GiveMeSomeCredit_Loan_Default_Challenge (1).ipynb

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  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "964lqRpT2fOz"
   },
   "source": [
    "**This Work was done Following CRISP-DM Methosology**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "HxAchQVa2u-p"
   },
   "source": [
    "**1.Business Understanding**\n",
    "- forecasting the probabilty of granting a loan to someone.\n",
    "- predicting the probability that somebody will experience financial distress in the next two years.\n",
    "-Data for the past 2 years\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "3wfLP98A5h5Z"
   },
   "source": [
    "**2.Data Understanding**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "e0ywiikcILsv"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "azFiGrjWPMD8",
    "outputId": "d7a21a9f-1081-4046-8a18-bb3cad6d96f4"
   },
   "outputs": [],
   "source": [
    "#from google.colab import drive\n",
    "#drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "95y_lGD_PW2L"
   },
   "outputs": [],
   "source": [
    "Train_data=pd.read_csv('cs-training.csv')\n",
    "Test_data=pd.read_csv('cs-test.csv')"
   ]
  },
  {
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   "execution_count": 7,
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    "outputId": "2bc5ecb7-653f-4e84-f4d8-e332c3323947"
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       "101498      101499               NaN                              0.282653   \n",
       "101499      101500               NaN                              0.922156   \n",
       "101500      101501               NaN                              0.081596   \n",
       "101501      101502               NaN                              0.335457   \n",
       "101502      101503               NaN                              0.441842   \n",
       "\n",
       "        age  NumberOfTime30-59DaysPastDueNotWorse    DebtRatio  MonthlyIncome  \\\n",
       "0        43                                     0     0.177513         5700.0   \n",
       "1        57                                     0     0.527237         9141.0   \n",
       "2        59                                     0     0.687648         5083.0   \n",
       "3        38                                     1     0.925961         3200.0   \n",
       "4        27                                     0     0.019917         3865.0   \n",
       "...     ...                                   ...          ...            ...   \n",
       "101498   24                                     0     0.068522         1400.0   \n",
       "101499   36                                     3     0.934217         7615.0   \n",
       "101500   70                                     0   836.000000            NaN   \n",
       "101501   56                                     0  3568.000000            NaN   \n",
       "101502   29                                     0     0.198918         5916.0   \n",
       "\n",
       "        NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                                     4                        0   \n",
       "1                                    15                        0   \n",
       "2                                    12                        0   \n",
       "3                                     7                        0   \n",
       "4                                     4                        0   \n",
       "...                                 ...                      ...   \n",
       "101498                                5                        0   \n",
       "101499                                8                        0   \n",
       "101500                                3                        0   \n",
       "101501                                8                        0   \n",
       "101502                               12                        0   \n",
       "\n",
       "        NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "0                                  0                                     0   \n",
       "1                                  4                                     0   \n",
       "2                                  1                                     0   \n",
       "3                                  2                                     0   \n",
       "4                                  0                                     0   \n",
       "...                              ...                                   ...   \n",
       "101498                             0                                     0   \n",
       "101499                             2                                     0   \n",
       "101500                             0                                     0   \n",
       "101501                             2                                     1   \n",
       "101502                             0                                     0   \n",
       "\n",
       "        NumberOfDependents  \n",
       "0                      0.0  \n",
       "1                      2.0  \n",
       "2                      2.0  \n",
       "3                      0.0  \n",
       "4                      1.0  \n",
       "...                    ...  \n",
       "101498                 0.0  \n",
       "101499                 4.0  \n",
       "101500                 NaN  \n",
       "101501                 3.0  \n",
       "101502                 0.0  \n",
       "\n",
       "[101503 rows x 12 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LJMQfY-06dje",
    "outputId": "e726570c-6296-4d68-9391-e53883d3f891"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of training data (150000, 12)\n",
      "shape of test data (101503, 12)\n"
     ]
    }
   ],
   "source": [
    "print('shape of training data {}'.format(Train_data.shape))\n",
    "print('shape of test data {}'.format(Test_data.shape))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f672ed9P60gU",
    "outputId": "86722c30-2f92-447f-e936-5469a4a51bfc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique value in  Unnamed: 0\n",
      "150000 [     1      2      3 ... 149998 149999 150000]\n",
      "Unique value in  SeriousDlqin2yrs\n",
      "2 [1 0]\n",
      "Unique value in  RevolvingUtilizationOfUnsecuredLines\n",
      "125728 [0.76612661 0.95715102 0.65818014 ... 0.29974515 0.24604392 0.85028295]\n",
      "Unique value in  age\n",
      "86 [ 45  40  38  30  49  74  57  39  27  51  46  76  64  78  53  43  25  32\n",
      "  58  50  69  24  28  62  42  75  26  52  41  81  31  68  70  73  29  55\n",
      "  35  72  60  67  36  56  37  66  83  34  44  48  61  80  47  59  77  63\n",
      "  54  33  79  65  86  92  23  87  71  22  90  97  84  82  91  89  85  88\n",
      "  21  93  96  99  94  95 101  98 103 102 107 105   0 109]\n",
      "Unique value in  NumberOfTime30-59DaysPastDueNotWorse\n",
      "16 [ 2  0  1  3  4  5  7 10  6 98 12  8  9 96 13 11]\n",
      "Unique value in  DebtRatio\n",
      "114194 [0.80298213 0.1218762  0.08511338 ... 0.40429286 0.71656222 0.24990808]\n",
      "Unique value in  MonthlyIncome\n",
      "13595 [ 9120.  2600.  3042. ... 15648.   521.  7647.]\n",
      "Unique value in  NumberOfOpenCreditLinesAndLoans\n",
      "58 [13  4  2  5  7  3  8  9  6 10 14 22 17  1 15 31 12 21 11 19 18 26 20 23\n",
      "  0 16 27 29 28 24 25 32 46 33 30 34 37 38 36 35 54 43 45 49 57 48 40 58\n",
      " 51 52 39 41 56 42 47 50 44 53]\n",
      "Unique value in  NumberOfTimes90DaysLate\n",
      "19 [ 0  1  3  2  5  4 98 10  9  6  7  8 15 96 11 13 14 17 12]\n",
      "Unique value in  NumberRealEstateLoansOrLines\n",
      "28 [ 6  0  1  3  4  2  5  8  7  9 17 15 10 12 11 16 26 14 54 21 18 13 23 29\n",
      " 25 19 20 32]\n",
      "Unique value in  NumberOfTime60-89DaysPastDueNotWorse\n",
      "13 [ 0  1  2  5  3 98  4  6  7  8 96 11  9]\n",
      "Unique value in  NumberOfDependents\n",
      "14 [ 2.  1.  0. nan  3.  4.  5.  6.  8.  7. 20. 10.  9. 13.]\n"
     ]
    }
   ],
   "source": [
    "for column in Train_data.columns:\n",
    "  print('Unique value in  {}'.format(column))\n",
    "  print(len(Train_data[column].unique()),Train_data[column].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NK4ZiCh6QFRy",
    "outputId": "42420324-567f-436a-8f68-a49fcace06e2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150000 entries, 0 to 149999\n",
      "Data columns (total 12 columns):\n",
      " #   Column                                Non-Null Count   Dtype  \n",
      "---  ------                                --------------   -----  \n",
      " 0   Unnamed: 0                            150000 non-null  int64  \n",
      " 1   SeriousDlqin2yrs                      150000 non-null  int64  \n",
      " 2   RevolvingUtilizationOfUnsecuredLines  150000 non-null  float64\n",
      " 3   age                                   150000 non-null  int64  \n",
      " 4   NumberOfTime30-59DaysPastDueNotWorse  150000 non-null  int64  \n",
      " 5   DebtRatio                             150000 non-null  float64\n",
      " 6   MonthlyIncome                         120269 non-null  float64\n",
      " 7   NumberOfOpenCreditLinesAndLoans       150000 non-null  int64  \n",
      " 8   NumberOfTimes90DaysLate               150000 non-null  int64  \n",
      " 9   NumberRealEstateLoansOrLines          150000 non-null  int64  \n",
      " 10  NumberOfTime60-89DaysPastDueNotWorse  150000 non-null  int64  \n",
      " 11  NumberOfDependents                    146076 non-null  float64\n",
      "dtypes: float64(4), int64(8)\n",
      "memory usage: 13.7 MB\n"
     ]
    }
   ],
   "source": [
    "Train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 382
    },
    "id": "vv0WZyvmS2Lf",
    "outputId": "30980cec-beca-4d76-e53c-d079ea3d686f"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>1.202690e+05</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>146076.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75000.500000</td>\n",
       "      <td>0.066840</td>\n",
       "      <td>6.048438</td>\n",
       "      <td>52.295207</td>\n",
       "      <td>0.421033</td>\n",
       "      <td>353.005076</td>\n",
       "      <td>6.670221e+03</td>\n",
       "      <td>8.452760</td>\n",
       "      <td>0.265973</td>\n",
       "      <td>1.018240</td>\n",
       "      <td>0.240387</td>\n",
       "      <td>0.757222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43301.414527</td>\n",
       "      <td>0.249746</td>\n",
       "      <td>249.755371</td>\n",
       "      <td>14.771866</td>\n",
       "      <td>4.192781</td>\n",
       "      <td>2037.818523</td>\n",
       "      <td>1.438467e+04</td>\n",
       "      <td>5.145951</td>\n",
       "      <td>4.169304</td>\n",
       "      <td>1.129771</td>\n",
       "      <td>4.155179</td>\n",
       "      <td>1.115086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>37500.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029867</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.175074</td>\n",
       "      <td>3.400000e+03</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75000.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.154181</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.366508</td>\n",
       "      <td>5.400000e+03</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112500.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.559046</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.868254</td>\n",
       "      <td>8.249000e+03</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>150000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50708.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>329664.000000</td>\n",
       "      <td>3.008750e+06</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Unnamed: 0  SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  \\\n",
       "count  150000.000000     150000.000000                         150000.000000   \n",
       "mean    75000.500000          0.066840                              6.048438   \n",
       "std     43301.414527          0.249746                            249.755371   \n",
       "min         1.000000          0.000000                              0.000000   \n",
       "25%     37500.750000          0.000000                              0.029867   \n",
       "50%     75000.500000          0.000000                              0.154181   \n",
       "75%    112500.250000          0.000000                              0.559046   \n",
       "max    150000.000000          1.000000                          50708.000000   \n",
       "\n",
       "                 age  NumberOfTime30-59DaysPastDueNotWorse      DebtRatio  \\\n",
       "count  150000.000000                         150000.000000  150000.000000   \n",
       "mean       52.295207                              0.421033     353.005076   \n",
       "std        14.771866                              4.192781    2037.818523   \n",
       "min         0.000000                              0.000000       0.000000   \n",
       "25%        41.000000                              0.000000       0.175074   \n",
       "50%        52.000000                              0.000000       0.366508   \n",
       "75%        63.000000                              0.000000       0.868254   \n",
       "max       109.000000                             98.000000  329664.000000   \n",
       "\n",
       "       MonthlyIncome  NumberOfOpenCreditLinesAndLoans  \\\n",
       "count   1.202690e+05                    150000.000000   \n",
       "mean    6.670221e+03                         8.452760   \n",
       "std     1.438467e+04                         5.145951   \n",
       "min     0.000000e+00                         0.000000   \n",
       "25%     3.400000e+03                         5.000000   \n",
       "50%     5.400000e+03                         8.000000   \n",
       "75%     8.249000e+03                        11.000000   \n",
       "max     3.008750e+06                        58.000000   \n",
       "\n",
       "       NumberOfTimes90DaysLate  NumberRealEstateLoansOrLines  \\\n",
       "count            150000.000000                 150000.000000   \n",
       "mean                  0.265973                      1.018240   \n",
       "std                   4.169304                      1.129771   \n",
       "min                   0.000000                      0.000000   \n",
       "25%                   0.000000                      0.000000   \n",
       "50%                   0.000000                      1.000000   \n",
       "75%                   0.000000                      2.000000   \n",
       "max                  98.000000                     54.000000   \n",
       "\n",
       "       NumberOfTime60-89DaysPastDueNotWorse  NumberOfDependents  \n",
       "count                         150000.000000       146076.000000  \n",
       "mean                               0.240387            0.757222  \n",
       "std                                4.155179            1.115086  \n",
       "min                                0.000000            0.000000  \n",
       "25%                                0.000000            0.000000  \n",
       "50%                                0.000000            0.000000  \n",
       "75%                                0.000000            1.000000  \n",
       "max                               98.000000           20.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-IThWIHDUPBS",
