10055\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10056\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"pearson\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mto_numpy\u001b[0;34m(self, dtype, copy, na_value)\u001b[0m\n\u001b[1;32m   1836\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1837\u001b[0m             \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1839\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1840\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mas_array\u001b[0;34m(self, dtype, copy, na_value)\u001b[0m\n\u001b[1;32m   1730\u001b[0m                 \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwriteable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1731\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1732\u001b[0;31m             \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_interleave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1733\u001b[0m             \u001b[0;31m# The underlying data was copied within _interleave, so no need\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1734\u001b[0m             \u001b[0;31m# to further copy if copy=True or setting na_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36m_interleave\u001b[0;34m(self, dtype, na_value)\u001b[0m\n\u001b[1;32m   1792\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1793\u001b[0m                 \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mblk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1794\u001b[0;31m             \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1795\u001b[0m             \u001b[0mitemmask\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1796\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '34847.84_'"
          ]
        }
      ],
      "source": [
        "correlation_matrix=df_new.corr()\n",
        "plt.figure(figsize=(12,6))\n",
        "sns.heatmap(correlation_matrix,annot=True,cmap=\"YlGnBu\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 790
        },
        "id": "H-Ed3nEnshMB",
        "outputId": "799a3b1d-a1d5-43f0-9ad2-66e947985c71"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-514c8e6f-9e96-4497-b816-40eaf7563314\" class=\"colab-df-container\">\n",
              "    <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>ID</th>\n",
              "      <th>Customer_ID</th>\n",
              "      <th>Month</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>SSN</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Annual_Income</th>\n",
              "      <th>Monthly_Inhand_Salary</th>\n",
              "      <th>Num_Bank_Accounts</th>\n",
              "      <th>...</th>\n",
              "      <th>Monthly_Balance</th>\n",
              "      <th>Credit_History_Age_in_years</th>\n",
              "      <th>Payment_history_score</th>\n",
              "      <th>Credit_Utilization_score</th>\n",
              "      <th>Credit_Mix_score</th>\n",
              "      <th>Credit_length_score</th>\n",
              "      <th>Debt_Income_Ratio</th>\n",
              "      <th>Debt_to_income_score</th>\n",
              "      <th>CENT_value</th>\n",
              "      <th>CENT_SCORE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0x160e</td>\n",
              "      <td>CUS_0x21b1</td>\n",
              "      <td>January</td>\n",
              "      <td>Rick Rothackerj</td>\n",
              "      <td>28</td>\n",
              "      <td>004-07-5839</td>\n",
              "      <td>_______</td>\n",
              "      <td>34847.84</td>\n",
              "      <td>3037.986667</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>470.690627</td>\n",
              "      <td>26</td>\n",
              "      <td>83</td>\n",
              "      <td>95</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>19.915492</td>\n",
              "      <td>100</td>\n",
              "      <td>89.05</td>\n",
              "      <td>756.925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0x1613</td>\n",
              "      <td>CUS_0x21b1</td>\n",
              "      <td>June</td>\n",
              "      <td>Rick Rothackerj</td>\n",
              "      <td>28</td>\n",
              "      <td>004-07-5839</td>\n",
              "      <td>Teacher</td>\n",
              "      <td>34847.84</td>\n",
              "      <td>3037.986667</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>481.505262</td>\n",
              "      <td>27</td>\n",
              "      <td>93</td>\n",
              "      <td>85</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>19.915492</td>\n",
              "      <td>100</td>\n",
              "      <td>89.55</td>\n",
              "      <td>761.175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>0x1642</td>\n",
              "      <td>CUS_0x95ee</td>\n",
              "      <td>May</td>\n",
              "      <td>Np</td>\n",
              "      <td>31</td>\n",
              "      <td>612-70-8987</td>\n",
              "      <td>Lawyer</td>\n",
              "      <td>73928.46</td>\n",
              "      <td>5988.705000</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>690.918664</td>\n",
              "      <td>32</td>\n",
              "      <td>85</td>\n",
              "      <td>95</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>9.153899</td>\n",
