datathon.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "218fdf78-c41b-4ae4-b90a-19bf546780d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from category_encoders import OneHotEncoder,CatBoostEncoder,OrdinalEncoder,SumEncoder\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import mutual_info_score\n",
    "\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy import stats\n",
    "from scipy.stats import uniform as sp_randfloat\n",
    "from scipy.stats import randint as sp_randint\n",
    "\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "import xgboost as xgb\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3f5406f6-8ce8-4436-9f90-747b518ecd26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xgboost\n",
      "  Using cached xgboost-1.6.2-py3-none-manylinux2014_x86_64.whl (255.9 MB)\n",
      "Requirement already satisfied: scipy in /srv/conda/envs/notebook/lib/python3.9/site-packages (from xgboost) (1.9.1)\n",
      "Requirement already satisfied: numpy in /srv/conda/envs/notebook/lib/python3.9/site-packages (from xgboost) (1.23.3)\n",
      "Installing collected packages: xgboost\n",
      "Successfully installed xgboost-1.6.2\n"
     ]
    }
   ],
   "source": [
    "#pip install category_encoders\n",
    "#!pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "898b79ee-1023-4579-90d8-77f756c1fef5",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>pay_schedule</th>\n",
       "      <th>home_owner</th>\n",
       "      <th>income</th>\n",
       "      <th>months_employed</th>\n",
       "      <th>years_employed</th>\n",
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       "      <th>personal_account_y</th>\n",
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       "      <th>risk_score_3</th>\n",
       "      <th>risk_score_4</th>\n",
       "      <th>risk_score_5</th>\n",
       "      <th>ext_quality_score</th>\n",
       "      <th>ext_quality_score_2</th>\n",
       "      <th>inquiries_last_month</th>\n",
       "      <th>e_signed</th>\n",
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       "    <tr>\n",
       "      <th>Entry_id</th>\n",
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       "      <th>8492423</th>\n",
       "      <td>21</td>\n",
       "      <td>weekly</td>\n",
       "      <td>0</td>\n",
       "      <td>2303</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "          age  pay_schedule  home_owner  income  months_employed  \\\n",
       "Entry_id                                                           \n",
       "7629673    40     bi-weekly           1    3135                0   \n",
       "5335819    33  semi-monthly           0    3590                0   \n",
       "8492423    21        weekly           0    2303                0   \n",
       "7948313    26     bi-weekly           0    2795                0   \n",
       "4297036    43     bi-weekly           0    5000                0   \n",
       "\n",
       "          years_employed  current_address_year  personal_account_m  \\\n",
       "Entry_id                                                             \n",
       "7629673                3                     3                   6   \n",
       "5335819                5                     2                   2   \n",
       "8492423                5                     8                   2   \n",
       "7948313                4                     4                   1   \n",
       "4297036                2                     1                   1   \n",
       "\n",
       "          personal_account_y  has_debt  amount_requested  risk_score  \\\n",
       "Entry_id                                                               \n",
       "7629673                    2         1               550       36200   \n",
       "5335819                    8         1              1100       53850   \n",
       "8492423                    7         1               600       74850   \n",
       "7948313                    6         1               800       50800   \n",
       "4297036                    2         1              1100       69100   \n",
       "\n",
       "          risk_score_2  risk_score_3  risk_score_4  risk_score_5  \\\n",
       "Entry_id                                                           \n",
       "7629673       0.737398      0.903517      0.487712      0.515977   \n",
       "5335819       0.617361      0.857560      0.613487      0.665523   \n",
       "8492423       0.677109      0.758765      0.495609      0.664762   \n",
       "7948313       0.738055      0.873204      0.666437      0.700392   \n",
       "4297036       0.798303      0.841747      0.401971      0.568787   \n",
       "\n",
       "          ext_quality_score  ext_quality_score_2  inquiries_last_month  \\\n",
       "Entry_id                                                                 \n",
       "7629673            0.580918             0.380918                    10   \n",
       "5335819            0.744634             0.744634                    12   \n",
       "8492423            0.592556             0.492556                     6   \n",
       "7948313            0.584130             0.684130                    14   \n",
       "4297036            0.525905             0.725905                     5   \n",
       "\n",
       "          e_signed  \n",
       "Entry_id            \n",
       "7629673          1  \n",
       "5335819          0  \n",
       "8492423          1  \n",
       "7948313          1  \n",
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      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"train.csv\").set_index('Entry_id')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "ddedf83d-f1e2-4845-a496-662477f721ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['request_ratio']=df['amount_requested']/df['income']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "0b5dcb8d-d140-4b80-a0ef-df1188923ef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['duration_employed'] = df['years_employed'] + (df['months_employed']/12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "a44a93d0-397a-4493-8e6a-bfabe0bc316f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['personal_account_d'] = df['personal_account_y'] + (df['personal_account_m']/12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "859c4a8b-a6d1-401c-ab6e-e6f614592a2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['avg_risk'] = (df['risk_score']+df['risk_score_2']+df['risk_score_3']+df['risk_score_4']+df['risk_score_5'])/5"
   ]
  },
  {
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   "execution_count": 69,
   "id": "c4ef3baf-faf8-4cc9-a088-a4996c403c7f",
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      ],
      "text/plain": [
       "          age  pay_schedule  home_owner  income  months_employed  \\\n",
       "Entry_id                                                           \n",
       "7629673    40     bi-weekly           1    3135                0   \n",
       "5335819    33  semi-monthly           0    3590                0   \n",
       "8492423    21        weekly           0    2303                0   \n",
       "7948313    26     bi-weekly           0    2795                0   \n",
       "4297036    43     bi-weekly           0    5000                0   \n",
       "\n",
       "          years_employed  current_address_year  personal_account_m  \\\n",
       "Entry_id                                                             \n",
       "7629673                3                     3                   6   \n",
       "5335819                5                     2                   2   \n",
       "8492423                5                     8                   2   \n",
       "7948313                4                     4                   1   \n",
       "4297036                2                     1                   1   \n",
       "\n",
       "          personal_account_y  has_debt  ...  risk_score_4  risk_score_5  \\\n",
       "Entry_id                                ...                               \n",
       "7629673                    2         1  ...      0.487712      0.515977   \n",
       "5335819                    8         1  ...      0.613487      0.665523   \n",
       "8492423                    7         1  ...      0.495609      0.664762   \n",
       "7948313                    6         1  ...      0.666437      0.700392   \n",
       "4297036                    2         1  ...      0.401971      0.568787   \n",
       "\n",
       "          ext_quality_score  ext_quality_score_2  inquiries_last_month  \\\n",
       "Entry_id                                                                 \n",
       "7629673            0.580918             0.380918                    10   \n",
       "5335819            0.744634             0.744634                    12   \n",
       "8492423            0.592556             0.492556                     6   \n",
       "7948313            0.584130             0.684130                    14   \n",
       "4297036            0.525905             0.725905                     5   \n",
       "\n",
       "          e_signed  request_ratio  duration_employed  personal_account_d  \\\n",
       "Entry_id                                                                   \n",
       "7629673          1       0.175439                3.0            2.500000   \n",
       "5335819          0       0.306407                5.0            8.166667   \n",
       "8492423          1       0.260530                5.0            7.166667   \n",
       "7948313          1       0.286225                4.0            6.083333   \n",
       "4297036          1       0.220000                2.0            2.083333   \n",
       "\n",
       "              avg_risk  \n",
       "Entry_id                \n",
       "7629673    7240.528921  \n",
       "5335819   10770.550786  \n",
       "8492423   14970.519249  \n",
       "7948313   10160.595618  \n",
       "4297036   13820.522162  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "3af9096c-2f56-46c4-8a0b-9c7f801a1736",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/sklearn/metrics/cluster/_supervised.py:64: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/sklearn/metrics/cluster/_supervised.py:64: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/sklearn/metrics/cluster/_supervised.py:64: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/sklearn/metrics/cluster/_supervised.py:64: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/sklearn/metrics/cluster/_supervised.py:64: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/sklearn/metrics/cluster/_supervised.py:64: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/sklearn/metrics/cluster/_supervised.py:64: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/sklearn/metrics/cluster/_supervised.py:64: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/sklearn/metrics/cluster/_supervised.py:64: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "age                     0.007713\n",