    "outputId": "fe13f5d6-031f-4844-e448-7c69bdf36b45"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29731"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_data['MonthlyIncome'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 382
    },
    "id": "aRMWZTpjVYFx",
    "outputId": "4989d845-3068-41c9-af63-58c63dcbf81b"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>101503.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>101503.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>50752.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.310000</td>\n",
       "      <td>52.405436</td>\n",
       "      <td>0.453770</td>\n",
       "      <td>344.475020</td>\n",
       "      <td>6.855036e+03</td>\n",
       "      <td>8.453514</td>\n",
       "      <td>0.296691</td>\n",
       "      <td>1.013074</td>\n",
       "      <td>0.270317</td>\n",
       "      <td>0.769046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29301.536524</td>\n",
       "      <td>NaN</td>\n",
       "      <td>196.156039</td>\n",
       "      <td>14.779756</td>\n",
       "      <td>4.538487</td>\n",
       "      <td>1632.595231</td>\n",
       "      <td>3.650860e+04</td>\n",
       "      <td>5.144100</td>\n",
       "      <td>4.515859</td>\n",
       "      <td>1.110253</td>\n",
       "      <td>4.503578</td>\n",
       "      <td>1.136778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25376.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.030131</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.173423</td>\n",
       "      <td>3.408000e+03</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50752.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.152586</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.364260</td>\n",
       "      <td>5.400000e+03</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>76127.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.564225</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.851619</td>\n",
       "      <td>8.200000e+03</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>101503.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21821.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>268326.000000</td>\n",
       "      <td>7.727000e+06</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Unnamed: 0  SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  \\\n",
       "count  101503.000000               0.0                         101503.000000   \n",
       "mean    50752.000000               NaN                              5.310000   \n",
       "std     29301.536524               NaN                            196.156039   \n",
       "min         1.000000               NaN                              0.000000   \n",
       "25%     25376.500000               NaN                              0.030131   \n",
       "50%     50752.000000               NaN                              0.152586   \n",
       "75%     76127.500000               NaN                              0.564225   \n",
       "max    101503.000000               NaN                          21821.000000   \n",
       "\n",
       "                 age  NumberOfTime30-59DaysPastDueNotWorse      DebtRatio  \\\n",
       "count  101503.000000                         101503.000000  101503.000000   \n",
       "mean       52.405436                              0.453770     344.475020   \n",
       "std        14.779756                              4.538487    1632.595231   \n",
       "min        21.000000                              0.000000       0.000000   \n",
       "25%        41.000000                              0.000000       0.173423   \n",
       "50%        52.000000                              0.000000       0.364260   \n",
       "75%        63.000000                              0.000000       0.851619   \n",
       "max       104.000000                             98.000000  268326.000000   \n",
       "\n",
       "       MonthlyIncome  NumberOfOpenCreditLinesAndLoans  \\\n",
       "count   8.140000e+04                    101503.000000   \n",
       "mean    6.855036e+03                         8.453514   \n",
       "std     3.650860e+04                         5.144100   \n",
       "min     0.000000e+00                         0.000000   \n",
       "25%     3.408000e+03                         5.000000   \n",
       "50%     5.400000e+03                         8.000000   \n",
       "75%     8.200000e+03                        11.000000   \n",
       "max     7.727000e+06                        85.000000   \n",
       "\n",
       "       NumberOfTimes90DaysLate  NumberRealEstateLoansOrLines  \\\n",
       "count            101503.000000                 101503.000000   \n",
       "mean                  0.296691                      1.013074   \n",
       "std                   4.515859                      1.110253   \n",
       "min                   0.000000                      0.000000   \n",
       "25%                   0.000000                      0.000000   \n",
       "50%                   0.000000                      1.000000   \n",
       "75%                   0.000000                      2.000000   \n",
       "max                  98.000000                     37.000000   \n",
       "\n",
       "       NumberOfTime60-89DaysPastDueNotWorse  NumberOfDependents  \n",
       "count                         101503.000000        98877.000000  \n",
       "mean                               0.270317            0.769046  \n",
       "std                                4.503578            1.136778  \n",
       "min                                0.000000            0.000000  \n",
       "25%                                0.000000            0.000000  \n",
       "50%                                0.000000            0.000000  \n",
       "75%                                0.000000            1.000000  \n",
       "max                               98.000000           43.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 382
    },
    "id": "Bp-UxhNXPlK3",
    "outputId": "dd1ae2b7-d960-4305-86ea-b8a80eaecd00"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>1.202690e+05</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>146076.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75000.500000</td>\n",
       "      <td>0.066840</td>\n",
       "      <td>6.048438</td>\n",
       "      <td>52.295207</td>\n",
       "      <td>0.421033</td>\n",
       "      <td>353.005076</td>\n",
       "      <td>6.670221e+03</td>\n",
       "      <td>8.452760</td>\n",
       "      <td>0.265973</td>\n",
       "      <td>1.018240</td>\n",
       "      <td>0.240387</td>\n",
       "      <td>0.757222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43301.414527</td>\n",
       "      <td>0.249746</td>\n",
       "      <td>249.755371</td>\n",
       "      <td>14.771866</td>\n",
       "      <td>4.192781</td>\n",
       "      <td>2037.818523</td>\n",
       "      <td>1.438467e+04</td>\n",
       "      <td>5.145951</td>\n",
       "      <td>4.169304</td>\n",
       "      <td>1.129771</td>\n",
       "      <td>4.155179</td>\n",
       "      <td>1.115086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>37500.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029867</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.175074</td>\n",
       "      <td>3.400000e+03</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75000.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.154181</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.366508</td>\n",
       "      <td>5.400000e+03</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112500.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.559046</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.868254</td>\n",
       "      <td>8.249000e+03</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>150000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50708.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>329664.000000</td>\n",
       "      <td>3.008750e+06</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Unnamed: 0  SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  \\\n",
       "count  150000.000000     150000.000000                         150000.000000   \n",
       "mean    75000.500000          0.066840                              6.048438   \n",
       "std     43301.414527          0.249746                            249.755371   \n",
       "min         1.000000          0.000000                              0.000000   \n",
       "25%     37500.750000          0.000000                              0.029867   \n",
       "50%     75000.500000          0.000000                              0.154181   \n",
       "75%    112500.250000          0.000000                              0.559046   \n",
       "max    150000.000000          1.000000                          50708.000000   \n",
       "\n",
       "                 age  NumberOfTime30-59DaysPastDueNotWorse      DebtRatio  \\\n",
       "count  150000.000000                         150000.000000  150000.000000   \n",
       "mean       52.295207                              0.421033     353.005076   \n",
       "std        14.771866                              4.192781    2037.818523   \n",
       "min         0.000000                              0.000000       0.000000   \n",
       "25%        41.000000                              0.000000       0.175074   \n",
       "50%        52.000000                              0.000000       0.366508   \n",
       "75%        63.000000                              0.000000       0.868254   \n",
       "max       109.000000                             98.000000  329664.000000   \n",
       "\n",
       "       MonthlyIncome  NumberOfOpenCreditLinesAndLoans  \\\n",
       "count   1.202690e+05                    150000.000000   \n",
       "mean    6.670221e+03                         8.452760   \n",
       "std     1.438467e+04                         5.145951   \n",
       "min     0.000000e+00                         0.000000   \n",
       "25%     3.400000e+03                         5.000000   \n",
       "50%     5.400000e+03                         8.000000   \n",
       "75%     8.249000e+03                        11.000000   \n",
       "max     3.008750e+06                        58.000000   \n",
       "\n",
       "       NumberOfTimes90DaysLate  NumberRealEstateLoansOrLines  \\\n",
       "count            150000.000000                 150000.000000   \n",
       "mean                  0.265973                      1.018240   \n",
       "std                   4.169304                      1.129771   \n",
       "min                   0.000000                      0.000000   \n",
       "25%                   0.000000                      0.000000   \n",
       "50%                   0.000000                      1.000000   \n",
       "75%                   0.000000                      2.000000   \n",
       "max                  98.000000                     54.000000   \n",
       "\n",
       "       NumberOfTime60-89DaysPastDueNotWorse  NumberOfDependents  \n",
       "count                         150000.000000       146076.000000  \n",
       "mean                               0.240387            0.757222  \n",
       "std                                4.155179            1.115086  \n",
       "min                                0.000000            0.000000  \n",
       "25%                                0.000000            0.000000  \n",
       "50%                                0.000000            0.000000  \n",
       "75%                                0.000000            1.000000  \n",
       "max                               98.000000           20.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "eBRzjTKrI6JC"
   },
   "outputs": [],
   "source": [
    "#plt.figure(figsize=(12,10))\n",
    "#sns.violinplot(x='DebtRatio',y='SeriousDlqin2yrs',data=Train_data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "fSUtV_-DorCe",
    "outputId": "56b4dbac-b1e7-4eea-9a61-a59eba6eec0c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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T3759a5xrzpw5atu2rUJDQ5WYmKjVq1dX2//w4cOaNGmSWrRoIbvdro4dO2rRokXePA0AAGBBXt3nJj09XQMHDqy2j8Ph0LJly6rt8+677yolJUUvvPCCEhMTNWvWLA0dOlTbtm2r9As4i4qKdPHFFysmJkbvv/++WrVqpV9++UWRkZHePA0AAGBBXn0s5SuJiYnq27evZs+eLUkqKytTfHy8br/9dk2dOrVC/xdeeEEzZ87U1q1bFRwcXKt9FBYWqrCw0PWz0+lUfHw8H0sBANCAnNaPpRYtWqSbbrpJ9913n7Zs2eK27dChQ7roootqNU9RUZHWrFmj5OTkP4oJCFBycrIyMjIqHfPxxx8rKSlJkyZNUmxsrLp06aLHH39cpaWlVe4nLS1NERERrkd8fHyt6gMAAA2TR+Hm7bff1siRI5Wdna2MjAz16tVLb731lmt7UVGRvvjii1rNtX//fpWWlio2NtatPTY2VtnZ2ZWO+fnnn/X++++rtLRUixYt0oMPPqh//vOf+sc//lHlflJTU5Wbm+t67Nq1q1b1AQCAhsmjc25mzpypp59+WpMnT5YkzZ8/XzfccIMKCgp04403npYCT1RWVqaYmBi99NJLCgwMVO/evfXbb79p5syZmj59eqVj7Ha77Hb7aa8NAADUDx6Fm/LLvstdc801at68uUaOHKni4mJdeeWVtZ4rOjpagYGBFe5onJOTo7i4uErHtGjRQsHBwQoMDHS1nXPOOcrOzlZRUZFCQkI8eToAAMCCPPpYyuFwVAgjF154oT799FPde++9eu6552o9V0hIiHr37q309HRXW1lZmdLT05WUlFTpmP79+yszM1NlZWWuth9//FEtWrQg2AAAAEkehpt+/fpp8eLFFdovuOACffLJJ5o1a5ZHO09JSdHLL7+sN954Q1u2bNGtt96qI0eOaMKECZKksWPHKjU11dX/1ltv1cGDB3XHHXfoxx9/1MKFC/X4449r0qRJHu0XAABYl0cfS9111136+uuvK902ePBgffLJJ3rzzTdrPd/o0aO1b98+PfTQQ8rOzlaPHj20ZMkS10nGWVlZCgj4I3/Fx8frf//7n+666y5169ZNrVq10h133KEpU6Z48jQAAICF+fU+N/7A1y8AANDwePL+7dUdiqXj58dkZmZq7969bufASNKgQYO8nRYAAOCUeBVuVq1apWuvvVa//PKLTj7wY7PZqr2pHgAAwOnkVbi55ZZb1KdPHy1cuFAtWrRw+8JMAAAAf/Iq3Gzfvl3vv/++EhISfF0PAADAKfH4u6Wk4194mZmZ6etaAAAATplXR25uv/123X333crOzlbXrl0rfEN3t27dfFIcAACAp7y6FPzEe8+4JrLZZIyp9ycUcyk4AAANz2m/FHzHjh1eFQYAAHC6eRVu2rRp4+s6AAAAfKLW4ebjjz/WpZdequDgYH388cfV9h05cuQpFwYAAOCNWp9zExAQoOzsbMXExFR6zo1rQs65AQAAPnZazrk58SsWTv66BQAAgPrCq/vcAAAA1FdenVD87LPP1rrv5MmTvdkFAACAV7y6z027du20b98+HT16VJGRkZKkw4cPq1GjRmrevPkfk9ts+vnnn31WrC9wzg0AAA2PJ+/fXn0s9dhjj6lHjx7asmWLDh48qIMHD2rLli3q1auX/vGPf2jHjh3asWNHvQs2AADA+rw6ctO+fXu9//776tmzp1v7mjVr9Oc//7le3+SPIzcAADQ8p/3IzZ49e1RSUlKhvbS0VDk5Od5MCQAA4BNehZshQ4bor3/9q9auXetqW7NmjW699VYlJyf7rDgAAABPeRVuXnvtNcXFxalPnz6y2+2y2+3q16+fYmNj9corr/i6RgAAgFrz6lLw5s2ba9GiRfrxxx+1detWSVLnzp3VsWNHnxYHAADgKa/CTbmOHTsSaAAAQL1S63CTkpJS60mffvppr4oBAAA4VbUON+vWratVP5vN5nUxAAAAp6rW4WbZsmWnsw4AAACf8PqLM40x2r9/vw4cOODLegAAAE6Jx+EmOztbY8eOVVRUlGJjYxUTE6OoqCjdcMMN3MAPAAD4nUdXSzmdTp1//vnKz8/XhAkT1LlzZxljtHnzZr3zzjtauXKl1q5dq/Dw8NNVLwAAQLU8CjfPPPOMAgMD9cMPP7h9+7ckTZs2Tf3799ezzz6r+++/36dFAgAA1JZHH0stXLhQ999/f4VgI0kxMTFKTU3VJ5984rPiAAAAPOVRuPnxxx91/vnnV7n9/PPP17Zt2065KAAAAG95FG6cTqciIyOr3B4ZGSmn03mqNQEAAHjNo3BjjFFAQNVDbDabjDGnXBQAAIC3PDqh2Bijjh07VnkXYoINAADwN4/Czeuvv3666gAAAPAJj8LNuHHjTlcdAAAAPuHV1y/s2rVLv/76q+vn1atX684779RLL73ks8IAAAC84VW4ufbaa11fpJmdna3k5GStXr1aDzzwgB555BGfFggAAOAJr8LNpk2b1K9fP0nS/Pnz1bVrV3399dd66623NHfuXF/WBwAA4BGvwk1xcbHsdrsk6fPPP9fIkSMlSZ07d9aePXt8Vx0AAICHvAo35513nl544QWtWLFCn332mYYNGyZJ2r17t5o1a+bTAgEAADzhVbh54okn9OKLL2rw4MEaM2aMunfvLkn6+OOPXR9XAQAA+IPNeHnnvdLSUjmdTkVFRbnadu7cqUaNGikmJsZnBfqa0+lURESEcnNz5XA4/F0OAACoBU/evz26z82JAgMD3YKNJLVt29bb6QAAAHzCq3DTrl27Kr+CQZJ+/vlnrwsCAAA4FV6FmzvvvNPt5+LiYq1bt05LlizRvffe64u6AAAAvOJVuLnjjjsqbZ8zZ46+++67UyoIAADgVHh1tVRVLr30Un3wwQe+nBIAAMAjPg0377//vpo2berLKQEAADzi1cdSPXv2dDuh2Bij7Oxs7du3T88//7zPigMAAPCUV+Fm1KhRbj8HBASoefPmGjx4sDp37uyLugAAALzi9U38Gipu4gcAQMNTJzfxKy0t1X//+19t2bJF0vHvmxo5cqQCAwO9nRIAAOCUeRVuMjMzNXz4cP3222/q1KmTJCktLU3x8fFauHCh2rdv79MiAQAAasurq6UmT56s9u3ba9euXVq7dq3Wrl2rrKwstWvXTpMnT/Z1jQAAALXm1ZGbL774QqtWrXK77LtZs2aaMWOG+vfv77PiAAAAPOXVkRu73a68vLwK7fn5+QoJCfF4vjlz5qht27YKDQ1VYmKiVq9eXatx8+bNk81mq3D1FgAAOHN5FW4uv/xy3Xzzzfrmm29kjJExRqtWrdItt9yikSNHejTXu+++q5SUFE2fPl1r165V9+7dNXToUO3du7facTt37tQ999yjgQMHevMUAACARXkVbp599lm1b99eSUlJCg0NVWhoqPr376+EhAQ988wzHs319NNPa+LEiZowYYLOPfdcvfDCC2rUqJFee+21KseUlpbquuuu09///nedffbZ3jwFAABgUV6dcxMZGakFCxZo+/bt2rp1qyTpnHPOUUJCgkfzFBUVac2aNUpNTXW1BQQEKDk5WRkZGVWOe+SRRxQTE6Mbb7xRK1asqHYfhYWFKiwsdP3sdDo9qhEAADQsXt/nRpI6dOigDh06eD1+//79Ki0tVWxsrFt7bGysKzSdbOXKlXr11Ve1fv36Wu0jLS1Nf//7372uEQAANCy1DjcpKSl69NFH1bhxY6WkpFTb9+mnnz7lwiqTl5en66+/Xi+//LKio6NrNSY1NdWtXqfTqfj4+NNSHwAA8L9ah5t169apuLhYkrR27Vq3L848UVXtlYmOjlZgYKBycnLc2nNychQXF1eh/08//aSdO3dqxIgRrraysjJJUlBQkLZt21bhBoJ2u112u73WNQEAgIat1uFm2bJlrr8vX77cJzsPCQlR7969lZ6e7rqcu6ysTOnp6brtttsq9O/cubM2btzo1jZt2jTl5eXpmWee4YgMAADw/Jyb4uJihYWFaf369erSpcspF5CSkqJx48apT58+6tevn2bNmqUjR45owoQJkqSxY8eqVatWSktLU2hoaIV9RkZGSpJPagEAAA2fx+EmODhYrVu3VmlpqU8KGD16tPbt26eHHnpI2dnZ6tGjh5YsWeI6yTgrK0sBAV5dsQ4AAM5ANmOM8XTQq6++qg8//FD/+c9/3L6CoSHw5CvTAQBA/eDJ+7dXl4LPnj1bmZmZatmypdq0aaPGjRu7bV+7dq030wIAAJwyr8IN3+UEAADqK68+lmrI+FgKAICGx5P3b6/P1D18+LBeeeUVpaam6uDBg5KOfxz122+/eTslAADAKfPqY6kNGzYoOTlZERER2rlzpyZOnKimTZvqww8/VFZWlt58801f1wkAAFArXh25SUlJ0fjx47V9+3aFhoa62ocPH64vv/zSZ8UBAAB4yqtw8+233+qvf/1rhfZWrVopOzv7lIsCAADwllfhxm63y+l0Vmj/8ccf1bx581MuCgAAwFtehZuRI0fqkUcecX2Rps1mU1ZWlqZMmaKrr77apwUCAAB4wqtw889//lP5+fmKiYnRsWPHdMEFF6h9+/YKDw/XY4895usaAQAAas2rq6UiIiL02WefaeXKldqwYYPy8/PVu3dvDRkyxNf1AQAAeMSjIzcZGRn69NNPXT8PGDBAjRs31vPPP68xY8bo5ptvVmFhoc+LBAAAqC2Pws0jjzyiH374wfXzxo0bNXHiRF188cWaOnWqPvnkE6Wlpfm8SAAAgNryKNysX7/e7aOnefPmqV+/fnr55ZeVkpKiZ599VvPnz/d5kQAAALXlUbg5dOiQYmNjXT9/8cUXuvTSS10/9+3bV7t27fJddQAAAB7yKNzExsZqx44dkqSioiKtXbtWf/rTn1zb8/LyFBwc7NsKAQAAPOBRuBk+fLimTp2qFStWKDU1VY0aNdLAgQNd2zds2KD27dv7vEgAAIDa8uhS8EcffVRXXXWVLrjgAoWHh+uNN95QSEiIa/trr72mSy65xOdFAgAA1JbNGGM8HZSbm6vw8HAFBga6tR88eFDh4eFugae+cTqdioiIUG5urhwOh7/LAQAAteDJ+7fXN/GrTNOmTb2ZDgAAwGe8+voFAACA+opwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALKVehJs5c+aobdu2Cg0NVWJiolavXl1l35dfflkDBw5UVFSUoqKilJycXG1/AABwZvF7uHn33XeVkpKi6dOna+3aterevbuGDh2qvXv3Vtp/+fLlGjNmjJYtW6aMjAzFx8frkksu0W+//VbHlQMAgPrIZowx/iwgMTFRffv21ezZsyVJZWVlio+P1+23366pU6fWOL60tFRRUVGaPXu2xo4dW2N/p9OpiIgI5ebmyuFwnHL9AADg9PPk/duvR26Kioq0Zs0aJScnu9oCAgKUnJysjIyMWs1x9OhRFRcXq2nTppVuLywslNPpdHsAAADr8mu42b9/v0pLSxUbG+vWHhsbq+zs7FrNMWXKFLVs2dItIJ0oLS1NERERrkd8fPwp1w0AAOovv59zcypmzJihefPm6aOPPlJoaGilfVJTU5Wbm+t67Nq1q46rBAAAdSnInzuPjo5WYGCgcnJy3NpzcnIUFxdX7dinnnpKM2bM0Oeff65u3bpV2c9ut8tut/ukXgAAUP/59chNSEiIevfurfT0dFdbWVmZ0tPTlZSUVOW4J598Uo8++qiWLFmiPn361EWpAACggfDrkRtJSklJ0bhx49SnTx/169dPs2bN0pEjRzRhwgRJ0tixY9WqVSulpaVJkp544gk99NBDevvtt9W2bVvXuTnh4eEKDw/32/MAAAD1g9/DzejRo7Vv3z499NBDys7OVo8ePbRkyRLXScZZWVkKCPjjANO///1vFRUV6c9//rPbPNOnT9fDDz9cl6UDAIB6yO/3ualr3OcGAICGp8Hc5wYAAMDXCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSgvxdgCTNmTNHM2fOVHZ2trp3767nnntO/fr1q7L/e++9pwcffFA7d+5Uhw4d9MQTT2j48OF1WHFFvx46qryCEjmPFSsiLFjhIYEKknSoqFR5x4rlCAuWPShARWVlCg0MlDFGRpJNUlFZmYykkIAAFZaWKa+gRI1DAtUoOFABATYdLS5VXkGJHKFBCgsO1NGiEkk2GUn5BSUKDw1S45BABUjKO2F/YSGBOnSkUI3twYoIC9ax4lIdKSzRkaJSRYYFK6aJXceKSnW0uFTHikpVUFyqpo1DVFJmlF9YIkdYsKIbh6iwpEwHjxTJWVAiR1iQohqFKDQoQPvzi+QsKHb1i2gUIknKPVrkti3cHqQjhSXKPVax78lyjxZpb16hco8Vq7E9SDabFBRgU/Nwe5VjGpKT1+bktahqe03jUDXWrnKsi3XV1e+2sv1IqhevK7+Hm3fffVcpKSl64YUXlJiYqFmzZmno0KHatm2bYmJiKvT/+uuvNWbMGKWlpenyyy/X22+/rVGjRmnt2rXq0qWLH56B9MuBI7r/o436KvOAq21AQjM9PPI8Xf/qN9qfX+Rqe2jEeXpiyRalXNJJh44UqsxINtl0rLhUb2bs1JjENpr8zjpJ0qvj+uj5ZZlacdK8D15+fI6lW/e52gcmRGvShe11wxvf6WhRqVvfG+Z+qyf/3K3CXAM7RGv6iHP1+KItWvXzQT07pqee/vxHt+cxsEO0/ja4vW48Yd7j+0rQDW9862ob1CFaT1zdTUbSlA82aMX2/W41j+/fTpPfWaejRaUa1CFaM67uppaRYW7ruPvwMU15f4NWZP4xtn9CM03o304zFm/VI1d0qTCmIdl9+FiFtTlxLara/o9RXfTIp5v1+Za9lY5D1Wpa8zMV62JddfW7PXk/jUIC9dr4vpqzNNPt/3B/va5sxhhTp3s8SWJiovr27avZs2dLksrKyhQfH6/bb79dU6dOrdB/9OjROnLkiD799FNX25/+9Cf16NFDL7zwQo37czqdioiIUG5urhwOxynX/+uho5rywQa3QFBuwO9vzDe+8V2Ftte/2qHbLkrQz/uOSDp+BOe33AKtyzqknq2jJEnrsg5VOW+P1lGavTTTrb1/QjP1PKl9QEIz3Tess55YsrXauarbX2XzVtaWdlVXLdqwx+2FXVX/QR2i9dyYnm5He257e121YzfsOuw2piHJPVqk295Z5/YfTrlBHaI18/+66573vq90e1W/75PXEO5qWvMzde1YF+uqq99tZfu57aKEKt9DfLVvT96//XrOTVFRkdasWaPk5GRXW0BAgJKTk5WRkVHpmIyMDLf+kjR06NAq+xcWFsrpdLo9fCmvoKTSX6Ykrcw8oBiHvdK2lZkH1NgepFhHqGIdoYpxhKpnfKS+yjygnvGRrr9XNW/P+MgK7V9V0r4y84CCAgNqnKu6/VU2b2VtMU3slYaTyvp/uX2/64iWdPwwZk1jTx7TkOzPL6r0Pxzp+FocOlL19qp+3w15PepCTWt+pq4d62JddfW7rWw/1b2H+ON15ddws3//fpWWlio2NtatPTY2VtnZ2ZWOyc7O9qh/WlqaIiIiXI/4+HjfFP8757HiarfnF5RW2ZZfUKrCkjK3hyS3v1elqu2VtefVUKO3+zu5zdM58gr+qMtZUHONJ49pSGp6fs6Ckmq3V7W2DXU96kJNa36mrh3rYl119butbD81/f9f168ry18tlZqaqtzcXNdj165dPp3fERZc7fbw0MAq28JDA2UPCnB7SHL7e1Wq2l5Ze5MaavR2fye3eTpHk9A/6nKE1lzjyWMakpqenyO0+tPfqlrbhroedaGmNT9T1451sa66+t1Wtp+a/v+v69eVX8NNdHS0AgMDlZOT49aek5OjuLi4SsfExcV51N9ut8vhcLg9fKlJaJAGJDSrdNuAhGba6yystG1AQjMdKSxRjrNAOc4C7XUWaN2uw+qf0Ezrdh12/b2qedftOlyhvX8l7QMSmqmktKzGuarbX2XzVta2N69QgzpE12qOQR2iFR3+x+ev0eEhNY49eUxDUt3zG9QhWlGNq95e1e+7Ia9HXahpzc/UtWNdrKuufreV7ae69xB/vK78Gm5CQkLUu3dvpaenu9rKysqUnp6upKSkSsckJSW59Zekzz77rMr+p9tZUY302JVdKwSc41dLddGUDza4tT004jx9sGaXHh7ZRYE2m9o3D1dC83C1jAzTlt25mtC/nV5buUOvrdyh2y5M0MCE6ArzPjTiPG3enevWPjAhWrdf2EGvrdxRoe+kt9ZWOtfADtF6eOTxuV5buUMT+rer8OIc2CFat50078CEaN1+kXvboA7RurBjc824uluFF335SdTl/cuvrDrx5LKIRiGacXU3DTxpbPnVUlv3OCuMaUjKn9/Ja1O+FrGO0Cq3P35lV23b46x0XENdj7pQ05qfqWvHulhXXf1uK9vPayt36PaLOlT4P9xfryu/Xy317rvvaty4cXrxxRfVr18/zZo1S/Pnz9fWrVsVGxursWPHqlWrVkpLS5N0/FLwCy64QDNmzNBll12mefPm6fHHH6/1peC+vlqqXPl9bvIKitUkNFhNTrzPze9toSfd56ZMx9NlcVmZSo1kDzx+n5v8ghKFhQSqcVX3uSkukczv97kpLFG4/aT73Py+v0ZV3OfmaFGpIk6+z01xqQqK/rjPzZHCEjUJDVZ0+En3uQkNUlTjP+5zU76v6PCK97kp3xYeevw+N85jFfue7OT73ATYpEAL3uemsnWrbntN41A11q5yrIt11dXvtrL9SDpt+/bk/dvv4UaSZs+e7bqJX48ePfTss88qMTFRkjR48GC1bdtWc+fOdfV/7733NG3aNNdN/J588sla38TvdIUbAABw+jS4cFOXCDcAADQ8DeY+NwAAAL5GuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJYS5O8C6lr5DZmdTmcNPQEAQH1R/r5dmy9WOOPCTV5eniQpPj7ez5UAAABP5eXlKSIioto+Z9x3S5WVlWn37t1q0qSJbDabT+d2Op2Kj4/Xrl27+N4qsR6VYU3csR7uWI+KWBN3Z/J6GGOUl5enli1bKiCg+rNqzrgjNwEBATrrrLNO6z4cDscZ96KrDutREWvijvVwx3pUxJq4O1PXo6YjNuU4oRgAAFgK4QYAAFgK4caH7Ha7pk+fLrvd7u9S6gXWoyLWxB3r4Y71qIg1ccd61M4Zd0IxAACwNo7cAAAASyHcAAAASyHcAAAASyHcAAAASyHc+MicOXPUtm1bhYaGKjExUatXr/Z3SR57+OGHZbPZ3B6dO3d2bS8oKNCkSZPUrFkzhYeH6+qrr1ZOTo7bHFlZWbrsssvUqFEjxcTE6N5771VJSYlbn+XLl6tXr16y2+1KSEjQ3LlzK9Tir/X88ssvNWLECLVs2VI2m03//e9/3bYbY/TQQw+pRYsWCgsLU3JysrZv3+7W5+DBg7ruuuvkcDgUGRmpG2+8Ufn5+W59NmzYoIEDByo0NFTx8fF68sknK9Ty3nvvqXPnzgoNDVXXrl21aNEij2s5VTWtx/jx4yu8ZoYNG+bWx0rrkZaWpr59+6pJkyaKiYnRqFGjtG3bNrc+9enfSW1qOVW1WZPBgwdXeJ3ccsstbn2ssib//ve/1a1bN9dN9pKSkrR48WKP9m+VtfArg1M2b948ExISYl577TXzww8/mIkTJ5rIyEiTk5Pj79I8Mn36dHPeeeeZPXv2uB779u1zbb/llltMfHy8SU9PN999953505/+ZM4//3zX9pKSEtOlSxeTnJxs1q1bZxYtWmSio6NNamqqq8/PP/9sGjVqZFJSUszmzZvNc889ZwIDA82SJUtcffy5nosWLTIPPPCA+fDDD40k89FHH7ltnzFjhomIiDD//e9/zffff29Gjhxp2rVrZ44dO+bqM2zYMNO9e3ezatUqs2LFCpOQkGDGjBnj2p6bm2tiY2PNddddZzZt2mTeeecdExYWZl588UVXn6+++soEBgaaJ5980mzevNlMmzbNBAcHm40bN3pUy+lej3Hjxplhw4a5vWYOHjzo1sdK6zF06FDz+uuvm02bNpn169eb4cOHm9atW5v8/HxXn/r076SmWupqTS644AIzceJEt9dJbm6uJdfk448/NgsXLjQ//vij2bZtm7n//vtNcHCw2bRpU632b6W18CfCjQ/069fPTJo0yfVzaWmpadmypUlLS/NjVZ6bPn266d69e6XbDh8+bIKDg817773natuyZYuRZDIyMowxx98IAwICTHZ2tqvPv//9b+NwOExhYaExxpj77rvPnHfeeW5zjx492gwdOtT1c31Zz5PfzMvKykxcXJyZOXOmq+3w4cPGbrebd955xxhjzObNm40k8+2337r6LF682NhsNvPbb78ZY4x5/vnnTVRUlGtNjDFmypQpplOnTq6fr7nmGnPZZZe51ZOYmGj++te/1roWX6sq3FxxxRVVjrHyehhjzN69e40k88UXX7j2WV/+ndSmltPh5DUx5ni4ueOOO6ocY/U1iYqKMq+88gqvjzrEx1KnqKioSGvWrFFycrKrLSAgQMnJycrIyPBjZd7Zvn27WrZsqbPPPlvXXXedsrKyJElr1qxRcXGx2/Ps3LmzWrdu7XqeGRkZ6tq1q2JjY119hg4dKqfTqR9++MHV58Q5yvuUz1Gf13PHjh3Kzs52qy0iIkKJiYluaxAZGak+ffq4+iQnJysgIEDffPONq8+gQYMUEhLi6jN06FBt27ZNhw4dcvWpbp1qU0tdWb58uWJiYtSpUyfdeuutOnDggGub1dcjNzdXktS0aVNJ9evfSW1qOR1OXpNyb731lqKjo9WlSxelpqbq6NGjrm1WXZPS0lLNmzdPR44cUVJSEq+POnTGfXGmr+3fv1+lpaVuL0RJio2N1datW/1UlXcSExM1d+5cderUSXv27NHf//53DRw4UJs2bVJ2drZCQkIUGRnpNiY2NlbZ2dmSpOzs7ErXoXxbdX2cTqeOHTumQ4cO1dv1LH8OldV24vOLiYlx2x4UFKSmTZu69WnXrl2FOcq3RUVFVblOJ85RUy11YdiwYbrqqqvUrl07/fTTT7r//vt16aWXKiMjQ4GBgZZej7KyMt15553q37+/unTp4qqjvvw7qU0tvlbZmkjStddeqzZt2qhly5basGGDpkyZom3btunDDz901WqlNdm4caOSkpJUUFCg8PBwffTRRzr33HO1fv36M/r1UZcIN3C59NJLXX/v1q2bEhMT1aZNG82fP19hYWF+rAz11V/+8hfX37t27apu3bqpffv2Wr58uYYMGeLHyk6/SZMmadOmTVq5cqW/S6k3qlqTm2++2fX3rl27qkWLFhoyZIh++ukntW/fvq7LPO06deqk9evXKzc3V++//77GjRunL774wt9lnVH4WOoURUdHKzAwsMIZ5jk5OYqLi/NTVb4RGRmpjh07KjMzU3FxcSoqKtLhw4fd+pz4POPi4ipdh/Jt1fVxOBwKCwur1+tZvv/qaouLi9PevXvdtpeUlOjgwYM+WacTt9dUiz+cffbZio6OVmZmpiTrrsdtt92mTz/9VMuWLdNZZ53laq9P/05qU4svVbUmlUlMTJQkt9eJldYkJCRECQkJ6t27t9LS0tS9e3c988wzZ/Tro64Rbk5RSEiIevfurfT0dFdbWVmZ0tPTlZSU5MfKTl1+fr5++ukntWjRQr1791ZwcLDb89y2bZuysrJczzMpKUkbN250ezP77LPP5HA4dO6557r6nDhHeZ/yOerzerZr105xcXFutTmdTn3zzTdua3D48GGtWbPG1Wfp0qUqKytz/YeelJSkL7/8UsXFxa4+n332mTp16qSoqChXn+rWqTa1+MOvv/6qAwcOqEWLFpKstx7GGN1222366KOPtHTp0gofp9Wnfye1qcUXalqTyqxfv16S3F4nVlqTk5WVlamwsPCMfH34jb/PaLaCefPmGbvdbubOnWs2b95sbr75ZhMZGel2tntDcPfdd5vly5ebHTt2mK+++sokJyeb6Ohos3fvXmPM8csGW7dubZYuXWq+++47k5SUZJKSklzjyy9hvOSSS8z69evNkiVLTPPmzSu9hPHee+81W7ZsMXPmzKn0EkZ/rWdeXp5Zt26dWbdunZFknn76abNu3Trzyy+/GGOOX24cGRlpFixYYDZs2GCuuOKKSi8F79mzp/nmm2/MypUrTYcOHdwufT58+LCJjY01119/vdm0aZOZN2+eadSoUYVLn4OCgsxTTz1ltmzZYqZPn17ppc811XI61yMvL8/cc889JiMjw+zYscN8/vnnplevXqZDhw6moKDAkutx6623moiICLN8+XK3y5qPHj3q6lOf/p3UVEtdrElmZqZ55JFHzHfffWd27NhhFixYYM4++2wzaNAgS67J1KlTzRdffGF27NhhNmzYYKZOnWpsNpv5f//v/9Vq/1ZaC38i3PjIc889Z1q3bm1CQkJMv379zKpVq/xdksdGjx5tWrRoYUJCQkyrVq3M6NGjTWZmpmv7sWPHzN/+9jcTFRVlGjVqZK688kqzZ88etzl27txpLr30UhMWFmaio6PN3XffbYqLi936LFu2zPTo0cOEhISYs88+27z++usVavHXei5btsxIqvAYN26cMeb4JccPPvigiY2NNXa73QwZMsRs27bNbY4DBw6YMWPGmPDwcONwOMyECRNMXl6eW5/vv//eDBgwwNjtdtOqVSszY8aMCrXMnz/fdOzY0YSEhJjzzjvPLFy40G17bWo5netx9OhRc8kll5jmzZub4OBg06ZNGzNx4sQKIdRK61HZWkhyew3Xp38ntanlVNW0JllZWWbQoEGmadOmxm63m4SEBHPvvfe63efGSmtyww03mDZt2piQkBDTvHlzM2TIEFewqe3+rbIW/mQzxpi6O04EAABwenHODQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDYB65+GHH1aPHj38XUYF48eP16hRo/xdBoAaEG4A+Mz48eNls9lks9kUHBys2NhYXXzxxXrttddUVlbm831VFjTK92+z2eRwONS3b18tWLDAo7l37twpm83m+oLHcs8884zmzp3rfdEA6gThBoBPDRs2THv27NHOnTu1ePFiXXjhhbrjjjt0+eWXq6SkpE5qeP3117Vnzx5999136t+/v/785z9r48aNpzxvRESEIiMjT71AAKcV4QaAT9ntdsXFxalVq1bq1auX7r//fi1YsECLFy92HfU4fPiwbrrpJjVv3lwOh0MXXXSRvv/++wpzvfjii4qPj1ejRo10zTXXKDc3V9Lxj63eeOMNLViwwHWUZvny5a5xkZGRiouLU8eOHfXoo4+qpKREy5Ytc21fsmSJBgwYoMjISDVr1kyXX365fvrpJ9f2du3aSZJ69uwpm82mwYMHS6p4tKiwsFCTJ09WTEyMQkNDNWDAAH377bc+WkkA3iLcADjtLrroInXv3l0ffvihJOn//u//tHfvXi1evFhr1qxRr169NGTIEB08eNA1JjMzU/Pnz9cnn3yiJUuWaN26dfrb3/4mSbrnnnt0zTXXuI4S7dmzR+eff36F/ZaUlOjVV1+VJIWEhLjajxw5opSUFH333XdKT09XQECArrzyStdHZ6tXr5Ykff7559qzZ4+r7pPdd999+uCDD/TGG29o7dq1SkhI0NChQ92eBwA/8PfXkgOwjnHjxpkrrrii0m2jR48255xzjlmxYoVxOBymoKDAbXv79u3Niy++aIwxZvr06SYwMND8+uuvru2LFy82AQEBZs+ePdXuS5IJDQ01jRs3NgEBAUaSadu2rTlw4ECVde/bt89IMhs3bjTGGLNjxw4jyaxbt67K55efn2+Cg4PNW2+95dpeVFRkWrZsaZ588skq9wXg9OPIDYA6YYyRzWbT999/r/z8fDVr1kzh4eGux44dO9w+GmrdurVatWrl+jkpKUllZWXatm1bjfv617/+pfXr12vx4sU699xz9corr6hp06au7du3b9eYMWN09tlny+FwqG3btpKkrKysWj+fn376ScXFxerfv7+rLTg4WP369dOWLVtqPQ8A3wvydwEAzgxbtmxRu3btlJ+frxYtWridI1POVyfrxsXFKSEhQQkJCXr99dc1fPhwbd68WTExMZKkESNGqE2bNnr55ZfVsmVLlZWVqUuXLioqKvLJ/gH4F0duAJx2S5cu1caNG3X11VerV69eys7OVlBQkCuAlD+io6NdY7KysrR7927Xz6tWrVJAQIA6deok6fg5NKWlpTXuu1+/furdu7cee+wxSdKBAwe0bds2TZs2TUOGDNE555yjQ4cOuY0pPz+nuvnbt2+vkJAQffXVV6624uJiffvttzr33HNrsSoATheO3ADwqcLCQmVnZ6u0tFQ5OTlasmSJ0tLSdPnll2vs2LEKCAhQUlKSRo0apSeffFIdO3bU7t27tXDhQl155ZXq06ePJCk0NFTjxo3TU089JafTqcmTJ+uaa65RXFycJKlt27b63//+p23btqlZs2aKiIhQcHBwpTXdeeeduvLKK3XfffepRYsWatasmV566SW1aNFCWVlZmjp1qlv/mJgYhYWFacmSJTrrrLMUGhqqiIgItz6NGzfWrbfeqnvvvVdNmzZV69at9eSTT+ro0aO68cYbT8PKAqgtjtwA8KklS5aoRYsWatu2rYYNG6Zly5bp2Wef1YIFCxQYGCibzaZFixZp0KBBmjBhgjp27Ki//OUv+uWXXxQbG+uaJyEhQVdddZWGDx+uSy65RN26ddPzzz/v2j5x4kR16tRJffr0UfPmzd2OoJxs2LBhateunR577DEFBARo3rx5WrNmjbp06aK77rpLM2fOdOsfFBSkZ599Vi+++KJatmypK664otJ5Z8yYoauvvlrXX3+9evXqpczMTP3vf/9TVFTUKa4igFNhM8YYfxcBAADgKxy5AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlvL/A3d+BS1C8jZsAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Vu3Zt9e/fX3l5ebr77rvLXCssLEw+Pj5FrmiclZWliIiIYpepW7eufH195ePjY5vWqlUrWSwW5ebmys/Pz5HVAQAAJuTQ11LBwcFFwsif//xnffbZZ5o4caJeeeWVMtfy8/NTx44dlZqaaptWUFCg1NRUxcbGFrvMzTffrH379qmgoMA2bc+ePapbty7BBgAASHIw3HTp0kWff/55kendu3fXp59+qrlz5zr04gkJCXrzzTf1zjvvaOfOnXr88cd14cIFDRs2TJI0ePBgJSYm2sY//vjjOnPmjMaNG6c9e/Zo1apVmjFjhkaPHu3Q6wIAAPNy6GupJ598Ups3by52Xo8ePfTpp5/q3XffLXO9gQMH6uTJk5o6daosFovatWunNWvW2E4yPnTokLy9/5u/IiMj9cUXX+jJJ59U27ZtVb9+fY0bN06TJk1yZDUAAICJufU6N+7A7RcAAPA8juy/nbpCsfTr+TH79u3TiRMn7M6BkaRbb73V2bIAAADXxalws2XLFj344IM6ePCgfn/gx8vLq9SL6gEAAJQnp8LNY489pk6dOmnVqlWqW7eu3Q0zAQAA3MmpcLN3714tW7ZMTZs2dXU/AAAA18Xhe0tJv97wct++fa7uBQAA4Lo5deTmiSee0IQJE2SxWNSmTZsid+hu27atS5oDAABwlFM/Bf/ttWdshby8ZBhGpT+hmJ+CAwDgecr9p+CZmZlONQYAAFDenAo3jRo1cnUfAAAALlHmcLNy5Ur16dNHvr6+WrlyZalj+/fvf92NAQAAOKPM59x4e3vLYrGoTp06xZ5zYyvIOTcAAMDFyuWcm9/eYuH3t1sAAACoLJy6zg0AAEBl5dQJxfPmzSvz2LFjxzrzEgAAAE5x6jo3jRs31smTJ3Xx4kWFhoZKks6dO6dq1aqpdu3a/y3u5aVffvnFZc26AufcAADgeRzZfzv1tdQLL7ygdu3aaefOnTpz5ozOnDmjnTt3qkOHDnr++eeVmZmpzMzMShdsAACA+Tl15KZJkyZatmyZ2rdvbzc9PT1d9913X6W+yB9HbgAA8DzlfuTm+PHjunr1apHp+fn5ysrKcqYkAACASzgVbm677TY9+uijysjIsE1LT0/X448/rri4OJc1BwAA4Cinws3bb7+tiIgIderUSf7+/vL391eXLl0UHh6uf/zjH67uEQAAoMyc+il47dq1tXr1au3Zs0e7du2SJLVs2VLNmzd3aXMAAACOcircFGrevDmBBgAAVCplDjcJCQllLjpnzhynmgEAALheZQ4327ZtK9M4Ly8vp5sBAAC4XmUON+vXry/PPgAAAFzC6RtnGoahU6dO6fTp067sBwAA4Lo4HG4sFosGDx6sGjVqKDw8XHXq1FGNGjU0fPhwLuAHAADczqFfS1mtVnXt2lXnz5/XsGHD1LJlSxmGoZ9//lkffvihNm3apIyMDAUFBZVXvwAAAKVyKNz8/e9/l4+Pj3766Se7u39L0rPPPqubb75Z8+bN09NPP+3SJgEAAMrKoa+lVq1apaeffrpIsJGkOnXqKDExUZ9++qnLmgMAAHCUQ+Fmz5496tq1a4nzu3btqt27d193UwAAAM5yKNxYrVaFhoaWOD80NFRWq/V6ewIAAHCaQ+HGMAx5e5e8iJeXlwzDuO6mAAAAnOXQCcWGYah58+YlXoWYYAMAANzNoXCzcOHC8uoDAADAJRwKN0OGDCmvPgAAAFzCqdsvHD58WEeOHLE937p1q8aPH6833njDZY0BAAA4w6lw8+CDD9pupGmxWBQXF6etW7fqmWee0bRp01zaIAAAgCOcCjc7duxQly5dJElLly5VmzZttHnzZr3//vtatGiRK/sDAABwiFPhJi8vT/7+/pKkdevWqX///pKkli1b6vjx467rDgAAwEFOhZsbb7xR8+fP19dff621a9eqd+/ekqRjx46pVq1aLm0QAADAEU6Fm1mzZmnBggXq0aOHBg0apOjoaEnSypUrbV9XAQAAuIOX4eSV9/Lz82W1WlWjRg3btAMHDqhatWqqU6eOyxp0NavVqpCQEGVnZys4ONjd7QAAgDJwZP/t0HVufsvHx8cu2EhSVFSUs+UAAABcwqlw07hx4xJvwSBJv/zyi9MNAQAAXA+nws348ePtnufl5Wnbtm1as2aNJk6c6Iq+AAAAnOJUuBk3blyx01NSUvT9999fV0MAAADXw6lfS5WkT58++te//uXKkgAAAA5xabhZtmyZatas6cqSAAAADnHqa6n27dvbnVBsGIYsFotOnjyp1157zWXNAQAAOMqpcDNgwAC7597e3qpdu7Z69Oihli1buqIvAAAApzh9ET9PxUX8AADwPBVyEb/8/Hx98skn2rlzp6Rf7zfVv39/+fj4OFsSAADgujkVbvbt26e+ffvq6NGjatGihSQpOTlZkZGRWrVqlZo0aeLSJgEAAMrKqV9LjR07Vk2aNNHhw4eVkZGhjIwMHTp0SI0bN9bYsWNd3SMAAECZOXXkZuPGjdqyZYvdz75r1aqlmTNn6uabb3ZZcwAAAI5y6siNv7+/cnJyikw/f/68/Pz8HK6XkpKiqKgoBQQEKCYmRlu3bi3TcosXL5aXl1eRX28BAIA/LqfCzZ133qlRo0bp22+/lWEYMgxDW7Zs0WOPPab+/fs7VGvJkiVKSEhQUlKSMjIyFB0drfj4eJ04caLU5Q4cOKCnnnpK3bp1c2YVAACASTkVbubNm6cmTZooNjZWAQEBCggI0M0336ymTZvq73//u0O15syZo5EjR2rYsGH605/+pPnz56tatWp6++23S1wmPz9fDz30kP7617/qhhtucGYVAACASTl1zk1oaKhWrFihvXv3ateuXZKkVq1aqWnTpg7Vyc3NVXp6uhITE23TvL29FRcXp7S0tBKXmzZtmurUqaNHHnlEX3/9damvceXKFV25csX23Gq1OtQjAADwLE5f50aSmjVrpmbNmjm9/KlTp5Sfn6/w8HC76eHh4bbQ9HubNm3SW2+9pR9++KFMr5GcnKy//vWvTvcIAAA8S5nDTUJCgqZPn67AwEAlJCSUOnbOnDnX3VhxcnJy9PDDD+vNN99UWFhYmZZJTEy069dqtSoyMrJc+gMAAO5X5nCzbds25eXlSZIyMjLsbpz5WyVNL05YWJh8fHyUlZVlNz0rK0sRERFFxu/fv18HDhxQv379bNMKCgokSVWqVNHu3buLXEDQ399f/v7+Ze4JAAB4tjKHm/Xr19v+e8OGDS55cT8/P3Xs2FGpqam2n3MXFBQoNTVVY8aMKTK+ZcuW+vHHH+2mPfvss8rJydHf//53jsgAAADHz7nJy8tT1apV9cMPP6h169bX3UBCQoKGDBmiTp06qUuXLpo7d64uXLigYcOGSZIGDx6s+vXrKzk5WQEBAUVeMzQ0VJJc0gsAAPB8DocbX19fNWzYUPn5+S5pYODAgTp58qSmTp0qi8Widu3aac2aNbaTjA8dOiRvb6d+sQ4AAP6AvAzDMBxd6K233tLHH3+s9957z+4WDJ7AkVumAwCAysGR/bdTPwV/9dVXtW/fPtWrV0+NGjVSYGCg3fyMjAxnygIAAFw3p8IN93ICAACVlVNfS3kyvpYCAMDzOLL/dvpM3XPnzukf//iHEhMTdebMGUm/fh119OhRZ0sCAABcN6e+ltq+fbvi4uIUEhKiAwcOaOTIkapZs6Y+/vhjHTp0SO+++66r+wQAACgTp47cJCQkaOjQodq7d68CAgJs0/v27auvvvrKZc0BAAA4yqlw89133+nRRx8tMr1+/fqyWCzX3RQAAICznAo3/v7+slqtRabv2bNHtWvXvu6mAAAAnOVUuOnfv7+mTZtmu5Gml5eXDh06pEmTJunee+91aYMAAACOcCrcvPzyyzp//rzq1KmjS5cuqXv37mrSpImCgoL0wgsvuLpHAACAMnPq11IhISFau3atNm3apO3bt+v8+fPq2LGjbrvtNlf3BwAA4BCHjtykpaXps88+sz2/5ZZbFBgYqNdee02DBg3SqFGjdOXKFZc3CQAAUFYOhZtp06bpp59+sj3/8ccfNXLkSN1+++2aPHmyPv30UyUnJ7u8SQAAgLJyKNz88MMPdl89LV68WF26dNGbb76phIQEzZs3T0uXLnV5kwAAAGXlULg5e/aswsPDbc83btyoPn362J537txZhw8fdl13AAAADnIo3ISHhyszM1OSlJubq4yMDN100022+Tk5OfL19XVthwAAAA5wKNz07dtXkydP1tdff63ExERVq1ZN3bp1s83fvn27mjRp4vImAQAAysqhn4JPnz5d99xzj7p3766goCC988478vPzs81/++231atXL5c3CQAAUFZehmEYji6UnZ2toKAg+fj42E0/c+aMgoKC7AJPZWO1WhUSEqLs7GwFBwe7ux0AAFAGjuy/nb6IX3Fq1qzpTDkAAACXcer2CwAAAJUV4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJhKpQg3KSkpioqKUkBAgGJiYrR169YSx7755pvq1q2batSooRo1aiguLq7U8QAA4I/F7eFmyZIlSkhIUFJSkjIyMhQdHa34+HidOHGi2PEbNmzQoEGDtH79eqWlpSkyMlK9evXS0aNHK7hzAABQGXkZhmG4s4GYmBh17txZr776qiSpoKBAkZGReuKJJzR58uRrLp+fn68aNWro1Vdf1eDBg6853mq1KiQkRNnZ2QoODr7u/gEAQPlzZP/t1iM3ubm5Sk9PV1xcnG2at7e34uLilJaWVqYaFy9eVF5enmrWrFns/CtXrshqtdo9AACAebk13Jw6dUr5+fkKDw+3mx4eHi6LxVKmGpMmTVK9evXsAtJvJScnKyQkxPaIjIy87r4BAEDl5fZzbq7HzJkztXjxYi1fvlwBAQHFjklMTFR2drbtcfjw4QruEgAAVKQq7nzxsLAw+fj4KCsry256VlaWIiIiSl32pZde0syZM7Vu3Tq1bdu2xHH+/v7y9/d3Sb8AAKDyc+uRGz8/P3Xs2FGpqam2aQUFBUpNTVVsbGyJy7344ouaPn261qxZo06dOlVEqwAAwEO49ciNJCUkJGjIkCHq1KmTunTporlz5+rChQsaNmyYJGnw4MGqX7++kpOTJUmzZs3S1KlT9cEHHygqKsp2bk5QUJCCgoLcth4AAKBycHu4GThwoE6ePKmpU6fKYrGoXbt2WrNmje0k40OHDsnb+78HmF5//XXl5ubqvvvus6uTlJSk5557riJbBwAAlZDbr3NT0bjODQAAnsdjrnMDAADgaoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKlXc3YAkpaSkaPbs2bJYLIqOjtYrr7yiLl26lDj+o48+0pQpU3TgwAE1a9ZMs2bNUt++fSuw46KOnL2onMtXZb2Up5CqvgoKqKIGNapV2rrlWdsTey7P2vRcMbXLs2dPlH0xV6fO58p6OU/BVX0VFuinkGp+7m4LJldZ3nduDzdLlixRQkKC5s+fr5iYGM2dO1fx8fHavXu36tSpU2T85s2bNWjQICUnJ+vOO+/UBx98oAEDBigjI0OtW7d2wxpIB09f0NPLf9Q3+07bpt3StJZeuLuNGtUKrHR16bniatOz5/fsiY6du6RJ/9qur/eesk27tVmYZt7bVvVCq7qxM5hZZXrfeRmGYVToK/5OTEyMOnfurFdffVWSVFBQoMjISD3xxBOaPHlykfEDBw7UhQsX9Nlnn9mm3XTTTWrXrp3mz59/zdezWq0KCQlRdna2goODr7v/I2cvatK/ttv9o1rolqa1NPPetk7932N51aXniqtNz57fsyfKvpirMR9us9vBFLq1WZheGdSeIzhwuYp43zmy/3brOTe5ublKT09XXFycbZq3t7fi4uKUlpZW7DJpaWl24yUpPj6+xPFXrlyR1Wq1e7hSzuWrxf6jKkmb9p1WzuWrlapuedb2xJ7LszY9V0zt8uzZE506n1vsDkaSvtp7SqfO51ZwR/gjqGzvO7eGm1OnTik/P1/h4eF208PDw2WxWIpdxmKxODQ+OTlZISEhtkdkZKRrmv9/1kt5pc7PuVz6/IquW561PbHn8qxNzxVTuzx79kTWa6zvH+3vgYpR2d53pv+1VGJiorKzs22Pw4cPu7R+cFXfUudXDyh9fkXXLc/anthzedam54qpXZ49e6Lga6zvH+3vgYpR2d53bg03YWFh8vHxUVZWlt30rKwsRUREFLtMRESEQ+P9/f0VHBxs93Cl6gFVdEvTWsXOu6VpLVUPcO6c7fKqW561PbHn8qxNzxVTuzx79kRhQX66tVlYsfNubRamsCDOt4HrVbb3nVvDjZ+fnzp27KjU1FTbtIKCAqWmpio2NrbYZWJjY+3GS9LatWtLHF/eGtSophfublPkH9fCX2o4eyJjedWl54qrTc+e37MnCqnmp5n3ti2yo7m1WZhm3duWk4lRLirb+87tv5ZasmSJhgwZogULFqhLly6aO3euli5dql27dik8PFyDBw9W/fr1lZycLOnXn4J3795dM2fO1B133KHFixdrxowZZf4puKt/LVWo8BobOZfzVD3AV9VdfO0OV9ctz9qe2HN51qbniqldnj17osLrjRT+PcKCuM4Nyl95vu8c2X+7/XjtwIEDdfLkSU2dOlUWi0Xt2rXTmjVrbCcNHzp0SN7e/z3A1LVrV33wwQd69tln9fTTT6tZs2b65JNP3HaNm0Ll9Y9oef7jTM8VU5ueK6b2HznIFCekGmEGFa+yvO/cfuSmopXXkRsAAFB+POY6NwAAAK5GuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKbi9tsvVLTCCzJbrVY3dwIAAMqqcL9dlhsr/OHCTU5OjiQpMjLSzZ0AAABH5eTkKCQkpNQxf7h7SxUUFOjYsWOqXr26vLy8XFrbarUqMjJShw8fNuV9q8y+fpL515H183xmX0fWz/OV1zoahqGcnBzVq1fP7obaxfnDHbnx9vZWgwYNyvU1goODTfumlcy/fpL515H183xmX0fWz/OVxzpe64hNIU4oBgAApkK4AQAApkK4cSF/f38lJSXJ39/f3a2UC7Ovn2T+dWT9PJ/Z15H183yVYR3/cCcUAwAAc+PIDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCjYNSUlIUFRWlgIAAxcTEaOvWraWO/+ijj9SyZUsFBASoTZs2Wr16dQV16pjk5GR17txZ1atXV506dTRgwADt3r271GUWLVokLy8vu0dAQEAFdey45557rki/LVu2LHUZT9l+khQVFVVk/by8vDR69Ohix1f27ffVV1+pX79+qlevnry8vPTJJ5/YzTcMQ1OnTlXdunVVtWpVxcXFae/evdes6+hnuDyVto55eXmaNGmS2rRpo8DAQNWrV0+DBw/WsWPHSq3pzPu8vFxrGw4dOrRIr717975mXU/ZhpKK/Ux6eXlp9uzZJdasLNuwLPuFy5cva/To0apVq5aCgoJ07733Kisrq9S6zn52HUG4ccCSJUuUkJCgpKQkZWRkKDo6WvHx8Tpx4kSx4zdv3qxBgwbpkUce0bZt2zRgwAANGDBAO3bsqODOr23jxo0aPXq0tmzZorVr1yovL0+9evXShQsXSl0uODhYx48ftz0OHjxYQR0758Ybb7Trd9OmTSWO9aTtJ0nfffed3bqtXbtWkvSXv/ylxGUq8/a7cOGCoqOjlZKSUuz8F198UfPmzdP8+fP17bffKjAwUPHx8bp8+XKJNR39DJe30tbx4sWLysjI0JQpU5SRkaGPP/5Yu3fvVv/+/a9Z15H3eXm61jaUpN69e9v1+uGHH5Za05O2oSS7dTt+/LjefvtteXl56d577y21bmXYhmXZLzz55JP69NNP9dFHH2njxo06duyY7rnnnlLrOvPZdZiBMuvSpYsxevRo2/P8/HyjXr16RnJycrHj77//fuOOO+6wmxYTE2M8+uij5dqnK5w4ccKQZGzcuLHEMQsXLjRCQkIqrqnrlJSUZERHR5d5vCdvP8MwjHHjxhlNmjQxCgoKip3vSdtPkrF8+XLb84KCAiMiIsKYPXu2bdq5c+cMf39/48MPPyyxjqOf4Yr0+3UsztatWw1JxsGDB0sc4+j7vKIUt35Dhgwx7rrrLofqePo2vOuuu4yePXuWOqaybsPf7xfOnTtn+Pr6Gh999JFtzM6dOw1JRlpaWrE1nP3sOoojN2WUm5ur9PR0xcXF2aZ5e3srLi5OaWlpxS6TlpZmN16S4uPjSxxfmWRnZ0uSatasWeq48+fPq1GjRoqMjNRdd92ln376qSLac9revXtVr1493XDDDXrooYd06NChEsd68vbLzc3VP//5Tw0fPrzUG8R62vYrlJmZKYvFYrd9QkJCFBMTU+L2ceYzXNlkZ2fLy8tLoaGhpY5z5H3ubhs2bFCdOnXUokULPf744zp9+nSJYz19G2ZlZWnVqlV65JFHrjm2Mm7D3+8X0tPTlZeXZ7c9WrZsqYYNG5a4PZz57DqDcFNGp06dUn5+vsLDw+2mh4eHy2KxFLuMxWJxaHxlUVBQoPHjx+vmm29W69atSxzXokULvf3221qxYoX++c9/qqCgQF27dtWRI0cqsNuyi4mJ0aJFi7RmzRq9/vrryszMVLdu3ZSTk1PseE/dfpL0ySef6Ny5cxo6dGiJYzxt+/1W4TZwZPs48xmuTC5fvqxJkyZp0KBBpd6M0NH3uTv17t1b7777rlJTUzVr1ixt3LhRffr0UX5+frHjPX0bvvPOO6pevfo1v7apjNuwuP2CxWKRn59fkbB9rf1i4ZiyLuOMP9xdwXFto0eP1o4dO675HW9sbKxiY2Ntz7t27apWrVppwYIFmj59enm36bA+ffrY/rtt27aKiYlRo0aNtHTp0jL9n5Qneeutt9SnTx/Vq1evxDGetv3+yPLy8nT//ffLMAy9/vrrpY71pPf5Aw88YPvvNm3aqG3btmrSpIk2bNig2267zY2dlY+3335bDz300DVP3K+M27Cs+4XKgiM3ZRQWFiYfH58iZ4FnZWUpIiKi2GUiIiIcGl8ZjBkzRp999pnWr1+vBg0aOLSsr6+v2rdvr3379pVTd64VGhqq5s2bl9ivJ24/STp48KDWrVunESNGOLScJ22/wm3gyPZx5jNcGRQGm4MHD2rt2rWlHrUpzrXe55XJDTfcoLCwsBJ79dRtKElff/21du/e7fDnUnL/NixpvxAREaHc3FydO3fObvy19ouFY8q6jDMIN2Xk5+enjh07KjU11TatoKBAqampdv/3+1uxsbF24yVp7dq1JY53J8MwNGbMGC1fvlxffvmlGjdu7HCN/Px8/fjjj6pbt245dOh658+f1/79+0vs15O2328tXLhQderU0R133OHQcp60/Ro3bqyIiAi77WO1WvXtt9+WuH2c+Qy7W2Gw2bt3r9atW6datWo5XONa7/PK5MiRIzp9+nSJvXriNiz01ltvqWPHjoqOjnZ4WXdtw2vtFzp27ChfX1+77bF7924dOnSoxO3hzGfX2eZRRosXLzb8/f2NRYsWGT///LMxatQoIzQ01LBYLIZhGMbDDz9sTJ482Tb+m2++MapUqWK89NJLxs6dO42kpCTD19fX+PHHH921CiV6/PHHjZCQEGPDhg3G8ePHbY+LFy/axvx+/f76178aX3zxhbF//34jPT3deOCBB4yAgADjp59+cscqXNOECROMDRs2GJmZmcY333xjxMXFGWFhYcaJEycMw/Ds7VcoPz/faNiwoTFp0qQi8zxt++Xk5Bjbtm0ztm3bZkgy5syZY2zbts32S6GZM2caoaGhxooVK4zt27cbd911l9G4cWPj0qVLtho9e/Y0XnnlFdvza32GK1pp65ibm2v079/faNCggfHDDz/YfS6vXLliq/H7dbzW+7yyrF9OTo7x1FNPGWlpaUZmZqaxbt06o0OHDkazZs2My5cvl7h+nrQNC2VnZxvVqlUzXn/99WJrVNZtWJb9wmOPPWY0bNjQ+PLLL43vv//eiI2NNWJjY+3qtGjRwvj4449tz8vy2b1ehBsHvfLKK0bDhg0NPz8/o0uXLsaWLVts87p3724MGTLEbvzSpUuN5s2bG35+fsaNN95orFq1qoI7LhtJxT4WLlxoG/P79Rs/frztbxEeHm707dvXyMjIqPjmy2jgwIFG3bp1DT8/P6N+/frGwIEDjX379tnme/L2K/TFF18Ykozdu3cXmedp22/9+vXFvicL16GgoMCYMmWKER4ebvj7+xu33XZbkfVu1KiRkZSUZDettM9wRSttHTMzM0v8XK5fv95W4/freK33eUUqbf0uXrxo9OrVy6hdu7bh6+trNGrUyBg5cmSRkOLJ27DQggULjKpVqxrnzp0rtkZl3YZl2S9cunTJ+J//+R+jRo0aRrVq1Yy7777bOH78eJE6v12mLJ/d6+X1/y8MAABgCpxzAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwA6BEBw4ckJeXl3744Qd3t6Jdu3bppptuUkBAgNq1a+fudlyqR48eGj9+vLvbAEyDcANUckOHDpWXl5dmzpxpN/2TTz6Rl5eXm7pync2bN6tv376qUaOGAgIC1KZNG82ZM0f5+fl245KSkhQYGKjdu3fbbrrn5eVlewQGBqpZs2YaOnSo0tPT3bEqlcrQoUM1YMAAd7cBuAXhBvAAAQEBmjVrls6ePevuVlwiNzdXkrR8+XJ1795dDRo00Pr167Vr1y6NGzdOzz//vB544AH99u4w+/fv1y233KJGjRrZ3R174cKFOn78uH766SelpKTo/PnziomJ0bvvvlvh6wWgciDcAB4gLi5OERERSk5OLnb+c889V+Srmrlz5yoqKsr2vPD/5GfMmKHw8HCFhoZq2rRpunr1qiZOnKiaNWuqQYMGWrhwYZH6u3btUteuXRUQEKDWrVtr48aNdvN37NihPn36KCgoSOHh4Xr44Yd16tQp2/wePXpozJgxGj9+vMLCwhQfH68LFy5o5MiR6t+/v9544w21a9dOUVFRGjFihN555x0tW7ZMS5culfTrEZr09HRNmzZNXl5eeu6552y1Q0NDFRERoaioKPXq1UvLli3TQw89pDFjxtiFwU2bNqlbt26qWrWqIiMjNXbsWF24cME2PyoqStOnT9egQYMUGBio+vXrKyUlxW49z507pxEjRqh27doKDg5Wz5499Z///KfIdnjvvfcUFRWlkJAQPfDAA8rJybGNuXDhggYPHqygoCDVrVtXL7/8cpG/95UrV/TUU0+pfv36CgwMVExMjDZs2GCbv2jRIoWGhuqLL75Qq1atFBQUpN69e+v48eO2Pt555x2tWLHCdmRrw4YNys3N1ZgxY1S3bl0FBASoUaNGJb6nAE9GuAE8gI+Pj2bMmKFXXnlFR44ccbrOl19+qWPHjumrr77SnDlzlJSUpDvvvFM1atTQt99+q8cee0yPPvpokdeYOHGiJkyYoG3btik2Nlb9+vXT6dOnJf26w+/Zs6fat2+v77//XmvWrFFWVpbuv/9+uxrvvPOO/Pz89M0332j+/Pn697//rdOnT+upp54q0me/fv3UvHlzffjhh5Kk48eP68Ybb9SECRN0/PjxYpf5rSeffFI5OTlau3atpF+P+vTu3Vv33nuvtm/friVLlmjTpk0aM2aM3XKzZ89WdHS0tm3bpsmTJ2vcuHG2GpL0l7/8RSdOnNDnn3+u9PR0dejQQbfddpvOnDljG7N//3598skn+uyzz/TZZ59p48aNdl8pTpw4URs3btSKFSv073//Wxs2bFBGRoZdH2PGjFFaWpoWL16s7du36y9/+Yt69+6tvXv32sZcvHhRL730kt577z199dVXOnTokO3v8tRTT+n++++3BZ7jx4+ra9eumjdvnlauXKmlS5dq9+7dev/99+0CMGAaLr3HOACXGzJkiHHXXXcZhmEYN910kzF8+HDDMAxj+fLlRuFHOCkpyYiOjrZb7m9/+5vRqFEjuzqNGjUy8vPzbdNatGhhdOvWzfb86tWrRmBgoPHhhx8ahmEYmZmZhiRj5syZtjF5eXlGgwYNjFmzZhmGYRjTp083evXqZffahw8fNiQZu3fvNgzDMLp37260b9/ebszMmTMNScbZs2eLXe/+/fsbrVq1sj2Pjo42kpKS7MZIMpYvX15k2UuXLhmSbD0+8sgjxqhRo+zGfP3114a3t7dx6dIlwzAMo1GjRkbv3r3txgwcONDo06ePbXxwcLBx+fJluzFNmjQxFixYYBjGr9uhWrVqhtVqtc2fOHGiERMTYxiGYeTk5Bh+fn7G0qVLbfNPnz5tVK1a1Rg3bpxhGIZx8OBBw8fHxzh69Kjd69x2221GYmKiYRiGsXDhQkOSsW/fPtv8lJQUIzw83Pb8t++bQk888YTRs2dPo6CgoMjfDDCTKm7MVQAcNGvWLPXs2fOaRy5KcuONN8rb+78HbMPDw9W6dWvbcx8fH9WqVUsnTpywWy42Ntb231WqVFGnTp20c+dOSdJ//vMfrV+/XkFBQUVeb//+/WrevLkkqWPHjsX2ZPzmvBpXKaxZeML1f/7zH23fvl3vv/++3ZiCggJlZmaqVatWkuzXs/D53LlzbTXOnz9vd76PJF26dEn79++3PY+KilL16tVtz+vWrWv7e+7fv1+5ubmKiYmxza9Zs6ZatGhhe/7jjz8qPz/f9ncrdOXKFbvXrlatmpo0aVLs65Rk6NChuv3229WiRQv17t1bd955p3r16lXqMoAnItwAHuTWW29VfHy8EhMTNXToUNt0b2/vIiEhLy+vyPK+vr52z728vIqdVlBQUOaezp8/r379+mnWrFlF5tWtW9f234GBgXbzCnfeO3fuVNeuXYssu3PnTv3pT38qcx+/X1aSGjdubOvx0Ucf1dixY4uMbdiwYZlqnj9/XnXr1rU796VQaGio7b9d8ff08fFRenq6fHx87Ob9NkAW9zrXCoodOnRQZmamPv/8c61bt07333+/4uLitGzZsjL3B3gCwg3gYWbOnKl27drZ/d9+7dq1ZbFYZBiG7WiFK69Ns2XLFt16662SpKtXryo9Pd12vkqHDh30r3/9S1FRUapSpez/pPTq1Us1a9bUyy+/XCTcrFy5Unv37tX06dOd6nfu3LkKDg5WXFycrceff/5ZTZs2LXW5LVu2FHleeFSnQ4cOslgsqlKlitPnqTRp0kS+vr769ttvbaHq7Nmz2rNnj7p37y5Jat++vfLz83XixAl169bNqdeRJD8/vyI/p5ek4OBgDRw4UAMHDtR9992n3r1768yZM6pZs6bTrwVUNpxQDHiYNm3a6KGHHtK8efNs03r06KGTJ0/qxRdf1P79+5WSkqLPP//cZa+ZkpKi5cuXa9euXRo9erTOnj2r4cOHS5JGjx6tM2fOaNCgQfruu++0f/9+ffHFFxo2bFixO9dCgYGBWrBggVasWKFRo0Zp+/btOnDggN566y0NHTpU9913X5GTkotz7tw5WSwWHTx4UGvXrtV9992nDz74QK+//rrtiMqkSZO0efNmjRkzRj/88IP27t2rFStWFDmh+JtvvtGLL76oPXv2KCUlRR999JHGjRsn6ddfrMXGxmrAgAH697//rQMHDmjz5s165pln9P3335fp7xgUFKRHHnlEEydO1JdffqkdO3Zo6NChdl8VNm/eXA899JAGDx6sjz/+WJmZmdq6dauSk5O1atWqMr2O9OvXY9u3b9fu3bt16tQp5eXlac6cOfrwww+1a9cu7dmzRx999JEiIiLsjjwBZkC4ATzQtGnT7L7qaNWqlV577TWlpKQoOjpaW7dudfq8nOLMnDlTM2fOVHR0tDZt2qSVK1cqLCxMklSvXj198803ys/PV69evdSmTRuNHz9eoaGhdjvt4tx3331av369Dh06pG7duqlFixb629/+pmeeeUaLFy8u00UKhw0bprp166ply5Z6/PHHFRQUpK1bt+rBBx+0jWnbtq02btyoPXv2qFu3bmrfvr2mTp2qevXq2dWaMGGCvv/+e7Vv317PP/+85syZo/j4eEm/fu2zevVq3XrrrRo2bJiaN2+uBx54QAcPHlR4eHiZ/5azZ89Wt27d1K9fP8XFxemWW24pcj7SwoULNXjwYE2YMEEtWrTQgAED9N1335X5KzRJGjlypFq0aKFOnTqpdu3a+uabb1S9enW9+OKL6tSpkzp37qwDBw5o9erV19xOgKfxMsrjbD4A8DBRUVEaP348t0EATIC4DgAATIVwAwAATIWvpQAAgKlw5AYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJjK/wFrLQLuU/xfXAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for column in Train_data.columns:\n",
    "  sns.scatterplot(data=Train_data,y=Train_data['SeriousDlqin2yrs'],x=column)\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 816
    },
    "id": "TLH4z_mxo_6Y",
    "outputId": "a8a6e41d-4cab-415d-dec1-0c644a5fdaee"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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svvnmG3Xo0EEvv/yyhg4dWjMnAgBcWHXNK4WFhZJ+/nIkSTk5OSorK1N4eLiZ6dSpk9q0aaPs7Gzdfvvtys7OVvfu3RUQEGBmbDabxo0bp4MHD6p3797Kzs522kdlZuLEiZKk0tJS5eTkaNq0aea4u7u7wsPDlZ2dXS3HCgCuoia/awAAUJOuqQlXKScnR59//rkkqWvXrurdu/d1KarSmTNndOedd+qee+7Rhx9+qJtuuklHjhwxb14qSXPmzNHChQv11ltvKSQkRM8++6xsNpsOHTpkXiIeFRWlkydPKjMzU2VlZRo9erRiY2PNv1Y5HA4NGjRI4eHhSk1N1f79+/X444/L19fXvER827ZtGjVqlJKSknTfffcpPT1dkZGR2rNnD5eIA8B1cj3nlYqKCk2cOFF33nmn+Tltt9vl6ekpX19fp2xAQIDsdruZOb8BVzleOXapjMPh0E8//aQzZ86ovLy8yszhw4cvWnNJSYlKSkrM1w6H4yqOGABcS3V/1wAAoKZdUxPu1KlTGjlypDZv3mx+kSkoKNA999yjFStW6Kabbrouxb388ssKDg52+mtYSEiI+W/DMLRgwQJNnz5dw4YNkyS9/fbbCggI0Jo1azRy5Eh9/vnnysjI0K5du8yr5xYtWqShQ4fq1VdfVVBQkNLS0lRaWqply5bJ09NTXbt2VW5urubNm2c24ZKTkzV48GBNnjxZkjR79mxlZmZq8eLFSk1NvS7HCwD1VXXMK3FxcTpw4IA+/fTT61xt9UlKStJzzz1X22UAQK2qqe8aAADUtGt6Our48eP1ww8/6ODBgzp9+rROnz6tAwcOyOFw6Mknn7xuxb3//vvq06ePRowYIX9/f/Xu3Vuvv/66OX706FHZ7XannwT5+PgoLCzM/LlPdna2fH19zQacJIWHh8vd3V07duwwM/3795enp6eZqbx59pkzZ8xMVT89utTPikpKSuRwOJwWAMCFrve8Eh8fr7Vr1+rjjz9W69atzfWBgYEqLS1VQUGBUz4/P1+BgYFm5pdPS618fbmMxWJRo0aN1LJlS3l4eFSZqdxHVaZNm6bCwkJzOX78+NUdOAC4gJr6rgEAQE27piZcRkaGXnvtNXXu3Nlc16VLF6WkpOjDDz+8bsV9/fXX5v3dNmzYoHHjxunJJ5/UW2+9Jen/fhZU1c99zv/J0C+fQtegQQP5+fldl58eVY5XJSkpST4+PuYSHBx8VccPAPXF9ZpXDMNQfHy8Vq9erU2bNjldPS1JoaGhatiwobKyssx1eXl5OnbsmKxWqyTJarVq//79OnXqlJnJzMyUxWJRly5dzMz5+6jMVO7D09NToaGhTpmKigplZWWZmap4eXnJYrE4LQBQ39TUdw0AAGraNf0ctaKiQg0bNrxgfcOGDVVRUfGrizr/ffr06aMXX3xRktS7d28dOHBAqampio6Ovm7vU12mTZumhIQE87XD4aARBwBVuF7zSlxcnNLT0/Xee++pWbNm5h9KfHx81KhRI/n4+CgmJkYJCQny8/OTxWLR+PHjZbVadfvtt0uSBg0apC5duuiRRx7RnDlzZLfbNX36dMXFxcnLy0uSNHbsWC1evFhTpkzR448/rk2bNundd9/VunXrzFoSEhIUHR2tPn36qG/fvlqwYIGKi4vNp6UCAKpWU981AACoadd0Jdy9996rCRMm6MSJE+a67777TpMmTdLAgQOvW3GtWrUyrzqo1LlzZx07dkzS//0s6FI/9wkMDHS6mkGSzp07p9OnT1+Xnx5d6mdFXNEAAFfmes0rS5YsUWFhoQYMGKBWrVqZy8qVK83M/Pnzdd9992n48OHq37+/AgMD9a9//csc9/Dw0Nq1a+Xh4SGr1ao//elPevTRRzVr1iwzExISonXr1ikzM1M9e/bU3Llz9cYbb8hms5mZhx56SK+++qoSExPVq1cv5ebmKiMj44KrqgEAzmrquwYAADXtmq6EW7x4sX7/+9+rXbt25pVdx48fV7du3fTOO+9ct+LuvPNO5eXlOa374osv1LZtW0k/fwkKDAxUVlaWevXqJennq8127NihcePGSfr5J0MFBQXKyclRaGioJGnTpk2qqKhQWFiYmfmf//kflZWVmX91y8zMVMeOHc0nsVb+9GjixIlmLef/9AgAcO2u17xiGMZlM97e3kpJSVFKSspFM23bttX69esvuZ8BAwZo7969l8zEx8crPj7+sjUBAP5PTX3XAACgpl1TEy44OFh79uzRRx99pMOHD0v6+Qq1Xz644NeaNGmS7rjjDr344ov64x//qJ07d2rp0qVaunSpJMnNzU0TJ07U888/rw4dOigkJETPPvusgoKCFBkZadY1ePBgjRkzRqmpqSorK1N8fLxGjhypoKAgSdLDDz+s5557TjExMZo6daoOHDig5ORkzZ8/36xlwoQJuvvuuzV37lxFRERoxYoV2r17t1kLAODa1dS8AgCo+5gTAACu6qp+jrpp0yZ16dJFDodDbm5u+t3vfqfx48dr/Pjxuu2229S1a1d98skn16242267TatXr9Y//vEPdevWTbNnz9aCBQsUFRVlZqZMmaLx48crNjZWt912m4qKipSRkSFvb28zk5aWpk6dOmngwIEaOnSo+vXr59Q88/Hx0caNG3X06FGFhobqqaeeUmJiomJjY83MHXfcofT0dC1dulQ9e/bUP//5T61Zs0bdunW7bscLAPVNTc8rAIC6izkBAODqrupKuAULFmjMmDFV3tvMx8dHf/7znzVv3jzddddd163A++67T/fdd99Fx93c3DRr1iyne/X8kp+fn9LT0y/5Pj169LjspD5ixAiNGDHi0gUDAK5YbcwrAIC6iTkBAODqrupKuH//+98aPHjwRccHDRqknJycX10UAKB+YF4BAFRiTgAAuLqrasLl5+dX+bjwSg0aNND333//q4sCANQPzCsAgErMCQAAV3dVTbibb75ZBw4cuOj4vn371KpVq19dFACgfmBeAQBUYk4AALi6q2rCDR06VM8++6zOnj17wdhPP/2kGTNmXPL+bQAAnI95BQBQiTkBAODqrurBDNOnT9e//vUv3XLLLYqPj1fHjh0lSYcPH1ZKSorKy8v1P//zP9VSKADA9TCvAAAqMScAAFzdVTXhAgICtG3bNo0bN07Tpk2TYRiSfn5Cqc1mU0pKigICAqqlUACA62FeAQBUYk4AALi6q2rCSVLbtm21fv16nTlzRl9++aUMw1CHDh3UvHnz6qgPAODimFcAAJWYEwAAruyqm3CVmjdvrttuu+161gIAqMeYVwAAlZgTAACu6KoezAAAAAAAAADg6tGEAwAAAAAAAKoZTTgAAAAAAACgml3zPeEAAABqytdff31VeV9fX56iCAAAgDqFJhwAAKizSooKJDc3DR069Kq2a9rMoi+PfEEjDgAAAHUGTTgAAFBnlZ39UTIM9Y19US1uDrmibYr/c0JbFz2lgoICmnAAAACoM2jCAQCAOq9JiyBZAtvWdhkAAADANePBDAAAAAAAAEA1owkHAAAAAAAAVDOacAAAAAAAAEA1owkHAAAAoN767rvv9Kc//UktWrRQo0aN1L17d+3evdscNwxDiYmJatWqlRo1aqTw8HAdOXLEaR+nT59WVFSULBaLfH19FRMTo6KiIqfMvn37dNddd8nb21vBwcGaM2fOBbWsWrVKnTp1kre3t7p3767169dXz0EDAGoFTTgAAAAA9dKZM2d05513qmHDhvrwww916NAhzZ07V82bNzczc+bM0cKFC5WamqodO3aoSZMmstlsOnv2rJmJiorSwYMHlZmZqbVr12rr1q2KjY01xx0OhwYNGqS2bdsqJydHr7zyimbOnKmlS5eamW3btmnUqFGKiYnR3r17FRkZqcjISB04cKBmTgYAoNrxdFQAAAAA9dLLL7+s4OBgvfnmm+a6kJAQ89+GYWjBggWaPn26hg0bJkl6++23FRAQoDVr1mjkyJH6/PPPlZGRoV27dqlPnz6SpEWLFmno0KF69dVXFRQUpLS0NJWWlmrZsmXy9PRU165dlZubq3nz5pnNuuTkZA0ePFiTJ0+WJM2ePVuZmZlavHixUlNTa+qUAACqEVfCAQAAAKiX3n//ffXp00cjRoyQv7+/evfurddff90cP3r0qOx2u8LDw811Pj4+CgsLU3Z2tiQpOztbvr6+ZgNOksLDw+Xu7q4dO3aYmf79+8vT09PM2Gw25eXl6cyZM2bm/PepzFS+DwDgxkcTDgAAAEC99PXXX2vJkiXq0KGDNmzYoHHjxunJJ5/UW2+9JUmy2+2SpICAAKftAgICzDG73S5/f3+n8QYNGsjPz88pU9U+zn+Pi2Uqx3+ppKREDofDaQEA1G38HBUAAABAvVRRUaE+ffroxRdflCT17t1bBw4cUGpqqqKjo2u5uktLSkrSc889V9tlAACuAlfCAQAAAKiXWrVqpS5dujit69y5s44dOyZJCgwMlCTl5+c7ZfLz882xwMBAnTp1ymn83LlzOn36tFOmqn2c/x4Xy1SO/9K0adNUWFhoLsePH7+ygwYA1BqacAAAl7F161bdf//9CgoKkpubm9asWeM0/thjj8nNzc1pGTx4sFPm9OnTioqKksVika+vr2JiYlRUVOSU2bdvn+666y55e3srODhYc+bMuaCWVatWqVOnTvL29lb37t21fv366368AIBf584771ReXp7Tui+++