              "      <td>100</td>\n",
              "      <td>89.75</td>\n",
              "      <td>762.875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0x164a</td>\n",
              "      <td>CUS_0x284a</td>\n",
              "      <td>January</td>\n",
              "      <td>Nadiaq</td>\n",
              "      <td>33</td>\n",
              "      <td>411-51-0676</td>\n",
              "      <td>Lawyer</td>\n",
              "      <td>131313.40</td>\n",
              "      <td>11242.783330</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>858.462474</td>\n",
              "      <td>30</td>\n",
              "      <td>90</td>\n",
              "      <td>85</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>3.132320</td>\n",
              "      <td>100</td>\n",
              "      <td>88.50</td>\n",
              "      <td>752.250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>0x164c</td>\n",
              "      <td>CUS_0x284a</td>\n",
              "      <td>March</td>\n",
              "      <td>Nadiaq</td>\n",
              "      <td>34</td>\n",
              "      <td>411-51-0676</td>\n",
              "      <td>Lawyer</td>\n",
              "      <td>131313.40</td>\n",
              "      <td>10469.207760</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1038.569407</td>\n",
              "      <td>30</td>\n",
              "      <td>90</td>\n",
              "      <td>85</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>3.363769</td>\n",
              "      <td>100</td>\n",
              "      <td>88.50</td>\n",
              "      <td>752.250</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>99683</th>\n",
              "      <td>0x25e15</td>\n",
              "      <td>CUS_0x68ea</td>\n",
              "      <td>April</td>\n",
              "      <td>Forgiones</td>\n",
              "      <td>47</td>\n",
              "      <td>297-20-0070</td>\n",
              "      <td>Scientist</td>\n",
              "      <td>41646.88</td>\n",
              "      <td>3515.573333</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>429.511476</td>\n",
              "      <td>24</td>\n",
              "      <td>82</td>\n",
              "      <td>95</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>20.824768</td>\n",
              "      <td>100</td>\n",
              "      <td>88.70</td>\n",
              "      <td>753.950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99881</th>\n",
              "      <td>0x25f3f</td>\n",
              "      <td>CUS_0x47fa</td>\n",
              "      <td>February</td>\n",
              "      <td>Yantoultra Nguif</td>\n",
              "      <td>31</td>\n",
              "      <td>291-51-7240</td>\n",
              "      <td>Mechanic</td>\n",
              "      <td>64511.34</td>\n",
              "      <td>5440.945000</td>\n",
              "      <td>7</td>\n",
              "      <td>...</td>\n",
              "      <td>495.944541</td>\n",
              "      <td>22</td>\n",
              "      <td>87</td>\n",
              "      <td>95</td>\n",
              "      <td>60</td>\n",
              "      <td>90</td>\n",
              "      <td>6.076150</td>\n",
              "      <td>100</td>\n",
              "      <td>88.45</td>\n",
              "      <td>751.825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99949</th>\n",
              "      <td>0x25fa3</td>\n",
              "      <td>CUS_0x51b3</td>\n",
              "      <td>June</td>\n",
              "      <td>Ryana</td>\n",
              "      <td>33</td>\n",
              "      <td>837-85-9800</td>\n",
              "      <td>Media_Manager</td>\n",
              "      <td>59146.36</td>\n",
              "      <td>4908.863333</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>622.786250</td>\n",
              "      <td>20</td>\n",
              "      <td>82</td>\n",
              "      <td>95</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>8.515821</td>\n",
              "      <td>100</td>\n",
              "      <td>88.70</td>\n",
              "      <td>753.950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99960</th>\n",
              "      <td>0x25fb6</td>\n",
              "      <td>CUS_0x372c</td>\n",
              "      <td>January</td>\n",
              "      <td>Lucia Mutikanik</td>\n",
              "      <td>18</td>\n",
              "      <td>340-85-7301</td>\n",
              "      <td>Lawyer</td>\n",
              "      <td>42903.79</td>\n",
              "      <td>3468.315833</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>493.341182</td>\n",
              "      <td>28</td>\n",
              "      <td>90</td>\n",
              "      <td>95</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>31.124040</td>\n",
              "      <td>85</td>\n",
              "      <td>90.00</td>\n",
              "      <td>765.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99964</th>\n",
              "      <td>0x25fba</td>\n",
              "      <td>CUS_0x372c</td>\n",
              "      <td>May</td>\n",
              "      <td>Lucia Mutikanik</td>\n",
              "      <td>18</td>\n",
              "      <td>340-85-7301</td>\n",
              "      <td>Lawyer</td>\n",
              "      <td>42903.79</td>\n",