       "pay_schedule            0.000352\n",
       "home_owner              0.001500\n",
       "income                  0.100868\n",
       "months_employed         0.001164\n",
       "years_employed          0.001212\n",
       "current_address_year    0.000613\n",
       "personal_account_m      0.007018\n",
       "personal_account_y      0.007046\n",
       "has_debt                0.000542\n",
       "amount_requested        0.046757\n",
       "risk_score              0.065406\n",
       "risk_score_2            0.677028\n",
       "risk_score_3            0.185455\n",
       "risk_score_4            0.682676\n",
       "risk_score_5            0.680656\n",
       "ext_quality_score       0.678357\n",
       "ext_quality_score_2     0.679617\n",
       "inquiries_last_month    0.001734\n",
       "e_signed                0.689849\n",
       "request_ratio           0.342117\n",
       "homedebt_cluster        0.001957\n",
       "duration_employed       0.009167\n",
       "personal_account_d      0.017726\n",
       "dtype: float64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "def mutual_info(series):\n",
    "    return mutual_info_score(series,df['e_signed'])\n",
    "df.apply(mutual_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "aa791cdb-c120-4602-8d33-da80783bd592",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=4)\n",
    "df[\"homedebt_cluster\"] = kmeans.fit_predict(df[['home_owner','has_debt']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "96b55554-bbe6-4634-bb39-c1e8a41e05b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "162"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['duration_employed'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "3ebdbdff-7c36-484f-954a-41888e554f05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'pay_schedule', 'home_owner', 'income', 'months_employed',\n",
       "       'years_employed', 'current_address_year', 'personal_account_m',\n",
       "       'personal_account_y', 'has_debt', 'amount_requested', 'risk_score',\n",
       "       'risk_score_2', 'risk_score_3', 'risk_score_4', 'risk_score_5',\n",
       "       'ext_quality_score', 'ext_quality_score_2', 'inquiries_last_month',\n",
       "       'e_signed'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "2871a602-e496-49d9-b57c-4eb191e50d47",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/category_encoders/one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for cat_name, class_ in values.iteritems():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.8398921294446664\n",
      "Validation Accuracy: 0.6385782747603834\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/category_encoders/one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for cat_name, class_ in values.iteritems():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.8420894926088693\n",
      "Validation Accuracy: 0.6477635782747604\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/category_encoders/one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for cat_name, class_ in values.iteritems():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.8420894926088693\n",
      "Validation Accuracy: 0.6481629392971247\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/category_encoders/one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for cat_name, class_ in values.iteritems():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.8328006392329205\n",
      "Validation Accuracy: 0.6425718849840255\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/category_encoders/one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for cat_name, class_ in values.iteritems():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.8420894926088693\n",
      "Validation Accuracy: 0.6481629392971247\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/category_encoders/one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for cat_name, class_ in values.iteritems():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.8420894926088693\n",
      "Validation Accuracy: 0.6481629392971247\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/category_encoders/one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for cat_name, class_ in values.iteritems():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.8328006392329205\n",
      "Validation Accuracy: 0.6417731629392971\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/category_encoders/one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for cat_name, class_ in values.iteritems():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.8420894926088693\n",
      "Validation Accuracy: 0.6473642172523961\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/category_encoders/one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for cat_name, class_ in values.iteritems():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.8420894926088693\n",
      "Validation Accuracy: 0.6489616613418531\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/category_encoders/one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for cat_name, class_ in values.iteritems():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.8420894926088693\n",
      "Validation Accuracy: 0.6449680511182109\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/category_encoders/one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for cat_name, class_ in values.iteritems():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.8398921294446664\n",