EJt27aV9PNDGgIDA5WVlWWOOxwO7dixQ1arVZJktVpVUFCgnJwcM7Np0yZVVFQoLCzMzGzdulVlZWVmJjMzUx07djSfxGq1Wp3epzJT+T6/5OXlJYvF4rQAAOo2mnAAAJdRXFysnj17KiUl5aKZwYMH6+TJk+byj3/8w2k8KipKBw8eVGZmptauXautW7eaT66Tfv7yNWjQILVt21Y5OTl65ZVXNHPmTC1dutTMbNu2TaNGjVJMTIz27t2ryMhIRUZG6sCBA9f/oAEA12zSpEnavn27XnzxRX355ZdKT0/X0qVLFRcXJ0lyc3PTxIkT9fzzz+v999/X/v379eijjyooKEiRkZGSfr5ybvDgwRozZox27typzz77TPHx8Ro5cqSCgoIkSQ8//LA8PT0VExOjgwcPauXKlUpOTlZCQoJZy4QJE5SRkaG5c+fq8OHDmjlzpnbv3q34+PgaPy8AgOrBPeEAAC5jyJAhGjJkyCUzXl5eF/1pz+eff66MjAzt2rXLfMrdokWLNHToUL366qsKCgpSWlqaSktLtWzZMnl6eqpr167Kzc3VvHnzzGZdcnKyBg8erMmTJ0uSZs+erczMTC1evFipqanX8YgBAL/GbbfdptWrV2vatGmaNWuWQkJCtGDBAkVFRZmZKVOmqLi4WLGxsSooKFC/fv2UkZEhb29vM5OWlqb4+HgNHDhQ7u7uGj58uBYuXGiO+/j4aOPGjYqLi1NoaKhatmypxMREpz/y3HHHHUpPT9f06dP1zDPPqEOHDlqzZo26detWMycDAFDtaMIBAOqVzZs3y9/fX82bN9e9996r559/Xi1atJAkZWdny9fX12zASVJ4eLjc3d21Y8cO/eEPf1B2drb69+8vT09PM2Oz2fTyyy/rzJkzat68ubKzs52ubqjM/PLnsb9UUlKikpIS8zVPugOA6nfffffpvvvuu+i4m5ubZs2apVmzZl004+fnp/T09Eu+T48ePfTJJ59cMjNixAiNGDHi0gUDAG5Y/BwVAFBvDB48WG+//baysrL08ssva8uWLRoyZIjKy8slSXa7Xf7+/k7bNGjQQH5+frLb7WYmICDAKVP5+nKZyvGLSUpKko+Pj7kEBwdf+8ECAAAAqFO4Eg4AUG+MHDnS/Hf37t3Vo0cP/fa3v9XmzZs1cODAWqzsZ9OmTXO6gs7hcNCIAwAAAFwEV8IBAOqt3/zmN2rZsqW+/PJLSVJgYKBOnTrllDl37pxOnz5t3kcuMDBQ+fn5TpnK15fLXOxedJV40h0AAADgumjCAQDqrf/93//Vf//7X7Vq1UqSZLVaVVBQoJycHDOzadMmVVRUKCwszMxs3bpVZWVlZiYzM1MdO3ZU8+bNzUxWVpbTe2VmZspqtVb3IQEAAACoo2jCAQBcRlFRkXJzc5WbmytJOnr0qHJzc3Xs2DEVFRVp8uTJ2r59u7755htlZWVp2LBhat++vWw2mySpc+fOGjx4sMaMGaOdO3fqs88+U3x8vEaOHKmgoCBJ0sMPPyxPT0/FxMTo4MGDWrlypZKTk51+RjphwgRlZGRo7ty5Onz4sGbOnKndu3crPj6+xs8JAAAAgLqBJhwAwGXs3r1bvXv3Vu/evSVJCQkJ6t27txITE+Xh4aF9+/bp97//vW655RbFxMQoNDRUn3zyiby8vMx9pKWlqVOnTho4cKCGDh2qfv36aenSpea4j4+PNm7cqKNHjyo0NFRPPfWUEhMTFRsba2buuOMOpaena+nSperZs6f++c9/as2aNerWrVvNnQwAAAAAdQoPZgAAuIwBAwbIMIyLjm/YsOGy+/Dz81N6evolMz169NAnn3xyycyIESM0YsSIy74fAAAAgPqBK+EAAAAAAACAakYTDgAAAAAAAKhmNOEAAAAAAACAanZDNeFeeuklubm5aeLEiea6s2fPKi4uTi1atFDTpk01fPhw5efnO2137NgxRUREqHHjxvL399fkyZN17tw5p8zmzZt16623ysvLS+3bt9fy5csveP+UlBS1a9dO3t7eCgsL086dO6vjMAEAAAAAAOBibpgm3K5du/S3v/1NPXr0cFo/adIkffDBB1q1apW2bNmiEydO6IEHHjDHy8vLFRERodLSUm3btk1vvfWWli9frsTERDNz9OhRRURE6J577lFubq4mTpyoJ554wukG3itXrlRCQoJmzJihPXv2qGfPnrLZbDp16lT1HzwAAAAAAABuaDdEE66oqEhRUVF6/fXX1bx5c3N9YWGh/v73v2vevHm69957FRoaqjfffFPbtm3T9u3bJUkbN27UoUOH9M4776hXr14aMmSIZs+erZSUFJWWlkqSUlNTFRISorlz56pz586Kj4/Xgw8+qPnz55vvNW/ePI0ZM0ajR49Wly5dlJqaqsaNG2vZsmU1ezIAAAAAAABww7khmnBxcXGKiIhQeHi40/qcnByVlZU5re/UqZPatGmj7OxsSVJ2dra6d++ugIAAM2Oz2eRwOHTw4EEz88t922w2cx+lpaXKyclxyri7uys8PNzMVKWkpEQOh8NpAQAAAAAAQP3ToLYLuJwVK1Zoz5492rVr1wVjdrtdnp6e8vX1dVofEBAgu91uZs5vwFWOV45dKuNwOPTTTz/pzJkzKi8vrzJz+PDhi9aelJSk55577soOFACA6yQ/P18FBQVXnP/666+rrxgAAAAAkup4E+748eOaMGGCMjMz5e3tXdvlXLVp06YpISHBfO1wOBQcHFyLFQEAXF1+fr7ad7hFRT9c/dXXZWXnLh8CAAAAcE3qdBMuJydHp06d0q233mquKy8v19atW7V48WJt2LBBpaWlKigocLoaLj8/X4GBgZKkwMDAC55iWvn01PMzv3yian5+viwWixo1aiQPDw95eHhUmancR1W8vLzk5eV19QcOAMA1KigoUNEPDvUfP1dNWgZd0TbfH8nV7ndeVnl5eTVXBwAAANRfdfqecAMHDtT+/fuVm5trLn369FFUVJT574YNGyorK8vcJi8vT8eOHZPVapUkWa1W7d+/3+kpppmZmbJYLOrSpYuZOX8flZnKfXh6eio0NNQpU1FRoaysLDMDAEBd0qRlkCyBba9oadTcv7bLBQAAAFxenb4SrlmzZurWrZvTuiZNmqhFixbm+piYGCUkJMjPz08Wi0Xjx4+X1WrV7bffLkkaNGiQunTpokceeURz5syR3W7X9OnTFRcXZ16lNnbsWC1evFhTpkzR448/rk2bNundd9/VunXrzPdNSEhQdHS0+vTpo759+2rBggUqLi7W6NGja+hsAAAAAAAA4EZVp5twV2L+/Plyd3fX8OHDVVJSIpvNptdee80c9/Dw0Nq1azVu3DhZrVY1adJE0dHRmjVrlpkJCQnRunXrNGnSJCUnJ6t169Z64403ZLPZzMxDDz2k77//XomJibLb7erVq5cyMjIueFgDAAAAAAAA8Es3XBNu8+bNTq+9vb2VkpKilJSUi27Ttm1brV+//pL7HTBggPbu3XvJTHx8vOLj46+4VgAAAAAAAECq4/eEAwAAAAAAAFwBTTgAAAAAAACgmtGEAwAAAAAAAKoZTTgAAAAAAACgmtGEAwAAAAAAAKoZTTgAAAAAAACgmtGEAwAAAAAAAKoZTTgAAAAAAACgmtGEAwAAAFDvvfTSS3Jzc9PEiRPNdWfPnlVcXJxatGihpk2bavjw4crPz3fa7tixY4qIiFDjxo3l7++vyZMn69y5c06ZzZs369Zbb5WXl5fat2+v5cuXX/D+KSkpateunby9vRUWFqadO3dWx2ECAGoRTTgAAAAA9dquXbv0t7/9TT169HBaP2nSJH3wwQdatWqVtmzZohMnTuiBBx4wx8vLyxUREaHS0lJt27ZNb731lpYvX67ExEQzc/ToUUVEROiee+5Rbm6uJk6cqCeeeEIbNmwwMytXrlRCQoJmzJihPXv2qGfPnrLZbDp16lT1HzwAoMbQhAMAAABQbxUVFSkqKkqvv/66mjdvbq4vLCzU3//+d82bN0/33nuvQkND9eabb2rbtm3avn27JGnjxo06dOiQ3nnnHfXq1UtDhgzR7NmzlZKSotLSUklSamqqQkJCNHfuXHXu3Fnx8fF68MEHNX/+fPO95s2bpzFjxmj06NHq0qWLUlNT1bhxYy1btqxmTwYAoFrRhAMAAABQb8XFxSkiIkLh4eFO63NyclRWVua0vlOnTmrTpo2ys7MlSdnZ2erevbsCAgLMjM1mk8Ph0MGDB83ML/dts9nMfZSWlionJ8cp4+7urvDwcDMDAHANDWq7AAAAAACoDStWrNCePXu0a9euC8bsdrs8PT3l6+vrtD4gIEB2u93MnN+AqxyvHLtUxuFw6KefftKZM2dUXl5eZebw4cMXrb2kpEQlJSXma4fDcZmjBQDUNq6EAwC4jK1bt+r+++9XUFCQ3NzctGbNGqdxwzCUmJioVq1aqVGjRgoPD9eRI0ecMqdPn1ZUVJQsFot8fX0VExOjoqIip8y+fft01113ydvbW8HBwZozZ84FtaxatUqdOnWSt7e3unfvrvXr11/34wUAXLvjx49rwoQJSktLk7e3d22Xc9WSkpLk4+NjLsHBwbVdEgDgMmjCAQBcRnFxsXr27KmUlJQqx+fMmaOFCxcqNTVVO3bsUJMmTWSz2XT27FkzExUVpYMHDyozM1Nr167V1q1bFRsba447HA4NGjRIbdu2VU5Ojl555RXNnDlTS5cuNTPbtm3TqFGjFBMTo7179yoyMlKRkZE6cOBA9R08AOCq5OTk6NSpU7r11lvVoEEDNWjQQFu2bNHChQvVoEEDBQQEqLS0VAUFBU7b5efnKzAwUJIUGBh4wdNSK19fLmOxWNSoUSO1bNlSHh4eVWYq91GVadOmqbCw0FyOHz9+TecBAFBzaMIBAFzGkCFD9Pzzz+sPf/jDBWOGYWjBggWaPn26hg0bph49eujtt9/WiRMnzCvmPv/8c2VkZOiNN95QWFiY+vXrp0WLFmnFihU6ceKEJCktLU2lpaVatmyZunbtqpEjR+rJJ5/UvHnzzPdKTk7W4MGDNXnyZHXu3FmzZ8/WrbfeqsWLF9fIeQAAXN7AgQO1f/9+5ebmmkufPn0UFRVl/rthw4bKysoyt8nLy9OxY8dktVolSVarVfv373d6imlmZqYsFou6dOliZs7fR2Wmch+enp4KDQ11ylRUVCgrK8vMVMXLy0sWi8VpAQDUbTThAAD1wtGjR2W3251ufO3j46OwsDCnG2z7+vqqT58+ZiY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",
      "text/plain": [
       "<Figure size 1200x1600 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,16))\n",
    "for i, j in enumerate(Train_data.describe().columns):\n",
    "    plt.subplot(4,3, i+1)\n",
    "    sns.histplot(x=Train_data[j],bins=20)\n",
    "    plt.xlabel(j)\n",
    "    plt.title('{} Distribution'.format(j))\n",
    "    # plt.subplots_adjust(wspace=.2, hspace=.5)\n",
    "    plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 644
    },
    "id": "NrvnxVm1wjtl",
    "outputId": "3e0f6b49-866d-4a7b-de30-4cbb152126bc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "for i,j in enumerate(Train_data.describe().columns):\n",
    "  plt.subplot(4,3, i+1)\n",
    "  sns.boxplot(x=Train_data[j])\n",
    "  plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "id": "zx0DcOlExeqC",
    "outputId": "690bb6cb-666a-445b-ae44-7c6017d96d6e"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
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       "      <th>Unnamed: 0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002801</td>\n",
       "      <td>0.002372</td>\n",
       "      <td>0.004403</td>\n",
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       "      <td>-0.000666</td>\n",
       "      <td>-0.000777</td>\n",
       "      <td>-0.000055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <td>0.002801</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001802</td>\n",
       "      <td>-0.115386</td>\n",
       "      <td>0.125587</td>\n",
       "      <td>-0.007602</td>\n",
       "      <td>-0.019746</td>\n",
       "      <td>-0.029669</td>\n",
       "      <td>0.117175</td>\n",
       "      <td>-0.007038</td>\n",
       "      <td>0.102261</td>\n",
       "      <td>0.046048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <td>0.002372</td>\n",
       "      <td>-0.001802</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005898</td>\n",
       "      <td>-0.001314</td>\n",
       "      <td>0.003961</td>\n",
       "      <td>0.007124</td>\n",
       "      <td>-0.011281</td>\n",
       "      <td>-0.001061</td>\n",
       "      <td>0.006235</td>\n",
       "      <td>-0.001048</td>\n",
       "      <td>0.001557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.004403</td>\n",
       "      <td>-0.115386</td>\n",
       "      <td>-0.005898</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.062995</td>\n",
       "      <td>0.024188</td>\n",
       "      <td>0.037717</td>\n",
       "      <td>0.147705</td>\n",
       "      <td>-0.061005</td>\n",
       "      <td>0.033150</td>\n",
       "      <td>-0.057159</td>\n",
       "      <td>-0.213303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <td>-0.000571</td>\n",
       "      <td>0.125587</td>\n",
       "      <td>-0.001314</td>\n",
       "      <td>-0.062995</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.006542</td>\n",
       "      <td>-0.010217</td>\n",
       "      <td>-0.055312</td>\n",
       "      <td>0.983603</td>\n",
       "      <td>-0.030565</td>\n",
       "      <td>0.987005</td>\n",
       "      <td>-0.002680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DebtRatio</th>\n",
       "      <td>-0.002906</td>\n",
       "      <td>-0.007602</td>\n",
       "      <td>0.003961</td>\n",
       "      <td>0.024188</td>\n",
       "      <td>-0.006542</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.028712</td>\n",
       "      <td>0.049565</td>\n",
       "      <td>-0.008320</td>\n",
       "      <td>0.120046</td>\n",
       "      <td>-0.007533</td>\n",
       "      <td>-0.040673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <td>0.002632</td>\n",
       "      <td>-0.019746</td>\n",
       "      <td>0.007124</td>\n",
       "      <td>0.037717</td>\n",
       "      <td>-0.010217</td>\n",
       "      <td>-0.028712</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.091455</td>\n",
       "      <td>-0.012743</td>\n",
       "      <td>0.124959</td>\n",
       "      <td>-0.011116</td>\n",
       "      <td>0.062647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <td>0.004586</td>\n",
       "      <td>-0.029669</td>\n",
       "      <td>-0.011281</td>\n",
       "      <td>0.147705</td>\n",
       "      <td>-0.055312</td>\n",
       "      <td>0.049565</td>\n",
       "      <td>0.091455</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.079984</td>\n",
       "      <td>0.433959</td>\n",
       "      <td>-0.071077</td>\n",
       "      <td>0.065322</td>\n",
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       "    <tr>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <td>-0.001104</td>\n",
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       "      <td>-0.001061</td>\n",
       "      <td>-0.061005</td>\n",
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       "      <td>-0.008320</td>\n",
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       "      <td>-0.079984</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.045205</td>\n",
       "      <td>0.992796</td>\n",
       "      <td>-0.010176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <td>-0.000666</td>\n",
       "      <td>-0.007038</td>\n",
       "      <td>0.006235</td>\n",
       "      <td>0.033150</td>\n",
       "      <td>-0.030565</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>-0.039722</td>\n",
       "      <td>0.124684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <td>-0.000777</td>\n",
       "      <td>0.102261</td>\n",
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       "      <td>-0.011116</td>\n",
       "      <td>-0.071077</td>\n",
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       "      <td>-0.039722</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010922</td>\n",
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       "    <tr>\n",
       "      <th>NumberOfDependents</th>\n",
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       "      <td>-0.213303</td>\n",
       "      <td>-0.002680</td>\n",
       "      <td>-0.040673</td>\n",
       "      <td>0.062647</td>\n",
       "      <td>0.065322</td>\n",
       "      <td>-0.010176</td>\n",
       "      <td>0.124684</td>\n",
       "      <td>-0.010922</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Unnamed: 0  SeriousDlqin2yrs  \\\n",
       "Unnamed: 0                              1.000000          0.002801   \n",
       "SeriousDlqin2yrs                        0.002801          1.000000   \n",
       "RevolvingUtilizationOfUnsecuredLines    0.002372         -0.001802   \n",
       "age                                     0.004403         -0.115386   \n",
       "NumberOfTime30-59DaysPastDueNotWorse   -0.000571          0.125587   \n",
       "DebtRatio                              -0.002906         -0.007602   \n",
       "MonthlyIncome                           0.002632         -0.019746   \n",
       "NumberOfOpenCreditLinesAndLoans         0.004586         -0.029669   \n",
       "NumberOfTimes90DaysLate                -0.001104          0.117175   \n",
       "NumberRealEstateLoansOrLines           -0.000666         -0.007038   \n",
       "NumberOfTime60-89DaysPastDueNotWorse   -0.000777          0.102261   \n",
       "NumberOfDependents                     -0.000055          0.046048   \n",
       "\n",
       "                                      RevolvingUtilizationOfUnsecuredLines  \\\n",
       "Unnamed: 0                                                        0.002372   \n",
       "SeriousDlqin2yrs                                                 -0.001802   \n",
       "RevolvingUtilizationOfUnsecuredLines                              1.000000   \n",
       "age                                                              -0.005898   \n",
       "NumberOfTime30-59DaysPastDueNotWorse                             -0.001314   \n",
       "DebtRatio                                                         0.003961   \n",
       "MonthlyIncome                                                     0.007124   \n",
       "NumberOfOpenCreditLinesAndLoans                                  -0.011281   \n",
       "NumberOfTimes90DaysLate                                          -0.001061   \n",
       "NumberRealEstateLoansOrLines                                      0.006235   \n",
       "NumberOfTime60-89DaysPastDueNotWorse                             -0.001048   \n",
       "NumberOfDependents                                                0.001557   \n",
       "\n",
       "                                           age  \\\n",
       "Unnamed: 0                            0.004403   \n",
       "SeriousDlqin2yrs                     -0.115386   \n",
       "RevolvingUtilizationOfUnsecuredLines -0.005898   \n",
       "age                                   1.000000   \n",
       "NumberOfTime30-59DaysPastDueNotWorse -0.062995   \n",
       "DebtRatio                             0.024188   \n",
       "MonthlyIncome                         0.037717   \n",
       "NumberOfOpenCreditLinesAndLoans       0.147705   \n",
       "NumberOfTimes90DaysLate              -0.061005   \n",
       "NumberRealEstateLoansOrLines          0.033150   \n",
       "NumberOfTime60-89DaysPastDueNotWorse -0.057159   \n",
       "NumberOfDependents                   -0.213303   \n",
       "\n",
       "                                      NumberOfTime30-59DaysPastDueNotWorse  \\\n",
       "Unnamed: 0                                                       -0.000571   \n",
       "SeriousDlqin2yrs                                                  0.125587   \n",
       "RevolvingUtilizationOfUnsecuredLines                             -0.001314   \n",
       "age                                                              -0.062995   \n",
       "NumberOfTime30-59DaysPastDueNotWorse                              1.000000   \n",
       "DebtRatio                                                        -0.006542   \n",
       "MonthlyIncome                                                    -0.010217   \n",
       "NumberOfOpenCreditLinesAndLoans                                  -0.055312   \n",
       "NumberOfTimes90DaysLate                                           0.983603   \n",
       "NumberRealEstateLoansOrLines                                     -0.030565   \n",