              "      <td>3468.315833</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>151.210033</td>\n",
              "      <td>28</td>\n",
              "      <td>90</td>\n",
              "      <td>95</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>31.124040</td>\n",
              "      <td>85</td>\n",
              "      <td>90.00</td>\n",
              "      <td>765.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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            ],
            "text/plain": [
              "            ID Customer_ID     Month              Name  Age          SSN  \\\n",
              "8       0x160e  CUS_0x21b1   January   Rick Rothackerj   28  004-07-5839   \n",
              "13      0x1613  CUS_0x21b1      June   Rick Rothackerj   28  004-07-5839   \n",
              "44      0x1642  CUS_0x95ee       May                Np   31  612-70-8987   \n",
              "48      0x164a  CUS_0x284a   January            Nadiaq   33  411-51-0676   \n",
              "50      0x164c  CUS_0x284a     March            Nadiaq   34  411-51-0676   \n",
              "...        ...         ...       ...               ...  ...          ...   \n",
              "99683  0x25e15  CUS_0x68ea     April         Forgiones   47  297-20-0070   \n",
              "99881  0x25f3f  CUS_0x47fa  February  Yantoultra Nguif   31  291-51-7240   \n",
              "99949  0x25fa3  CUS_0x51b3      June             Ryana   33  837-85-9800   \n",
              "99960  0x25fb6  CUS_0x372c   January   Lucia Mutikanik   18  340-85-7301   \n",
              "99964  0x25fba  CUS_0x372c       May   Lucia Mutikanik   18  340-85-7301   \n",
              "\n",
              "          Occupation  Annual_Income  Monthly_Inhand_Salary  Num_Bank_Accounts  \\\n",
              "8            _______       34847.84            3037.986667                  2   \n",
              "13           Teacher       34847.84            3037.986667                  2   \n",
              "44            Lawyer       73928.46            5988.705000                  4   \n",
              "48            Lawyer      131313.40           11242.783330                  0   \n",
              "50            Lawyer      131313.40           10469.207760                  0   \n",
              "...              ...            ...                    ...                ...   \n",
              "99683      Scientist       41646.88            3515.573333                  4   \n",
              "99881       Mechanic       64511.34            5440.945000                  7   \n",
              "99949  Media_Manager       59146.36            4908.863333                  3   \n",
              "99960         Lawyer       42903.79            3468.315833                  0   \n",
              "99964         Lawyer       42903.79            3468.315833                  0   \n",
              "\n",
              "       ...  Monthly_Balance  Credit_History_Age_in_years  \\\n",
              "8      ...       470.690627                           26   \n",
              "13     ...       481.505262                           27   \n",
              "44     ...       690.918664                           32   \n",
              "48     ...       858.462474                           30   \n",
              "50     ...      1038.569407                           30   \n",
              "...    ...              ...                          ...   \n",
              "99683  ...       429.511476                           24   \n",
              "99881  ...       495.944541                           22   \n",
              "99949  ...       622.786250                           20   \n",
              "99960  ...       493.341182                           28   \n",
              "99964  ...       151.210033                           28   \n",
              "\n",
              "       Payment_history_score Credit_Utilization_score  Credit_Mix_score  \\\n",
              "8                         83                       95                80   \n",
              "13                        93                       85                80   \n",
              "44                        85                       95                80   \n",
              "48                        90                       85                80   \n",
              "50                        90                       85                80   \n",
              "...                      ...                      ...               ...   \n",
              "99683                     82                       95                80   \n",
              "99881                     87                       95                60   \n",
              "99949                     82                       95                80   \n",
              "99960                     90                       95                80   \n",
              "99964                     90                       95                80   \n",
              "\n",
              "       Credit_length_score  Debt_Income_Ratio  Debt_to_income_score  \\\n",
              "8                       90          19.915492                   100   \n",
              "13                      90          19.915492                   100   \n",
              "44                      90           9.153899                   100   \n",