      "Validation Accuracy: 0.6393769968051118\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/category_encoders/one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for cat_name, class_ in values.iteritems():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.8328006392329205\n",
      "Validation Accuracy: 0.6417731629392971\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/category_encoders/one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for cat_name, class_ in values.iteritems():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.8398921294446664\n",
      "Validation Accuracy: 0.6405750798722045\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/category_encoders/one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for cat_name, class_ in values.iteritems():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.8398921294446664\n",
      "Validation Accuracy: 0.6405750798722045\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/category_encoders/one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for cat_name, class_ in values.iteritems():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.8420894926088693\n",
      "Validation Accuracy: 0.645367412140575\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/category_encoders/one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for cat_name, class_ in values.iteritems():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.8398921294446664\n",
      "Validation Accuracy: 0.639776357827476\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/category_encoders/one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for cat_name, class_ in values.iteritems():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.8398921294446664\n",
      "Validation Accuracy: 0.6413738019169329\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/category_encoders/one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for cat_name, class_ in values.iteritems():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.8420894926088693\n",
      "Validation Accuracy: 0.646964856230032\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/category_encoders/one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for cat_name, class_ in values.iteritems():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.8420894926088693\n",
      "Validation Accuracy: 0.6477635782747604\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/category_encoders/one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for cat_name, class_ in values.iteritems():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.8398921294446664\n",
      "Validation Accuracy: 0.6381789137380192\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/category_encoders/one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for cat_name, class_ in values.iteritems():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.8398921294446664\n",
      "Validation Accuracy: 0.6409744408945687\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/category_encoders/one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for cat_name, class_ in values.iteritems():\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [83], line 14\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m25\u001b[39m):\n\u001b[1;32m     13\u001b[0m     model \u001b[38;5;241m=\u001b[39m make_pipeline(OneHotEncoder(),GradientBoostingClassifier(max_depth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m6\u001b[39m,random_state\u001b[38;5;241m=\u001b[39mr))\n\u001b[0;32m---> 14\u001b[0m     \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     15\u001b[0m     acc_train \u001b[38;5;241m=\u001b[39m accuracy_score(y_train, model\u001b[38;5;241m.\u001b[39mpredict(X_train))\n\u001b[1;32m     16\u001b[0m     acc_val \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mscore(X_val, y_val)\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/sklearn/pipeline.py:382\u001b[0m, in \u001b[0;36mPipeline.fit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    380\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_final_estimator \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpassthrough\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    381\u001b[0m         fit_params_last_step \u001b[38;5;241m=\u001b[39m fit_params_steps[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m][\u001b[38;5;241m0\u001b[39m]]\n\u001b[0;32m--> 382\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_final_estimator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mXt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_params_last_step\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    384\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/sklearn/ensemble/_gb.py:668\u001b[0m, in \u001b[0;36mBaseGradientBoosting.fit\u001b[0;34m(self, X, y, sample_weight, monitor)\u001b[0m\n\u001b[1;32m    665\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_resize_state()\n\u001b[1;32m    667\u001b[0m \u001b[38;5;66;03m# fit the boosting stages\u001b[39;00m\n\u001b[0;32m--> 668\u001b[0m n_stages \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_stages\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    669\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    670\u001b[0m \u001b[43m    \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    671\u001b[0m \u001b[43m    \u001b[49m\u001b[43mraw_predictions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    672\u001b[0m \u001b[43m    \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    