       "NumberOfTime60-89DaysPastDueNotWorse                              0.987005   \n",
       "NumberOfDependents                                               -0.002680   \n",
       "\n",
       "                                      DebtRatio  MonthlyIncome  \\\n",
       "Unnamed: 0                            -0.002906       0.002632   \n",
       "SeriousDlqin2yrs                      -0.007602      -0.019746   \n",
       "RevolvingUtilizationOfUnsecuredLines   0.003961       0.007124   \n",
       "age                                    0.024188       0.037717   \n",
       "NumberOfTime30-59DaysPastDueNotWorse  -0.006542      -0.010217   \n",
       "DebtRatio                              1.000000      -0.028712   \n",
       "MonthlyIncome                         -0.028712       1.000000   \n",
       "NumberOfOpenCreditLinesAndLoans        0.049565       0.091455   \n",
       "NumberOfTimes90DaysLate               -0.008320      -0.012743   \n",
       "NumberRealEstateLoansOrLines           0.120046       0.124959   \n",
       "NumberOfTime60-89DaysPastDueNotWorse  -0.007533      -0.011116   \n",
       "NumberOfDependents                    -0.040673       0.062647   \n",
       "\n",
       "                                      NumberOfOpenCreditLinesAndLoans  \\\n",
       "Unnamed: 0                                                   0.004586   \n",
       "SeriousDlqin2yrs                                            -0.029669   \n",
       "RevolvingUtilizationOfUnsecuredLines                        -0.011281   \n",
       "age                                                          0.147705   \n",
       "NumberOfTime30-59DaysPastDueNotWorse                        -0.055312   \n",
       "DebtRatio                                                    0.049565   \n",
       "MonthlyIncome                                                0.091455   \n",
       "NumberOfOpenCreditLinesAndLoans                              1.000000   \n",
       "NumberOfTimes90DaysLate                                     -0.079984   \n",
       "NumberRealEstateLoansOrLines                                 0.433959   \n",
       "NumberOfTime60-89DaysPastDueNotWorse                        -0.071077   \n",
       "NumberOfDependents                                           0.065322   \n",
       "\n",
       "                                      NumberOfTimes90DaysLate  \\\n",
       "Unnamed: 0                                          -0.001104   \n",
       "SeriousDlqin2yrs                                     0.117175   \n",
       "RevolvingUtilizationOfUnsecuredLines                -0.001061   \n",
       "age                                                 -0.061005   \n",
       "NumberOfTime30-59DaysPastDueNotWorse                 0.983603   \n",
       "DebtRatio                                           -0.008320   \n",
       "MonthlyIncome                                       -0.012743   \n",
       "NumberOfOpenCreditLinesAndLoans                     -0.079984   \n",
       "NumberOfTimes90DaysLate                              1.000000   \n",
       "NumberRealEstateLoansOrLines                        -0.045205   \n",
       "NumberOfTime60-89DaysPastDueNotWorse                 0.992796   \n",
       "NumberOfDependents                                  -0.010176   \n",
       "\n",
       "                                      NumberRealEstateLoansOrLines  \\\n",
       "Unnamed: 0                                               -0.000666   \n",
       "SeriousDlqin2yrs                                         -0.007038   \n",
       "RevolvingUtilizationOfUnsecuredLines                      0.006235   \n",
       "age                                                       0.033150   \n",
       "NumberOfTime30-59DaysPastDueNotWorse                     -0.030565   \n",
       "DebtRatio                                                 0.120046   \n",
       "MonthlyIncome                                             0.124959   \n",
       "NumberOfOpenCreditLinesAndLoans                           0.433959   \n",
       "NumberOfTimes90DaysLate                                  -0.045205   \n",
       "NumberRealEstateLoansOrLines                              1.000000   \n",
       "NumberOfTime60-89DaysPastDueNotWorse                     -0.039722   \n",
       "NumberOfDependents                                        0.124684   \n",
       "\n",
       "                                      NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "Unnamed: 0                                                       -0.000777   \n",
       "SeriousDlqin2yrs                                                  0.102261   \n",
       "RevolvingUtilizationOfUnsecuredLines                             -0.001048   \n",
       "age                                                              -0.057159   \n",
       "NumberOfTime30-59DaysPastDueNotWorse                              0.987005   \n",
       "DebtRatio                                                        -0.007533   \n",
       "MonthlyIncome                                                    -0.011116   \n",
       "NumberOfOpenCreditLinesAndLoans                                  -0.071077   \n",
       "NumberOfTimes90DaysLate                                           0.992796   \n",
       "NumberRealEstateLoansOrLines                                     -0.039722   \n",
       "NumberOfTime60-89DaysPastDueNotWorse                              1.000000   \n",
       "NumberOfDependents                                               -0.010922   \n",
       "\n",
       "                                      NumberOfDependents  \n",
       "Unnamed: 0                                     -0.000055  \n",
       "SeriousDlqin2yrs                                0.046048  \n",
       "RevolvingUtilizationOfUnsecuredLines            0.001557  \n",
       "age                                            -0.213303  \n",
       "NumberOfTime30-59DaysPastDueNotWorse           -0.002680  \n",
       "DebtRatio                                      -0.040673  \n",
       "MonthlyIncome                                   0.062647  \n",
       "NumberOfOpenCreditLinesAndLoans                 0.065322  \n",
       "NumberOfTimes90DaysLate                        -0.010176  \n",
       "NumberRealEstateLoansOrLines                    0.124684  \n",
       "NumberOfTime60-89DaysPastDueNotWorse           -0.010922  \n",
       "NumberOfDependents                              1.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 579
    },
    "id": "TfsYmEtlMOHk",
    "outputId": "6b447d86-205b-4ca6-b656-57d8f9cbbee2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(Train_data.corr())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "ISk_Cisryatd"
   },
   "source": [
    "We can see a strong correlation between ( NumberOfTime30-59DaysPastDueNotWorse, NumberOfTime60-89DaysPastDueNotWorse\t, NumberOfTimes90DaysLate\t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0hAiL3UTy910",
    "outputId": "ecfd8085-2418-44f7-fa9e-986f4683bb3a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9870054474799452\n",
      "0.992796182591599\n",
      "0.9836026812820752\n"
     ]
    }
   ],
   "source": [
    "print(Train_data['NumberOfTime60-89DaysPastDueNotWorse'].corr(Train_data['NumberOfTime30-59DaysPastDueNotWorse']))\n",
    "print(Train_data['NumberOfTime60-89DaysPastDueNotWorse'].corr(Train_data['NumberOfTimes90DaysLate']))\n",
    "print(Train_data['NumberOfTimes90DaysLate'].corr(Train_data['NumberOfTime30-59DaysPastDueNotWorse']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "0DZu4U6MRT8i"
   },
   "outputs": [],
   "source": [
    "#Delete row where age=0 (outlier)\n",
    "index=Train_data[Train_data['age']==0].index\n",
    "Train_data.drop(index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "Oe9rINvs1oFn"
   },
   "outputs": [],
   "source": [
    "Train_data.drop(columns=['Unnamed: 0'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "bVkLF48Z2czq"
   },
   "outputs": [],
   "source": [
    "Test_data.drop(columns=['Unnamed: 0'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 505
    },
    "id": "WTee4X5i2JsZ",
    "outputId": "42513b9f-a0b1-4df8-bcc6-2d8c791c3364"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.766127</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>0.802982</td>\n",
       "      <td>9120.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.957151</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.121876</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.658180</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0.085113</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.233810</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.036050</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.907239</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0.024926</td>\n",
       "      <td>63588.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149995</th>\n",
       "      <td>0</td>\n",
       "      <td>0.040674</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0.225131</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149996</th>\n",
       "      <td>0</td>\n",
       "      <td>0.299745</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.716562</td>\n",
       "      <td>5584.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149997</th>\n",
       "      <td>0</td>\n",
       "      <td>0.246044</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>3870.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149998</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5716.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149999</th>\n",
       "      <td>0</td>\n",
       "      <td>0.850283</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.249908</td>\n",
       "      <td>8158.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149999 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "0                      1                              0.766127   45   \n",
       "1                      0                              0.957151   40   \n",
       "2                      0                              0.658180   38   \n",
       "3                      0                              0.233810   30   \n",
       "4                      0                              0.907239   49   \n",
       "...                  ...                                   ...  ...   \n",
       "149995                 0                              0.040674   74   \n",
       "149996                 0                              0.299745   44   \n",
       "149997                 0                              0.246044   58   \n",
       "149998                 0                              0.000000   30   \n",
       "149999                 0                              0.850283   64   \n",
       "\n",
       "        NumberOfTime30-59DaysPastDueNotWorse    DebtRatio  MonthlyIncome  \\\n",
       "0                                          2     0.802982         9120.0   \n",
       "1                                          0     0.121876         2600.0   \n",
       "2                                          1     0.085113         3042.0   \n",
       "3                                          0     0.036050         3300.0   \n",
       "4                                          1     0.024926        63588.0   \n",
       "...                                      ...          ...            ...   \n",
       "149995                                     0     0.225131         2100.0   \n",
       "149996                                     0     0.716562         5584.0   \n",
       "149997                                     0  3870.000000            NaN   \n",
       "149998                                     0     0.000000         5716.0   \n",
       "149999                                     0     0.249908         8158.0   \n",
       "\n",
       "        NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                                    13                        0   \n",
       "1                                     4                        0   \n",
       "2                                     2                        1   \n",
       "3                                     5                        0   \n",
       "4                                     7                        0   \n",
       "...                                 ...                      ...   \n",
       "149995                                4                        0   \n",
       "149996                                4                        0   \n",
       "149997                               18                        0   \n",
       "149998                                4                        0   \n",
       "149999                                8                        0   \n",
       "\n",
       "        NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "0                                  6                                     0   \n",
       "1                                  0                                     0   \n",
       "2                                  0                                     0   \n",
       "3                                  0                                     0   \n",
       "4                                  1                                     0   \n",
       "...                              ...                                   ...   \n",
       "149995                             1                                     0   \n",
       "149996                             1                                     0   \n",
       "149997                             1                                     0   \n",
       "149998                             0                                     0   \n",
       "149999                             2                                     0   \n",
       "\n",
       "        NumberOfDependents  \n",
       "0                      2.0  \n",
       "1                      1.0  \n",
       "2                      0.0  \n",
       "3                      0.0  \n",
       "4                      0.0  \n",
       "...                    ...  \n",
       "149995                 0.0  \n",
       "149996                 2.0  \n",
       "149997                 0.0  \n",
       "149998                 0.0  \n",
       "149999                 0.0  \n",
       "\n",
       "[149999 rows x 11 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 382
    },
    "id": "s6RLXDBfvFAK",
    "outputId": "1239f3be-c131-4963-819b-bf3237d168fa"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>149999.000000</td>\n",
       "      <td>149999.000000</td>\n",
       "      <td>149999.000000</td>\n",
       "      <td>149999.000000</td>\n",
       "      <td>149999.000000</td>\n",
       "      <td>1.202680e+05</td>\n",
       "      <td>149999.000000</td>\n",
       "      <td>149999.000000</td>\n",
       "      <td>149999.000000</td>\n",
       "      <td>149999.000000</td>\n",
       "      <td>146075.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.066840</td>\n",
       "      <td>6.048472</td>\n",
       "      <td>52.295555</td>\n",
       "      <td>0.421029</td>\n",
       "      <td>353.007426</td>\n",
       "      <td>6.670227e+03</td>\n",
       "      <td>8.452776</td>\n",
       "      <td>0.265975</td>\n",
       "      <td>1.018233</td>\n",
       "      <td>0.240388</td>\n",
       "      <td>0.757214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.249746</td>\n",
       "      <td>249.756203</td>\n",