              "48                      90           3.132320                   100   \n",
              "50                      90           3.363769                   100   \n",
              "...                    ...                ...                   ...   \n",
              "99683                   90          20.824768                   100   \n",
              "99881                   90           6.076150                   100   \n",
              "99949                   90           8.515821                   100   \n",
              "99960                   90          31.124040                    85   \n",
              "99964                   90          31.124040                    85   \n",
              "\n",
              "      CENT_value  CENT_SCORE  \n",
              "8          89.05     756.925  \n",
              "13         89.55     761.175  \n",
              "44         89.75     762.875  \n",
              "48         88.50     752.250  \n",
              "50         88.50     752.250  \n",
              "...          ...         ...  \n",
              "99683      88.70     753.950  \n",
              "99881      88.45     751.825  \n",
              "99949      88.70     753.950  \n",
              "99960      90.00     765.000  \n",
              "99964      90.00     765.000  \n",
              "\n",
              "[4077 rows x 36 columns]"
            ]
          },
          "execution_count": 210,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[df['CENT_SCORE']>750]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 790
        },
        "id": "DtwQiALttuaN",
        "outputId": "74a11bbb-bdd4-4d92-d1f8-2fb91c61c474"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Customer_ID</th>\n",
              "      <th>Month</th>\n",
              "      <th>Name</th>\n",
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              "      <th>0</th>\n",
              "      <td>0x1602</td>\n",
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              "      <td>70</td>\n",
              "      <td>86.05</td>\n",
              "      <td>731.425</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0x1603</td>\n",
              "      <td>CUS_0xd40</td>\n",
              "      <td>February</td>\n",
              "      <td>Aaron Maashoh</td>\n",
              "      <td>23</td>\n",
              "      <td>821-00-0265</td>\n",
              "      <td>Scientist</td>\n",
              "      <td>19114.12</td>\n",
              "      <td>1824.843333</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>284.629163</td>\n",
              "      <td>22</td>\n",
              "      <td>80</td>\n",
              "      <td>85</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>44.386276</td>\n",
              "      <td>70</td>\n",
              "      <td>82.00</td>\n",
              "      <td>697.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0x1604</td>\n",
              "      <td>CUS_0xd40</td>\n",
              "      <td>March</td>\n",
              "      <td>Aaron Maashoh</td>\n",
              "      <td>23</td>\n",
              "      <td>821-00-0265</td>\n",
              "      <td>Scientist</td>\n",
              "      <td>19114.12</td>\n",
              "      <td>1824.843333</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>331.209863</td>\n",
              "      <td>22</td>\n",
              "      <td>83</td>\n",
              "      <td>95</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>44.386276</td>\n",
              "      <td>70</td>\n",
              "      <td>86.05</td>\n",
              "      <td>731.425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0x1605</td>\n",
              "      <td>CUS_0xd40</td>\n",
              "      <td>April</td>\n",
              "      <td>Aaron Maashoh</td>\n",
              "      <td>23</td>\n",
              "      <td>821-00-0265</td>\n",
              "      <td>Scientist</td>\n",
              "      <td>19114.12</td>\n",
              "      <td>1824.843333</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>223.451310</td>\n",
              "      <td>22</td>\n",
              "      <td>82</td>\n",
              "      <td>85</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>44.386276</td>\n",
              "      <td>70</td>\n",
              "      <td>82.70</td>\n",
              "      <td>702.950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0x1606</td>\n",
              "      <td>CUS_0xd40</td>\n",
              "      <td>May</td>\n",
              "      <td>Aaron Maashoh</td>\n",
              "      <td>23</td>\n",
              "      <td>821-00-0265</td>\n",
              "      <td>Scientist</td>\n",
              "      <td>19114.12</td>\n",
              "      <td>1824.843333</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>341.489231</td>\n",
              "      <td>22</td>\n",
              "      <td>82</td>\n",
              "      <td>95</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>44.386276</td>\n",
              "      <td>70</td>\n",
              "      <td>85.70</td>\n",
              "      <td>728.450</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>99995</th>\n",
              "      <td>0x25fe9</td>\n",
              "      <td>CUS_0x942c</td>\n",
              "      <td>April</td>\n",