673\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_rng\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    674\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX_val\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    675\u001b[0m \u001b[43m    \u001b[49m\u001b[43my_val\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    676\u001b[0m \u001b[43m    \u001b[49m\u001b[43msample_weight_val\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    677\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbegin_at_stage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    678\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmonitor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    679\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    681\u001b[0m \u001b[38;5;66;03m# change shape of arrays after fit (early-stopping or additional ests)\u001b[39;00m\n\u001b[1;32m    682\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_stages \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimators_\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/sklearn/ensemble/_gb.py:745\u001b[0m, in \u001b[0;36mBaseGradientBoosting._fit_stages\u001b[0;34m(self, X, y, raw_predictions, sample_weight, random_state, X_val, y_val, sample_weight_val, begin_at_stage, monitor)\u001b[0m\n\u001b[1;32m    738\u001b[0m     old_oob_score \u001b[38;5;241m=\u001b[39m loss_(\n\u001b[1;32m    739\u001b[0m         y[\u001b[38;5;241m~\u001b[39msample_mask],\n\u001b[1;32m    740\u001b[0m         raw_predictions[\u001b[38;5;241m~\u001b[39msample_mask],\n\u001b[1;32m    741\u001b[0m         sample_weight[\u001b[38;5;241m~\u001b[39msample_mask],\n\u001b[1;32m    742\u001b[0m     )\n\u001b[1;32m    744\u001b[0m \u001b[38;5;66;03m# fit next stage of trees\u001b[39;00m\n\u001b[0;32m--> 745\u001b[0m raw_predictions \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_stage\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    746\u001b[0m \u001b[43m    \u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    747\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    748\u001b[0m \u001b[43m    \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    749\u001b[0m \u001b[43m    \u001b[49m\u001b[43mraw_predictions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    750\u001b[0m \u001b[43m    \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    751\u001b[0m \u001b[43m    \u001b[49m\u001b[43msample_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    752\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    753\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX_csc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    754\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX_csr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    755\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    757\u001b[0m \u001b[38;5;66;03m# track deviance (= loss)\u001b[39;00m\n\u001b[1;32m    758\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m do_oob:\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/sklearn/ensemble/_gb.py:247\u001b[0m, in \u001b[0;36mBaseGradientBoosting._fit_stage\u001b[0;34m(self, i, X, y, raw_predictions, sample_weight, sample_mask, random_state, X_csc, X_csr)\u001b[0m\n\u001b[1;32m    244\u001b[0m     sample_weight \u001b[38;5;241m=\u001b[39m sample_weight \u001b[38;5;241m*\u001b[39m sample_mask\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39mfloat64)\n\u001b[1;32m    246\u001b[0m X \u001b[38;5;241m=\u001b[39m X_csr \u001b[38;5;28;01mif\u001b[39;00m X_csr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m X\n\u001b[0;32m--> 247\u001b[0m \u001b[43mtree\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresidual\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_input\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    249\u001b[0m \u001b[38;5;66;03m# update tree leaves\u001b[39;00m\n\u001b[1;32m    250\u001b[0m loss\u001b[38;5;241m.\u001b[39mupdate_terminal_regions(\n\u001b[1;32m    251\u001b[0m     tree\u001b[38;5;241m.\u001b[39mtree_,\n\u001b[1;32m    252\u001b[0m     X,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    259\u001b[0m     k\u001b[38;5;241m=\u001b[39mk,\n\u001b[1;32m    260\u001b[0m )\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/sklearn/tree/_classes.py:1342\u001b[0m, in \u001b[0;36mDecisionTreeRegressor.fit\u001b[0;34m(self, X, y, sample_weight, check_input)\u001b[0m\n\u001b[1;32m   1313\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y, sample_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, check_input\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m   1314\u001b[0m     \u001b[38;5;124;03m\"\"\"Build a decision tree regressor from the training set (X, y).\u001b[39;00m\n\u001b[1;32m   1315\u001b[0m \n\u001b[1;32m   1316\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1339\u001b[0m \u001b[38;5;124;03m        Fitted estimator.