       "      <td>14.771298</td>\n",
       "      <td>4.192795</td>\n",
       "      <td>2037.825113</td>\n",
       "      <td>1.438473e+04</td>\n",
       "      <td>5.145964</td>\n",
       "      <td>4.169318</td>\n",
       "      <td>1.129772</td>\n",
       "      <td>4.155193</td>\n",
       "      <td>1.115085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029867</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.175074</td>\n",
       "      <td>3.400000e+03</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.154176</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.366503</td>\n",
       "      <td>5.400000e+03</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.559044</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.868257</td>\n",
       "      <td>8.249000e+03</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>50708.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>329664.000000</td>\n",
       "      <td>3.008750e+06</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines            age  \\\n",
       "count     149999.000000                         149999.000000  149999.000000   \n",
       "mean           0.066840                              6.048472      52.295555   \n",
       "std            0.249746                            249.756203      14.771298   \n",
       "min            0.000000                              0.000000      21.000000   \n",
       "25%            0.000000                              0.029867      41.000000   \n",
       "50%            0.000000                              0.154176      52.000000   \n",
       "75%            0.000000                              0.559044      63.000000   \n",
       "max            1.000000                          50708.000000     109.000000   \n",
       "\n",
       "       NumberOfTime30-59DaysPastDueNotWorse      DebtRatio  MonthlyIncome  \\\n",
       "count                         149999.000000  149999.000000   1.202680e+05   \n",
       "mean                               0.421029     353.007426   6.670227e+03   \n",
       "std                                4.192795    2037.825113   1.438473e+04   \n",
       "min                                0.000000       0.000000   0.000000e+00   \n",
       "25%                                0.000000       0.175074   3.400000e+03   \n",
       "50%                                0.000000       0.366503   5.400000e+03   \n",
       "75%                                0.000000       0.868257   8.249000e+03   \n",
       "max                               98.000000  329664.000000   3.008750e+06   \n",
       "\n",
       "       NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "count                    149999.000000            149999.000000   \n",
       "mean                          8.452776                 0.265975   \n",
       "std                           5.145964                 4.169318   \n",
       "min                           0.000000                 0.000000   \n",
       "25%                           5.000000                 0.000000   \n",
       "50%                           8.000000                 0.000000   \n",
       "75%                          11.000000                 0.000000   \n",
       "max                          58.000000                98.000000   \n",
       "\n",
       "       NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "count                 149999.000000                         149999.000000   \n",
       "mean                       1.018233                              0.240388   \n",
       "std                        1.129772                              4.155193   \n",
       "min                        0.000000                              0.000000   \n",
       "25%                        0.000000                              0.000000   \n",
       "50%                        1.000000                              0.000000   \n",
       "75%                        2.000000                              0.000000   \n",
       "max                       54.000000                             98.000000   \n",
       "\n",
       "       NumberOfDependents  \n",
       "count       146075.000000  \n",
       "mean             0.757214  \n",
       "std              1.115085  \n",
       "min              0.000000  \n",
       "25%              0.000000  \n",
       "50%              0.000000  \n",
       "75%              1.000000  \n",
       "max             20.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "A68TU-TtMpIq",
    "outputId": "36e2d165-8260-44eb-d4c2-722c31856154"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeriousDlqin2yrs                            0\n",
       "RevolvingUtilizationOfUnsecuredLines        0\n",
       "age                                         0\n",
       "NumberOfTime30-59DaysPastDueNotWorse        0\n",
       "DebtRatio                                   0\n",
       "MonthlyIncome                           29731\n",
       "NumberOfOpenCreditLinesAndLoans             0\n",
       "NumberOfTimes90DaysLate                     0\n",
       "NumberRealEstateLoansOrLines                0\n",
       "NumberOfTime60-89DaysPastDueNotWorse        0\n",
       "NumberOfDependents                       3924\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NuubqZ9OuPW9",
    "outputId": "a25e75c1-4029-4192-dd1d-812b55806114"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeriousDlqin2yrs                        101503\n",
       "RevolvingUtilizationOfUnsecuredLines         0\n",
       "age                                          0\n",
       "NumberOfTime30-59DaysPastDueNotWorse         0\n",
       "DebtRatio                                    0\n",
       "MonthlyIncome                            20103\n",
       "NumberOfOpenCreditLinesAndLoans              0\n",
       "NumberOfTimes90DaysLate                      0\n",
       "NumberRealEstateLoansOrLines                 0\n",
       "NumberOfTime60-89DaysPastDueNotWorse         0\n",
       "NumberOfDependents                        2626\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "lfHCop1HvKNq"
   },
   "outputs": [],
   "source": [
    "##imputation to handle missing data\n",
    "columns=['RevolvingUtilizationOfUnsecuredLines','age','NumberOfTime30-59DaysPastDueNotWorse','DebtRatio','MonthlyIncome','NumberOfOpenCreditLinesAndLoans','NumberOfTimes90DaysLate','NumberRealEstateLoansOrLines','NumberOfTime60-89DaysPastDueNotWorse','NumberOfDependents']\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import IterativeImputer\n",
    "imputer1 = IterativeImputer()\n",
    "Train_data[columns]=imputer1.fit_transform(Train_data[columns])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IOUfQpVUzjqs",
    "outputId": "341abddc-aab2-486c-8be0-c83720927267"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeriousDlqin2yrs                        0\n",
       "RevolvingUtilizationOfUnsecuredLines    0\n",
       "age                                     0\n",
       "NumberOfTime30-59DaysPastDueNotWorse    0\n",
       "DebtRatio                               0\n",
       "MonthlyIncome                           0\n",
       "NumberOfOpenCreditLinesAndLoans         0\n",
       "NumberOfTimes90DaysLate                 0\n",
       "NumberRealEstateLoansOrLines            0\n",
       "NumberOfTime60-89DaysPastDueNotWorse    0\n",
       "NumberOfDependents                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "KIDTo_ktzrw_"
   },
   "outputs": [],
   "source": [
    "Test_data[columns]=imputer1.transform(Test_data[columns])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iPiznu4WyDoT",
    "outputId": "1c347b44-34f4-42ca-9fad-45c4bf70b7d9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeriousDlqin2yrs                        101503\n",
       "RevolvingUtilizationOfUnsecuredLines         0\n",
       "age                                          0\n",
       "NumberOfTime30-59DaysPastDueNotWorse         0\n",
       "DebtRatio                                    0\n",
       "MonthlyIncome                                0\n",
       "NumberOfOpenCreditLinesAndLoans              0\n",
       "NumberOfTimes90DaysLate                      0\n",
       "NumberRealEstateLoansOrLines                 0\n",
       "NumberOfTime60-89DaysPastDueNotWorse         0\n",
       "NumberOfDependents                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "id": "kfzg1qRRRy-1",
    "outputId": "f25af645-0cba-478c-99e2-6a7ade958899"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='SeriousDlqin2yrs',data=Train_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ndrV7wKGT_3G",
    "outputId": "8fbe8f45-ca7e-43e1-bd40-34c37d074d3d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    139973\n",
       "1     10026\n",
       "Name: SeriousDlqin2yrs, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_data['SeriousDlqin2yrs'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "xxpaje0LSojI"
   },
   "outputs": [],
   "source": [
    "from sklearn.utils import resample\n",
    "#create two different dataframe of majority and minority class\n",
    "df_majority = Train_data[(Train_data['SeriousDlqin2yrs']==0)]\n",
    "df_minority = Train_data[(Train_data['SeriousDlqin2yrs']==1)]\n",
    "# upsample minority class\n",
    "df_minority_upsampled = resample(df_minority,\n",
    "                                 replace=True,    # sample with replacement\n",
    "                                 n_samples= 139973, # to match majority class\n",
    "                                 random_state=42)  # reproducible results\n",
    "# Combine majority class with upsampled minority class\n",
    "Train_data= pd.concat([df_minority_upsampled, df_majority])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WjzCkNIqUO90",
    "outputId": "e36b3cfc-2746-40e8-f0bb-e7922163e218"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    139973\n",
       "0    139973\n",
       "Name: SeriousDlqin2yrs, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_data['SeriousDlqin2yrs'].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "Y7q-EFal0Ii9"
   },
   "source": [
    "**Modeling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "5jfOCWWv1IQg"
   },
   "outputs": [],
   "source": [
    "X=Train_data.drop(columns=['SeriousDlqin2yrs'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 505
    },
    "id": "slt0PhiW2Oyw",
    "outputId": "af082e17-0951-4a72-9c1f-23ddd542a2ca"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>108932</th>\n",
       "      <td>0.455982</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.518825</td>\n",
       "      <td>8100.000000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13215</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.109424</td>\n",
       "      <td>5400.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81371</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.154211</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78054</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.208044</td>\n",
       "      <td>2162.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86466</th>\n",
       "      <td>1.264890</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.295894</td>\n",
       "      <td>5795.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149995</th>\n",
       "      <td>0.040674</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.225131</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149996</th>\n",
       "      <td>0.299745</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.716562</td>\n",
       "      <td>5584.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149997</th>\n",
       "      <td>0.246044</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3870.000000</td>\n",
       "      <td>3203.967613</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149998</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5716.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149999</th>\n",
       "      <td>0.850283</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.249908</td>\n",
       "      <td>8158.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>279946 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        RevolvingUtilizationOfUnsecuredLines   age  \\\n",
       "108932                              0.455982  36.0   \n",
       "13215                               1.000000  30.0   \n",
       "81371                               1.000000  25.0   \n",
       "78054                               1.000000  36.0   \n",
       "86466                               1.264890  60.0   \n",
       "...                                      ...   ...   \n",
       "149995                              0.040674  74.0   \n",
       "149996                              0.299745  44.0   \n",
       "149997                              0.246044  58.0   \n",
       "149998                              0.000000  30.0   \n",
       "149999                              0.850283  64.0   \n",
       "\n",
       "        NumberOfTime30-59DaysPastDueNotWorse    DebtRatio  MonthlyIncome  \\\n",
       "108932                                   0.0     0.518825    8100.000000   \n",
       "13215                                    1.0     0.109424    5400.000000   \n",
       "81371                                    0.0     0.154211    4000.000000   \n",
       "78054                                    0.0     0.208044    2162.000000   \n",
       "86466                                    3.0     0.295894    5795.000000   \n",
       "...                                      ...          ...            ...   \n",
       "149995                                   0.0     0.225131    2100.000000   \n",
       "149996                                   0.0     0.716562    5584.000000   \n",
       "149997                                   0.0  3870.000000    3203.967613   \n",
       "149998                                   0.0     0.000000    5716.000000   \n",
       "149999                                   0.0     0.249908    8158.000000   \n",
       "\n",
       "        NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "108932                             12.0                      1.0   \n",
       "13215                               4.0                      3.0   \n",
       "81371                               3.0                      0.0   \n",
       "78054                               2.0                      1.0   \n",
       "86466                               7.0                      4.0   \n",
       "...                                 ...                      ...   \n",
       "149995                              4.0                      0.0   \n",
       "149996                              4.0                      0.0   \n",
       "149997                             18.0                      0.0   \n",
       "149998                              4.0                      0.0   \n",
       "149999                              8.0                      0.0   \n",
       "\n",
       "        NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "108932                           1.0                                   0.0   \n",
       "13215                            0.0                                   2.0   \n",
       "81371                            0.0                                   0.0   \n",
       "78054                            0.0                                   1.0   \n",
       "86466                            1.0                                   1.0   \n",
       "...                              ...                                   ...   \n",