              "      <td>Nicks</td>\n",
              "      <td>25</td>\n",
              "      <td>078-73-5990</td>\n",
              "      <td>Mechanic</td>\n",
              "      <td>39628.99</td>\n",
              "      <td>3359.415833</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>479.866228</td>\n",
              "      <td>31</td>\n",
              "      <td>80</td>\n",
              "      <td>85</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>14.954386</td>\n",
              "      <td>100</td>\n",
              "      <td>85.00</td>\n",
              "      <td>722.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99996</th>\n",
              "      <td>0x25fea</td>\n",
              "      <td>CUS_0x942c</td>\n",
              "      <td>May</td>\n",
              "      <td>Nicks</td>\n",
              "      <td>25</td>\n",
              "      <td>078-73-5990</td>\n",
              "      <td>Mechanic</td>\n",
              "      <td>39628.99</td>\n",
              "      <td>3359.415833</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>496.651610</td>\n",
              "      <td>31</td>\n",
              "      <td>80</td>\n",
              "      <td>85</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>14.954386</td>\n",
              "      <td>100</td>\n",
              "      <td>85.00</td>\n",
              "      <td>722.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99997</th>\n",
              "      <td>0x25feb</td>\n",
              "      <td>CUS_0x942c</td>\n",
              "      <td>June</td>\n",
              "      <td>Nicks</td>\n",
              "      <td>25</td>\n",
              "      <td>078-73-5990</td>\n",
              "      <td>Mechanic</td>\n",
              "      <td>39628.99</td>\n",
              "      <td>3359.415833</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>516.809083</td>\n",
              "      <td>31</td>\n",
              "      <td>80</td>\n",
              "      <td>85</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>14.954386</td>\n",
              "      <td>100</td>\n",
              "      <td>85.00</td>\n",
              "      <td>722.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99998</th>\n",
              "      <td>0x25fec</td>\n",
              "      <td>CUS_0x942c</td>\n",
              "      <td>July</td>\n",
              "      <td>Nicks</td>\n",
              "      <td>25</td>\n",
              "      <td>078-73-5990</td>\n",
              "      <td>Mechanic</td>\n",
              "      <td>39628.99</td>\n",
              "      <td>3359.415833</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>319.164979</td>\n",
              "      <td>31</td>\n",
              "      <td>80</td>\n",
              "      <td>85</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>14.954386</td>\n",
              "      <td>100</td>\n",
              "      <td>85.00</td>\n",
              "      <td>722.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99999</th>\n",
              "      <td>0x25fed</td>\n",
              "      <td>CUS_0x942c</td>\n",
              "      <td>August</td>\n",
              "      <td>Nicks</td>\n",
              "      <td>25</td>\n",
              "      <td>078-73-5990</td>\n",
              "      <td>Mechanic</td>\n",
              "      <td>39628.99</td>\n",
              "      <td>3359.415833</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>393.673696</td>\n",
              "      <td>31</td>\n",
              "      <td>80</td>\n",
              "      <td>85</td>\n",
              "      <td>80</td>\n",
              "      <td>90</td>\n",
              "      <td>14.954386</td>\n",
              "      <td>100</td>\n",
              "      <td>85.00</td>\n",
              "      <td>722.500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>54493 rows × 36 columns</p>\n",
              "</div>\n",
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              "\n",
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            ],
            "text/plain": [
              "            ID Customer_ID     Month           Name  Age          SSN  \\\n",
              "0       0x1602   CUS_0xd40   January  Aaron Maashoh   23  821-00-0265   \n",
              "1       0x1603   CUS_0xd40  February  Aaron Maashoh   23  821-00-0265   \n",
              "2       0x1604   CUS_0xd40     March  Aaron Maashoh   23  821-00-0265   \n",
              "3       0x1605   CUS_0xd40     April  Aaron Maashoh   23  821-00-0265   \n",
              "4       0x1606   CUS_0xd40       May  Aaron Maashoh   23  821-00-0265   \n",
              "...        ...         ...       ...            ...  ...          ...   \n",
              "99995  0x25fe9  CUS_0x942c     April          Nicks   25  078-73-5990   \n",
              "99996  0x25fea  CUS_0x942c       May          Nicks   25  078-73-5990   \n",
              "99997  0x25feb  CUS_0x942c      June          Nicks   25  078-73-5990   \n",
              "99998  0x25fec  CUS_0x942c      July          Nicks   25  078-73-5990   \n",
              "99999  0x25fed  CUS_0x942c    August          Nicks   25  078-73-5990   \n",
              "\n",
              "      Occupation  Annual_Income  Monthly_Inhand_Salary  Num_Bank_Accounts  \\\n",