\u001b[39;00m\n\u001b[1;32m   1340\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1342\u001b[0m     \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1343\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1344\u001b[0m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1345\u001b[0m \u001b[43m        \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1346\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcheck_input\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcheck_input\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1347\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1348\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.9/site-packages/sklearn/tree/_classes.py:458\u001b[0m, in \u001b[0;36mBaseDecisionTree.fit\u001b[0;34m(self, X, y, sample_weight, check_input)\u001b[0m\n\u001b[1;32m    447\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    448\u001b[0m     builder \u001b[38;5;241m=\u001b[39m BestFirstTreeBuilder(\n\u001b[1;32m    449\u001b[0m         splitter,\n\u001b[1;32m    450\u001b[0m         min_samples_split,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    455\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmin_impurity_decrease,\n\u001b[1;32m    456\u001b[0m     )\n\u001b[0;32m--> 458\u001b[0m \u001b[43mbuilder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtree_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    460\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_outputs_ \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m is_classifier(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    461\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_classes_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_classes_[\u001b[38;5;241m0\u001b[39m]\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#col = ['request_ratio','risk_score_2','risk_score_3',\n",
    "#       'risk_score_4','risk_score_5','ext_quality_score','ext_quality_score_2',\n",
    " #       'income','age','home_owner','months_employed','years_employed','personal_account_y',\n",
    " #        'personal_account_m','amount_requested','risk_score','inquiries_last_month',\n",
    " #         'homedebt_cluster','duration_employed','personal_account_d']\n",
    "target = \"e_signed\"\n",
    "X = df.drop(columns=target)\n",
    "y = df[target]\n",
    "X_train, X_val, y_train, y_val = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42\n",
    ")\n",
    "for r in range(1,25):\n",
    "    model = make_pipeline(OneHotEncoder(),GradientBoostingClassifier(max_depth=6,random_state=r))\n",
    "    model.fit(X_train,y_train)\n",
    "    acc_train = accuracy_score(y_train, model.predict(X_train))\n",
    "    acc_val = model.score(X_val, y_val)\n",
    "\n",
    "    print(\"Training Accuracy:\",acc_train)\n",
    "    print(\"Validation Accuracy:\", acc_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "6b1bcbc6-e2e7-424e-b9db-00fd3687c3ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'pay_schedule', 'home_owner', 'income', 'months_employed',\n",
       "       'years_employed', 'current_address_year', 'personal_account_m',\n",
       "       'personal_account_y', 'has_debt', 'amount_requested', 'risk_score',\n",
       "       'risk_score_2', 'risk_score_3', 'risk_score_4', 'risk_score_5',\n",
       "       'ext_quality_score', 'ext_quality_score_2', 'inquiries_last_month',\n",
       "       'e_signed'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d74b897d-2b45-42a7-b4bb-523acfccd2e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = \"e_signed\"\n",
    "X = df.drop(columns=[target, 'request_ratio', 'duration_employed', 'personal_account_d'])\n",
    "y = df[target]\n",
    "X_train, X_val, y_train, y_val = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42\n",
    ")\n",
    "\n",
    "modelx = make_pipeline(\n",
    "                       \n",
    "                       xgb.XGBClassifier(StandardScaler(),))\n",
    "modelx.fit(X_train,y_train)\n",
    "                       \n",
    "                       \n",
    "\n",
    "acc_train = accuracy_score(y_train, modelx.predict(X_train))\n",
    "acc_val = modelx.score(X_val, y_val)\n",
    "\n",
    "print(\"Training Accuracy:\",acc_train)\n",
    "print(\"Validation Accuracy:\", acc_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "aa01d95e-0b3a-4259-87bd-672666bf96c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10012,)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "cf03fa8f-20d3-4fcc-91c4-4c28038e9fdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.8245059 , 1.25839553, 0.89114861, 1.71221867, 1.05558691,\n",
       "        2.12310185, 1.95580392, 1.0730516 , 0.68309855, 1.15992556]),\n",
       " 'std_fit_time': array([0.01112252, 0.01308326, 0.01507732, 0.01840408, 0.02044889,\n",
       "        0.00876153, 0.08259379, 0.00797131, 0.05988693, 0.07787511]),\n",
       " 'mean_score_time': array([0.00693326, 0.00851784, 0.00695357, 0.01302042, 0.00747662,\n",
       "        0.01304469, 0.00744309, 0.00863056, 0.00531163, 0.00504513]),\n",
       " 'std_score_time': array([2.45739564e-04, 2.14172013e-04, 7.93836633e-05, 5.70560848e-04,\n",
       "        2.13084297e-04, 4.07164339e-04, 2.15730038e-03, 1.87888810e-04,\n",
       "        1.30076317e-03, 3.99677727e-05]),\n",
       " 'param_learning_rate': masked_array(data=[0.30337498012674813, 0.1556087507653785,\n",
       "                    0.6704769384898571, 0.12234631861838996,\n",
       "                    0.9058545237523605, 0.07633597614521359,\n",
       "                    0.6631079416398787, 0.1927459560418805,\n",
       "                    0.20148190085605366, 0.3963215073996622],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_max_depth': masked_array(data=[5, 8, 6, 8, 4, 8, 7, 6, 8, 6],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_n_estimators': masked_array(data=[205, 235, 244, 664, 357, 497, 591, 311, 109, 412],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_subsample': masked_array(data=[0.3185721546644631, 0.40262675917714574,\n",
       "                    0.8594953859140694, 0.041205161577959615,\n",
       "                    0.2027230924006963, 0.24204131551187524,\n",