       "149995                           1.0                                   0.0   \n",
       "149996                           1.0                                   0.0   \n",
       "149997                           1.0                                   0.0   \n",
       "149998                           0.0                                   0.0   \n",
       "149999                           2.0                                   0.0   \n",
       "\n",
       "        NumberOfDependents  \n",
       "108932                 2.0  \n",
       "13215                  0.0  \n",
       "81371                  0.0  \n",
       "78054                  3.0  \n",
       "86466                  0.0  \n",
       "...                    ...  \n",
       "149995                 0.0  \n",
       "149996                 2.0  \n",
       "149997                 0.0  \n",
       "149998                 0.0  \n",
       "149999                 0.0  \n",
       "\n",
       "[279946 rows x 10 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "7nk3MG7IilB1"
   },
   "outputs": [],
   "source": [
    "y=Train_data['SeriousDlqin2yrs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yMIbEgUWiu7g",
    "outputId": "7ed5a5b6-b829-47b7-f760-b8111712d5cf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "108932    1\n",
       "13215     1\n",
       "81371     1\n",
       "78054     1\n",
       "86466     1\n",
       "         ..\n",
       "149995    0\n",
       "149996    0\n",
       "149997    0\n",
       "149998    0\n",
       "149999    0\n",
       "Name: SeriousDlqin2yrs, Length: 279946, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "cETd4CdAJGoj"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "x_train,x_test,y_train,y_test=train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "VISe8qiNmh6C"
   },
   "outputs": [],
   "source": [
    "y_train = np.reshape(y_train.values, (y_train.shape[0], 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "HP9KpoOqn5C6"
   },
   "outputs": [],
   "source": [
    "y_test = np.reshape(y_test.values, (y_test.shape[0], 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vSFLcLqqmK7t",
    "outputId": "0d85ac12-5f68-4954-fc17-a880a60004b1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shapes:\n",
      " x_train(223956, 10)\n",
      " y_train(223956, 1)\n",
      " x_test(55990, 10)\n",
      " y_test(55990, 1)\n"
     ]
    }
   ],
   "source": [
    "print('shapes:')\n",
    "print(' x_train{}'.format(x_train.shape))\n",
    "print(' y_train{}'.format(y_train.shape))\n",
    "print(' x_test{}'.format(x_test.shape))\n",
    "print(' y_test{}'.format(y_test.shape))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "4HvGEsCX2pGg"
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier as knn\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "sLc5tZFG5_rC"
   },
   "outputs": [],
   "source": [
    "def predict_(algorithm):\n",
    "  algorithm.fit(x_train,y_train)\n",
    "  return(algorithm.predict(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "-1EGjKiX7Vw8"
   },
   "outputs": [],
   "source": [
    "def metrics_(model,_prediction_):\n",
    "  acc=accuracy_score(y_test,_prediction_)\n",
    "  class_report=classification_report(y_test,_prediction_)\n",
    "  print(f\"for {model.__class__.__name__}\")\n",
    "  print(f\"accuracy : {acc*100} %\")\n",
    "  print(\"Classification report : \")\n",
    "  print(f\"{class_report}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oAL0irEq6brb",
    "outputId": "75b0d03f-7872-4e1e-94c5-995f20b1ab6f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\bedou\\OneDrive\\Bureau\\Give_me_credit\\GiveMeCredit__deployed\\venv\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "c:\\Users\\bedou\\OneDrive\\Bureau\\Give_me_credit\\GiveMeCredit__deployed\\venv\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\bedou\\OneDrive\\Bureau\\Give_me_credit\\GiveMeCredit__deployed\\venv\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "KNN_prediction=predict_(knn(n_neighbors=2))\n",
    "SGD_prediction=predict_(SGDClassifier())\n",
    "Tree_prediction=predict_(DecisionTreeClassifier())\n",
    "Naive_prediction=predict_(GaussianNB())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7d4p9sSEWRAR",
    "outputId": "3cff4dff-1dfa-482d-ee4e-f2687e3fa1c6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for KNeighborsClassifier\n",
      "accuracy : 97.05125915342026 %\n",
      "Classification report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.94      0.97     27997\n",
      "           1       0.95      1.00      0.97     27993\n",
      "\n",
      "    accuracy                           0.97     55990\n",
      "   macro avg       0.97      0.97      0.97     55990\n",
      "weighted avg       0.97      0.97      0.97     55990\n",
      "\n",
      "for SGDClassifier\n",
      "accuracy : 53.518485443829256 %\n",
      "Classification report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.52      0.96      0.67     27997\n",
      "           1       0.75      0.11      0.19     27993\n",
      "\n",
      "    accuracy                           0.54     55990\n",
      "   macro avg       0.63      0.54      0.43     55990\n",
      "weighted avg       0.63      0.54      0.43     55990\n",
      "\n",
      "for DecisionTreeClassifier\n",
      "accuracy : 96.80835863547061 %\n",
      "Classification report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.94      0.97     27997\n",
      "           1       0.94      1.00      0.97     27993\n",
      "\n",
      "    accuracy                           0.97     55990\n",
      "   macro avg       0.97      0.97      0.97     55990\n",
      "weighted avg       0.97      0.97      0.97     55990\n",
      "\n",
      "for GaussianNB\n",
      "accuracy : 52.861225218789066 %\n",
      "Classification report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.51      0.99      0.68     27997\n",
      "           1       0.88      0.07      0.12     27993\n",
      "\n",
      "    accuracy                           0.53     55990\n",
      "   macro avg       0.70      0.53      0.40     55990\n",
      "weighted avg       0.70      0.53      0.40     55990\n",
      "\n"
     ]
    }
   ],
   "source": [
    "metrics_(knn(n_neighbors=4),KNN_prediction)\n",
    "metrics_(SGDClassifier(),SGD_prediction)\n",
    "metrics_(DecisionTreeClassifier(),Tree_prediction)\n",
    "metrics_(GaussianNB(),Naive_prediction)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "aPCWv0sCF1ZS"
   },
   "source": [
    "Voting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "yPHGa3ZdFz1E"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import VotingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tRmZJRxbF6mL",
    "outputId": "a08775cd-2580-4680-e6b8-d20492663b40"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\bedou\\OneDrive\\Bureau\\Give_me_credit\\GiveMeCredit__deployed\\venv\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\bedou\\OneDrive\\Bureau\\Give_me_credit\\GiveMeCredit__deployed\\venv\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for VotingClassifier\n",
      "accuracy : 94.42936238614038 %\n",
      "Classification report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.89      0.94     27997\n",
      "           1       0.90      1.00      0.95     27993\n",
      "\n",
      "    accuracy                           0.94     55990\n",
      "   macro avg       0.95      0.94      0.94     55990\n",
      "weighted avg       0.95      0.94      0.94     55990\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model=VotingClassifier([('KNN',knn(n_neighbors=2)),('SGD',SGDClassifier(loss='log')),('DTC',DecisionTreeClassifier()),('GNB',GaussianNB())],voting='soft')\n",
    "voting_prediction=predict_(model)\n",
    "metrics_(model,voting_prediction)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "oCkofTuuI3Db"
   },
   "source": [
    "Bagging Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "R-PueSJ7HeSF"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier, RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "w9j1N0KWJAdv",
    "outputId": "ba7ad5d0-7b1a-4e6b-a266-a858682c2b6b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\bedou\\OneDrive\\Bureau\\Give_me_credit\\GiveMeCredit__deployed\\venv\\lib\\site-packages\\sklearn\\ensemble\\_bagging.py:719: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for BaggingClassifier\n",
      "accuracy : 52.81657438828362 %\n",
      "Classification report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.51      0.99      0.68     27997\n",
      "           1       0.88      0.06      0.12     27993\n",
      "\n",
      "    accuracy                           0.53     55990\n",
      "   macro avg       0.70      0.53      0.40     55990\n",
      "weighted avg       0.70      0.53      0.40     55990\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model2=BaggingClassifier(base_estimator=GaussianNB(),n_estimators=100)\n",
    "Bagging_prediction=predict_(model2)\n",
    "metrics_(model2,Bagging_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cwyL7JFPJaLW",
    "outputId": "9939a87a-2479-42bb-95f4-9b0472d75da1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\bedou\\OneDrive\\Bureau\\Give_me_credit\\GiveMeCredit__deployed\\venv\\lib\\site-packages\\ipykernel_launcher.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for ndarray\n",
      "accuracy : 98.49794606179675 %\n",
      "Classification report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.98     27997\n",
      "           1       0.97      1.00      0.99     27993\n",
      "\n",
      "    accuracy                           0.98     55990\n",
      "   macro avg       0.99      0.98      0.98     55990\n",
      "weighted avg       0.99      0.98      0.98     55990\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model3=RandomForestClassifier(n_estimators=100)\n",
    "RF_prediction=predict_(model3)\n",
    "metrics_(RF_prediction,RF_prediction)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "xpgM9R9HOhkR"
   },
   "source": [
    "Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "hMkwosWbJvHr"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fIG_uSfwKnT6",
    "outputId": "aedb6ae6-2622-48c3-90a7-1975b04236f3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\bedou\\OneDrive\\Bureau\\Give_me_credit\\GiveMeCredit__deployed\\venv\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for ndarray\n",
      "accuracy : 98.49794606179675 %\n",
      "Classification report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.98     27997\n",
      "           1       0.97      1.00      0.99     27993\n",
      "\n",
      "    accuracy                           0.98     55990\n",
      "   macro avg       0.99      0.98      0.98     55990\n",
      "weighted avg       0.99      0.98      0.98     55990\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model4=AdaBoostClassifier(n_estimators=100)\n",
    "Ada_prediction=predict_(model4)\n",
    "metrics_(Ada_prediction,RF_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "3Mnx9B7xoZTG"
   },
   "outputs": [],
   "source": [
    "test=Test_data.drop(columns=['SeriousDlqin2yrs'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UOT8mmOPKvjm",
    "outputId": "b4ecf7bb-9a16-4b1b-9736-f26482157a4a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\bedou\\OneDrive\\Bureau\\Give_me_credit\\GiveMeCredit__deployed\\venv\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\bedou\\OneDrive\\Bureau\\Give_me_credit\\GiveMeCredit__deployed\\venv\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "model.fit(x_train,y_train)\n",
    "prediction=model.predict_proba(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "id": "nALvjBcPO99o"
   },
   "outputs": [],
   "source": [
    "#submission=pd.read_csv('/content/drive/MyDrive/Challenge_Kaggle_Credit/sampleEntry.csv (1).zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wvZQwVRL9Dcs",
    "outputId": "2bf0d396-471d-445f-b3ad-e7cf0bf5ef7d"
   },
   "outputs": [],
   "source": [
    "#type(submission)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wOsTFBgS-zFZ",
    "outputId": "39ad25ee-1e9c-4203-ef78-adafe7ead750"
   },
   "outputs": [],
   "source": [
    "#type(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "NuEbXKN2Qvfv",
    "outputId": "85ddec2c-0d4c-4604-ec51-0ecec8e0c3da"
   },
   "outputs": [],
   "source": [
    "#submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "id": "2QXWibt1_Xxy"
   },
   "outputs": [],
   "source": [
    "#submission = submission.drop(columns='Probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1QSDe0IDAGvT",
    "outputId": "87f8e152-7d3a-460f-8cf2-4f632dfa4993"
   },
   "outputs": [],
   "source": [
    "#submission.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sb1aK3-3-aU3",
    "outputId": "7b4a8695-a115-4f2f-ee76-d8c5fe327871"
   },
   "outputs": [],
   "source": [
    "#type(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "id": "SYpx09QX-eqd"
   },
   "outputs": [],
   "source": [
    "#prediction_df = pd.DataFrame(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "id": "wBfET4PvQ24s"
   },
   "outputs": [],
   "source": [
    "#prediction_df= prediction_df.drop(columns=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "JDeh-6AADRDt",
    "outputId": "18686008-002e-4e29-9ac9-b39f2d180bfe"
   },
   "outputs": [],
   "source": [
    "#prediction_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "Unk83zdA3_uD",
    "outputId": "6eadfac3-4f4a-4f19-a96d-f8c743c58155"
   },
   "outputs": [],
   "source": [
    "#submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "id": "2-lFSDa_4Y02"
   },
   "outputs": [],
   "source": [
    "#submission=pd.DataFrame(submission)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IRu1sCGg49IW",
    "outputId": "f64d90a1-73e4-4f69-8878-a6d263e14b27"
   },
   "outputs": [],
   "source": [
    "#type(submission)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BQUfoeSI80GG",
    "outputId": "bb38f856-e197-4cd6-884e-69f51542a184"
   },
   "outputs": [],
   "source": [
    "#submission.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DSgXN_Sf671s",
    "outputId": "f0a9381f-d89b-4194-e32c-ba181d6a546f"
   },
   "outputs": [],
   "source": [
    "#submission.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "id": "BDG0Q0W7DX4T"
   },
   "outputs": [],
   "source": [
    "#submission['Probability'] = prediction_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "0kOEzEdVDkDM",
    "outputId": "7d31d3bd-964e-4738-93d7-a995abecc829"
   },
   "outputs": [],
   "source": [
    "#submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {
    "id": "HXTDDeIH4CDz"
   },
   "outputs": [],
   "source": [
    "#submission.to_csv('submission.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "id": "sHnufbqM5GVH"
   },
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "id": "FtsIVdarGxwo"
   },
   "outputs": [],
   "source": [
    "pickle.dump(model,open('model.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "id": "OACmP9uyHwfT"
   },
   "outputs": [],
   "source": [
    "pickled_model=pickle.load(open('model.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "h21O1AsyIPCy",
    "outputId": "dc31e7d3-e071-4410-d755-92a4e0afb19f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pickled_model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Dq-0SQ8ZIUxX"
   },
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
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