              "0      Scientist       19114.12            1824.843333                  3   \n",
              "1      Scientist       19114.12            1824.843333                  3   \n",
              "2      Scientist       19114.12            1824.843333                  3   \n",
              "3      Scientist       19114.12            1824.843333                  3   \n",
              "4      Scientist       19114.12            1824.843333                  3   \n",
              "...          ...            ...                    ...                ...   \n",
              "99995   Mechanic       39628.99            3359.415833                  4   \n",
              "99996   Mechanic       39628.99            3359.415833                  4   \n",
              "99997   Mechanic       39628.99            3359.415833                  4   \n",
              "99998   Mechanic       39628.99            3359.415833                  4   \n",
              "99999   Mechanic       39628.99            3359.415833                  4   \n",
              "\n",
              "       ...  Monthly_Balance  Credit_History_Age_in_years  \\\n",
              "0      ...       312.494089                           22   \n",
              "1      ...       284.629163                           22   \n",
              "2      ...       331.209863                           22   \n",
              "3      ...       223.451310                           22   \n",
              "4      ...       341.489231                           22   \n",
              "...    ...              ...                          ...   \n",
              "99995  ...       479.866228                           31   \n",
              "99996  ...       496.651610                           31   \n",
              "99997  ...       516.809083                           31   \n",
              "99998  ...       319.164979                           31   \n",
              "99999  ...       393.673696                           31   \n",
              "\n",
              "       Payment_history_score Credit_Utilization_score  Credit_Mix_score  \\\n",
              "0                         83                       95                80   \n",
              "1                         80                       85                80   \n",
              "2                         83                       95                80   \n",
              "3                         82                       85                80   \n",
              "4                         82                       95                80   \n",
              "...                      ...                      ...               ...   \n",
              "99995                     80                       85                80   \n",
              "99996                     80                       85                80   \n",
              "99997                     80                       85                80   \n",
              "99998                     80                       85                80   \n",
              "99999                     80                       85                80   \n",
              "\n",
              "       Credit_length_score  Debt_Income_Ratio  Debt_to_income_score  \\\n",
              "0                       90          44.386276                    70   \n",
              "1                       90          44.386276                    70   \n",
              "2                       90          44.386276                    70   \n",
              "3                       90          44.386276                    70   \n",
              "4                       90          44.386276                    70   \n",
              "...                    ...                ...                   ...   \n",
              "99995                   90          14.954386                   100   \n",
              "99996                   90          14.954386                   100   \n",
              "99997                   90          14.954386                   100   \n",
              "99998                   90          14.954386                   100   \n",
              "99999                   90          14.954386                   100   \n",
              "\n",
              "      CENT_value  CENT_SCORE  \n",
              "0          86.05     731.425  \n",
              "1          82.00     697.000  \n",
              "2          86.05     731.425  \n",
              "3          82.70     702.950  \n",
              "4          85.70     728.450  \n",
              "...          ...         ...  \n",
              "99995      85.00     722.500  \n",
              "99996      85.00     722.500  \n",
              "99997      85.00     722.500  \n",
              "99998      85.00     722.500  \n",
              "99999      85.00     722.500  \n",
              "\n",
              "[54493 rows x 36 columns]"
            ]
          },
          "execution_count": 213,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[(df['CENT_SCORE']>650) & (df['CENT_SCORE']<750)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "6xbN5ktfuJFz",
        "outputId": "efc7516e-da73-4650-928d-8f5ff4287293"
      },
      "outputs": [
        {
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              "      <td>Sarah McBridec</td>\n",