       "                    0.49409308827771403, 0.19011114135247598,\n",
       "                    0.35945013867153675, 0.49801054559092595],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'learning_rate': 0.30337498012674813,\n",
       "   'max_depth': 5,\n",
       "   'n_estimators': 205,\n",
       "   'subsample': 0.3185721546644631},\n",
       "  {'learning_rate': 0.1556087507653785,\n",
       "   'max_depth': 8,\n",
       "   'n_estimators': 235,\n",
       "   'subsample': 0.40262675917714574},\n",
       "  {'learning_rate': 0.6704769384898571,\n",
       "   'max_depth': 6,\n",
       "   'n_estimators': 244,\n",
       "   'subsample': 0.8594953859140694},\n",
       "  {'learning_rate': 0.12234631861838996,\n",
       "   'max_depth': 8,\n",
       "   'n_estimators': 664,\n",
       "   'subsample': 0.041205161577959615},\n",
       "  {'learning_rate': 0.9058545237523605,\n",
       "   'max_depth': 4,\n",
       "   'n_estimators': 357,\n",
       "   'subsample': 0.2027230924006963},\n",
       "  {'learning_rate': 0.07633597614521359,\n",
       "   'max_depth': 8,\n",
       "   'n_estimators': 497,\n",
       "   'subsample': 0.24204131551187524},\n",
       "  {'learning_rate': 0.6631079416398787,\n",
       "   'max_depth': 7,\n",
       "   'n_estimators': 591,\n",
       "   'subsample': 0.49409308827771403},\n",
       "  {'learning_rate': 0.1927459560418805,\n",
       "   'max_depth': 6,\n",
       "   'n_estimators': 311,\n",
       "   'subsample': 0.19011114135247598},\n",
       "  {'learning_rate': 0.20148190085605366,\n",
       "   'max_depth': 8,\n",
       "   'n_estimators': 109,\n",
       "   'subsample': 0.35945013867153675},\n",
       "  {'learning_rate': 0.3963215073996622,\n",
       "   'max_depth': 6,\n",
       "   'n_estimators': 412,\n",
       "   'subsample': 0.49801054559092595}],\n",
       " 'split0_test_score': array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),\n",
       " 'split1_test_score': array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),\n",
       " 'split2_test_score': array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),\n",
       " 'split3_test_score': array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),\n",
       " 'split4_test_score': array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),\n",
       " 'mean_test_score': array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),\n",
       " 'std_test_score': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),\n",
       " 'rank_test_score': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)}"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randm.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e179bf39-744e-447e-9dc1-1138b57c37f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e_signed</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5750</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6766</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count  unique  top  freq\n",
       "e_signed                          \n",
       "0          5750       6    5  1205\n",
       "1          6766       6    3  1388"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('e_signed')[\"Cluster\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "22120262-8c1c-4a95-92c4-8b0c5ed08f44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1940"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"income\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8b139b0b-8d67-4020-b465-081e8c3a739d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='e_signed', ylabel='income'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=\"income\",data=df,x='e_signed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f0659b32-a033-4682-b418-e8fd2af944bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='e_signed', ylabel='age'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kOnVut1uGYVgdIyJQZgAggrW2tmrixIlWx+gVzz33nJ577jmrY3Tb+vXr5fF4rI4RETjNBAAAHI2ZGQCIYG63W+vXr7c6xilrbW3VTTfdJEkaOXKkiouLLU506txut9URIgZlBgAimGEYEXcqo7i4OOJ+JpwaTjMBAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHs7TMnHfeeTIMo8M2e/ZsSZJpmvL7/UpMTJTH41FmZqaqqqqsjAwAAGzG0jJTWVmpffv2hbaysjJJ0q233ipJKioqUnFxsUpKSlRZWSmfz6esrCw1NzdbGRsAANiIpWVm0KBB8vl8oW3dunUaMWKEMjIyZJqmlixZovz8fE2ePFmpqalatWqVDh06pNLSUitjAwAAG7HNNTNtbW1avXq17rzzThmGoerqatXV1Sk7Ozt0jMvlUkZGhioqKixMCgAA7CTG6gBH/fGPf9TXX3+t6dOnS5Lq6uokSV6vN+w4r9erPXv2dPk6gUBAgUAg9LipqannwwIAANuwzczMihUrNHHiRCUmJoaNG4YR9tg0zQ5jxyosLFRCQkJoS0pK6pW8AADAHmxRZvbs2aM33nhDM2bMCI35fD5J/5yhOaq+vr7DbM2x5s+fr8bGxtBWW1vbO6EBAIAt2KLMrFy5Uuecc46uu+660FhycrJ8Pl/oE07SketqysvLlZ6e3uVruVwuxcfHh20AACByWX7NTHt7u1auXKlp06YpJuafcQzDUG5urgoKCpSSkqKUlBQVFBQoNjZWOTk5FiYGAAB2YnmZeeONN1RTU6M777yzw768vDy1tLRo1qxZamhoUFpamjZt2qS4uDgLkgIAADuyvMxkZ2fLNM1O9xmGIb/fL7/f37ehAACAY9jimhkAAIDuoswAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHo8wAAABHs7zMfPHFF7r99ts1YMAAxcbG6tJLL9V7770X2m+apvx+vxITE+XxeJSZmamqqioLEwMAADuxtMw0NDToRz/6kc444wytX79eu3bt0n/8x3/o7LPPDh1TVFSk4uJilZSUqLKyUj6fT1lZWWpubrYuOAAAsI0YK7/5okWLlJSUpJUrV4bGzjvvvNDXpmlqyZIlys/P1+TJkyVJq1atktfrVWlpqWbOnNnXkQEAgM1YOjPz2muvaezYsbr11lt1zjnnaMyYMXr22WdD+6urq1VXV6fs7OzQmMvlUkZGhioqKqyIDAAAbMbSMvPZZ59p2bJlSklJ0caNG3X33Xfr3nvv1QsvvCBJqqurkyR5vd6w53m93tC+4wUCATU1NYVtAAAgcll6mqm9vV1jx45VQUGBJGnMmDGqqqrSsmXL9Itf/CJ0nGEYYc8zTbPD2FGFhYV69NFHey80AACwFUtnZgYPHqxRo0aFjY0cOVI1NTWSJJ/PJ0kdZmHq6+s7zNYcNX/+fDU2Noa22traXkgOAADs4pTKzKeffqqNGzeqpaVF0pEZk+/jRz/6kXbv3h029vHHH+vcc8+VJCUnJ8vn86msrCy0v62tTeXl5UpPT+/0NV0ul+Lj48M2