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              "      <td>Architect</td>\n",
              "      <td>20002.88</td>\n",
              "      <td>1929.906667</td>\n",
              "      <td>10</td>\n",
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              "      <td>6</td>\n",
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              "      <td>30</td>\n",
              "      <td>185.071126</td>\n",
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              "      <td>571.625</td>\n",
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              "    <tr>\n",
              "      <th>99989</th>\n",
              "      <td>0x25fdf</td>\n",
              "      <td>CUS_0x8600</td>\n",
              "      <td>June</td>\n",
              "      <td>Sarah McBridec</td>\n",
              "      <td>28</td>\n",
              "      <td>031-35-0942</td>\n",
              "      <td>_______</td>\n",
              "      <td>20002.88</td>\n",
              "      <td>1929.906667</td>\n",
              "      <td>10</td>\n",
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              "      <td>307.659291</td>\n",
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              "      <td>75</td>\n",
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              "      <td>30</td>\n",
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              "      <td>64.25</td>\n",
              "      <td>546.125</td>\n",
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              "    <tr>\n",
              "      <th>99990</th>\n",
              "      <td>0x25fe0</td>\n",
              "      <td>CUS_0x8600</td>\n",
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              "      <td>Sarah McBridec</td>\n",
              "      <td>28</td>\n",
              "      <td>031-35-0942</td>\n",
              "      <td>Architect</td>\n",
              "      <td>20002.88</td>\n",
              "      <td>1929.906667</td>\n",
              "      <td>10</td>\n",
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              "      <td>228.750392</td>\n",
              "      <td>6</td>\n",
              "      <td>75</td>\n",
              "      <td>95</td>\n",
              "      <td>30</td>\n",
              "      <td>30</td>\n",
              "      <td>185.071126</td>\n",
              "      <td>50</td>\n",
              "      <td>67.25</td>\n",
              "      <td>571.625</td>\n",
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              "    <tr>\n",
              "      <th>99991</th>\n",
              "      <td>0x25fe1</td>\n",
              "      <td>CUS_0x8600</td>\n",
              "      <td>August</td>\n",
              "      <td>Sarah McBridec</td>\n",
              "      <td>29</td>\n",
              "      <td>031-35-0942</td>\n",
              "      <td>Architect</td>\n",
              "      <td>20002.88</td>\n",
              "      <td>1929.906667</td>\n",
              "      <td>10</td>\n",
              "      <td>...</td>\n",
              "      <td>337.362988</td>\n",
              "      <td>6</td>\n",
              "      <td>75</td>\n",
              "      <td>85</td>\n",
              "      <td>30</td>\n",
              "      <td>30</td>\n",
              "      <td>185.071126</td>\n",
              "      <td>50</td>\n",
              "      <td>64.25</td>\n",
              "      <td>546.125</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>41430 rows × 36 columns</p>\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ed2eb29f-15bc-4dc2-9394-1d17dc5f3a8f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "            ID Customer_ID     Month            Name  Age          SSN  \\\n",
              "56      0x1656  CUS_0x5407   January            Annk   30  500-92-6408   \n",
              "57      0x1657  CUS_0x5407  February            Annk   30  500-92-6408   \n",
              "58      0x1658  CUS_0x5407     March            Annk   30  500-92-6408   \n",
              "59      0x1659  CUS_0x5407     April            Annk   30  500-92-6408   \n",
              "60      0x165a  CUS_0x5407       May            Annk   30  500-92-6408   \n",
              "...        ...         ...       ...             ...  ...          ...   \n",
              "99987  0x25fdd  CUS_0x8600     April  Sarah McBridec   28  031-35-0942   \n",
              "99988  0x25fde  CUS_0x8600       May  Sarah McBridec   28    #F%$D@*&8   \n",
              "99989  0x25fdf  CUS_0x8600      June  Sarah McBridec   28  031-35-0942   \n",
              "99990  0x25fe0  CUS_0x8600      July  Sarah McBridec   28  031-35-0942   \n",
              "99991  0x25fe1  CUS_0x8600    August  Sarah McBridec   29  031-35-0942   \n",
              "\n",
              "          Occupation  Annual_Income  Monthly_Inhand_Salary  Num_Bank_Accounts  \\\n",
              "56     Media_Manager       34081.38            2611.115000                  8   \n",
              "57     Media_Manager       34081.38            2611.115000                  8   \n",
              "58     Media_Manager       34081.38            2611.115000                  8   \n",
              "59           _______       34081.38            2611.115000                  8   \n",
              "60           _______       34081.38            2611.115000                  8   \n",
              "...              ...            ...                    ...                ...   \n",
              "99987      Architect       20002.88            1929.906667                 10   \n",
              "99988      Architect       20002.88            1929.906667                 10   \n",
              "99989        _______       20002.88            1929.906667                 10   \n",
              "99990      Architect       20002.88            1929.906667                 10   \n",
              "99991      Architect       20002.88            1929.906667                 10   \n",
              "\n",
              "       ...  Monthly_Balance  Credit_History_Age_in_years  \\\n",
              "56     ...       298.192158                           15   \n",
              "57     ...       392.196185                           14   \n",
              "58     ...       260.912402                           14   \n",
              "59     ...       260.080975                           14   \n",
              "60     ...       397.228355                           14   \n",
              "...    ...              ...                          ...   \n",
              "99987  ...       328.301277                            5   \n",
              "99988  ...       208.047885                            6   \n",
              "99989  ...       307.659291                            6   \n",
              "99990  ...       228.750392                            6   \n",
              "99991  ...       337.362988                            6   \n",
              "\n",
              "       Payment_history_score Credit_Utilization_score  Credit_Mix_score  \\\n",
              "56                        75                       95                60   \n",
              "57                        75                       85                60   \n",
              "58                        75                       85                60   \n",
              "59                        75                       95                60   \n",
              "60                        75                       85                60   \n",
              "...                      ...                      ...               ...   \n",
              "99987                     75                       85                30   \n",
              "99988                     75                       95                30   \n",
              "99989                     75                       85                30   \n",
              "99990                     75                       95                30   \n",
              "99991                     75                       85                30   \n",
              "\n",
              "       Credit_length_score  Debt_Income_Ratio  Debt_to_income_score  \\\n",
              "56                      60          65.266371                    50   \n",
              "57                      60          65.266371                    50   \n",
              "58                      60          65.266371                    50   \n",
              "59                      60          65.266371                    50   \n",
              "60                      60          65.266371                    50   \n",
              "...                    ...                ...                   ...   \n",
              "99987                   30         185.071126                    50   \n",
              "99988                   30         185.071126                    50   \n",
              "99989                   30         185.071126                    50   \n",
              "99990                   30         185.071126                    50   \n",
              "99991                   30         185.071126                    50   \n",
              "\n",
              "      CENT_value  CENT_SCORE  \n",
              "56         74.75     635.375  \n",
              "57         71.75     609.875  \n",
              "58         71.75     609.875  \n",
              "59         74.75     635.375  \n",
              "60         71.75     609.875  \n",
              "...          ...         ...  \n",
              "99987      64.25     546.125  \n",
              "99988      67.25     571.625  \n",
              "99989      64.25     546.125  \n",
              "99990      67.25     571.625  \n",
              "99991      64.25     546.125  \n",
              "\n",
              "[41430 rows x 36 columns]"
            ]
          },
          "execution_count": 214,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[df['CENT_SCORE']<650]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ncv004Qws3aR"
      },
      "source": [
        "**Insights:**\n",
        "\n",
        "1. Nearly 7321 people have good credit score above 750\n",
        "2. 6,832 people have avg credit score\n",
        "3.5178 people have a bad credit score\n",
        "4. Annual income and Debt to income score are positvely corelated\n",
        "5. Cent score and Annual income have moderate positive co relation which indicates there are high chances if a persons income us high he will be having high credit score\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iBltme6GwHew"
      },
      "outputs": [],
      "source": [
        "!"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}