AAAQubpVZg4cOKBrr71W559/vn76059q3759kqQZM2bo/vvvP+nXue+++7RlyxYVFBTo008/VWlpqZYvX67Zs2dLOnJ6KTc3VwUFBVq7dq0+/PBDTZ8+XbGxscrJyelOdAAAEGG6VWbuu+8+xcTEqKamRrGxsaHx2267TRs2bDjp1xk3bpzWrl2rl19+WampqVqwYIGWLFmiqVOnho7Jy8tTbm6uZs2apbFjx+qLL77Qpk2bFBcX153oAAAgwnTrAuBNmzZp48aNGjp0aNh4SkqK9uzZ871ea9KkSZo0aVKX+w3DkN/vl9/v705UAAAQ4bo1M3Pw4MGwGZmjvvrqK7lcrlMOBQAAcLK6VWauuuqq0Fow0pHZk/b2di1evFhXX311j4UDAAD4Lt06zbR48WJlZmZq69atamtrU15enqqqqvSPf/xDf/vb33o6IwAAQJe6NTMzatQo7dixQ+PHj1dWVpYOHjyoyZMna9u2bRoxYkRPZwQAAOhSt1cA9vl8rLQLAAAs160ys2PHjk7HDcOQ2+3WsGHDuBAYAAD0iW6VmUsvvTR0b6Sjq/4ee6+kM844Q7fddpueeeYZud3uHogJAADQuW5dM7N27VqlpKRo+fLl+uCDD7R9+3YtX75cF1xwgUpLS7VixQq9+eabevDBB3s6LwAAQJhuzcwsXLhQS5cu1U9+8pPQ2OjRozV06FA99NBDevfdd3XWWWfp/vvv15NPPtljYQEAAI7XrZmZnTt3hm4Geaxzzz1XO3fulHTkVNTRezYBAAD0lm6VmQsvvFBPPPGE2traQmPffvutnnjiCV144YWSpC+++EJer7dnUgIAAHShW6eZfv/73+tnP/uZhg4dqtGjR8swDO3YsUPBYFDr1q2TJH322WeaNWtWj4YFAAA4XrfKTHp6uj7//HOtXr1aH3/8sUzT1C233KKcnJzQ3azvuOOOHg0KAADQmW4vmtevXz9dddVVOu+880Knm/7nf/5HkvSzn/2sZ9IBAAB8h26Vmc8++0w33XSTdu7cKcMwZJpm2DozwWCwxwICAACcSLcuAJ4zZ46Sk5O1f/9+xcbG6sMPP1R5ebnGjh2rzZs393BEAACArnVrZuZ///d/9eabb2rQoEGKiopSdHS0rrjiChUWFuree+/Vtm3bejonAABAp7o1MxMMBtWvXz9J0sCBA/Xll19KOrLOzO7du3suHQAAwHfo1sxMamqqduzYoeHDhystLU1FRUU688wztXz5cg0fPrynMwIAAHSpW2XmwQcf1MGDByVJjz/+uCZNmqQrr7xSAwYM0CuvvNKjAQEAAE6kW2Xm2HsyDR8+XLt27dI//vEP/eAHPwj7VBMAAEBv6/Y6M8fr379/T70UAADASeuxMnO6Mk1Tra2tVseAFPb3wN+JfbjdbmZsAfQqyswpam1t1cSJE62OgePcdNNNVkfA/7d+/Xp5PB6rYwCIYN36aDYAAIBdMDPTg765dIrMKP5ILWOaUvvhI19HxUic2rCM0X5Y/ba/bHUMAKcJfvP2IDMqRoo+w+oYp7kzrQ4ASabVAQCcVjjNBAAAHI0yAwAAHI0yAwAAHI0yAwAAHI0yAwAAHM3SMuP3+2UYRtjm8/lC+03TlN/vV2JiojwejzIzM1VVVWVhYgAAYDeWz8xcdNFF2rdvX2jbuXNnaF9RUZGKi4tVUlKiyspK+Xw+ZWVlqbm52cLEAADATiwvMzExMfL5fKFt0KBBko7MyixZskT5+fmaPHmyUlNTtWrVKh06dEilpaUWpwYAAHZh+aJ5n3zyiRITE+VyuZSWlqaCggINHz5c1dXVqqurU3Z2duhYl8uljIwMVVRUaObMmRamBnA64Eay9sGNZO3JLjeStbTMpKWl6YUXXtD555+v/fv36/HHH1d6erqqqqpUV1cnSfJ6vWHP8Xq92rNnT5evGQgEFAgEQo+bmpp6JzyAiMeNZO2JG8nah11uJGtpmTn2H4mLL75YEyZM0IgRI7Rq1SpdfvnlktSh8ZmmecIWWFhYqEcffbR3AgMAANux/DTTsc466yxdfPHF+uSTT3TjjTdKkurq6jR48ODQMfX19R1ma441f/58zZ07N/S4qalJSUlJvZYZwOmh5Ip/yBXNXaesYppSW/uRr8+M4j6yVgoEDd3zdn+rY4SxVZkJBAL66KOPdOWVVyo5OVk+n09lZWUaM2aMJKmtrU3l5eVatGhRl6/hcrnkcrn6KjKA04Qr2pQr2uoUpze31QHw/9mv1FtaZh544AFdf/31GjZsmOrr6/X444+rqalJ06ZNk2EYys3NVUFBgVJSUpSSkqKCggLFxsYqJyfHytgAAMBGLC0ze/fu1ZQpU/TVV19p0KBBuvzyy7Vlyxade+65kqS8vDy1tLRo1qxZamhoUFpamjZt2qS4uDgrYwMAABuxtMysWbPmhPsNw5Df75ff7++bQAAAwHEsXzQPAADgVFBmAACAo1FmAACAo1FmAACAo1FmAACAo1FmAACAo1FmAACAo1FmAACAo1FmAACAo1FmAACAo1FmAACAo1FmAACAo1FmAACAo1FmAACAo1FmAACAo1FmAACAo1FmAACAo1FmAACAo8VYHcDpTNP854Pgt9YFAezkmPdC2HsEAHoBZeYUBQKB0NdxH6yxMAlgT4FAQLGxsVbHABDBOM0EAAAcjZmZU+RyuUJfN1/ycyn6DAvTADYR/DY0U3nsewQAegNl5hQZhvHPB9FnUGaA44S9RwCgF3CaCQAAOBplBgAAOBplBgAAOBrXzABAF45dIycQtDAIYCPHvhfsso4UZQYAunDsOlL3vD3AwiSAPdllHSlOMwEAAEdjZgYAunDsGjklVxyQK9rCMIBNBIL/nKm0yzpSlBkA6MKxa+S4okWZAY5jl3WkOM0EAAAcjTIDAAAczTZlprCwUIZhKDc3NzRmmqb8fr8SExPl8XiUmZmpqqoq60ICAADbsUWZqays1PLlyzV69Oiw8aKiIhUXF6ukpESVlZXy+XzKyspSc3OzRUkBAIDdWF5mvvnmG02dOlXPPvusfvCDH4TGTdPUkiVLlJ+fr8mTJys1NVWrVq3SoUOHVFpaamFiAABgJ5aXmdmzZ+u6667TtddeGzZeXV2turo6ZWdnh8ZcLpcyMjJUUVHR5esFAgE1NTWFbQAAIHJZ+tHsNWvW6P3331dlZWWHfXV1dZIkr9cbNu71erVnz54uX7OwsFCPPvpozwYFAAC2ZdnMTG1trebMmaPVq1fL7XZ3edzxn2E3TfOEn2ufP3++GhsbQ1ttbW2PZQYAAPZj2czMe++9p/r6el122WWhsWAwqLfeekslJSXavXu3pCMzNIMHDw4dU19f32G25lgul8s2KxICAIDeZ9nMzI9//GPt3LlT27dvD21jx47V1KlTtX37dg0fPlw+n09lZWWh57S1tam8vFzp6elWxQYAADZj2cxMXFycUlNTw8bOOussDRgwIDSem5urgoICpaSkKCUlRQUFBYqNjVVOTo4VkQEAgA3Z+t5MeXl5amlp0axZs9TQ0KC0tDRt2rRJcXFxVkcDAAA2Yasys3nz5rDHhmHI7/fL7/dbkgcAANif5evMAAAAnArKDAAAcDTKDAAAcDTKDAAAcDTKDAAAcDTKDAAAcDTKDAAAcDTKDAAAcDTKDAAAcDTKDAAAcDTKDAAAcDTKDAAAcDTKDAAAcDRb3TXb6Yz2wzKtDnE6M02p/fCRr6NiJMOwNs9pzDj69wAAfYAy04P6bX/Z6ggAAJx2OM0EAAAcjZmZU+R2u7V+/XqrY0BSa2urbrrpJknS2rVr5Xa7LU4ESfw9AOh1lJlTZBiGPB6P1TFwHLfbzd8LAJwmOM0EAAAcjTIDAAAcjTIDAAAcjWtmAOAkBIKGxEpSljFNqa39yNdnRrGMlJWOvBfshTIDACfhnrf7Wx0BQBc4zQQAAByNmRkA6ALrSNkH60jZk13+HigzANAF1pGyJ9aRwvE4zQQAAByNMgMAAByNMgMAAByNMgMAAByNMgMAAByNMgMAABzN0jKzbNkyjR49WvHx8YqPj9eECRPC1nQwTVN+v1+JiYnyeDzKzMxUVVWVhYkBAIDdWFpmhg4dqieeeEJbt27V1q1bdc011+iGG24IFZaioiIVFxerpKRElZWV8vl8ysrKUnNzs5WxAQCAjVhaZq6//nr99Kc/1fnnn6/zzz9fCxcuVL9+/bRlyxaZpqklS5YoPz9fkydPVmpqqlatWqVDhw6ptLTUytgAAMBGbHPNTDAY1Jo1a3Tw4EFNmDBB1dXVqqurU3Z2dugYl8uljIwMVVRUdPk6gUBATU1NYRsAAIhclpeZnTt3ql+/fnK5XLr77ru1du1ajRo1SnV1dZIkr9cbdrzX6w3t60xhYaESEhJCW1JSUq/mBwAA1rK8zFxwwQXavn27tmzZol//+teaNm2adu3aFdpvGEbY8aZpdhg71vz589XY2Bjaamtrey07AACwnuU3mjzzzDP1L//yL5KksWPHqrKyUkuXLtW8efMkSXV1dRo8eHDo+Pr6+g6zNcdyuVxyuVy9GxoAANiG5TMzxzNNU4FAQMnJyfL5fCorKwvta2trU3l5udLT0y1MCAAA7MTSmZl///d/18SJE5WUlKTm5matWbNGmzdv1oYNG2QYhnJzc1VQUKCUlBSlpKSooKBAsbGxysnJsTI2AACwEUvLzP79+3XHHXdo3759SkhI0OjRo7VhwwZlZWVJkvLy8tTS0qJZs2apoaFBaWlp2rRpk+Li4qyMDQAAbMTSMrNixYoT7jcMQ36/X36/v28CAQAAx7HdNTMAAADfB2UGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4GmUGAAA4mqVlprCwUOPGjVNcXJzOOecc3Xjjjdq9e3fYMaZpyu/3KzExUR6PR5mZmaqqqrIoMQAAsBtLy0x5eblmz56tLVu2qKysTIcPH1Z2drYOHjwYOqaoqEjFxcUqKSlRZWWlfD6fsrKy1NzcbGFyAABgFzFWfvMNGzaEPV65cqXOOeccvffee7rqqqtkmqaWLFmi/Px8TZ48WZK0atUqeb1elZaWaubMmVbEBgAANmKra2YaGxslSf3795ckVVdXq66uTtnZ2aFjXC6XMjIyVFFR0elrBAIBNTU1hW0AACBy2abMmKapuXPn6oorrlBqaqokqa6uTpLk9XrDjvV6vaF9xyssLFRCQkJoS0pK6t3gAADAUrYpM/fcc4927Nihl19+ucM+wzDCHpum2WHsqPnz56uxsTG01dbW9kpeAABgD5ZeM3PUb37zG7322mt66623NHTo0NC4z+eTdGSGZvDgwaHx+vr6DrM1R7lcLrlcrt4NDAAAbMPSmRnTNHXPPffo1Vdf1Ztvvqnk5OSw/cnJyfL5fCorKwuNtbW1qby8XOnp6X0dFwAA2JClMzOzZ89WaWmp/vSnPykuLi50HUxCQoI8Ho8Mw1Bubq4KCgqUkpKilJQUFRQUKDY2Vjk5OVZGBwAANmFpmVm2bJkkKTMzM2x85cqVmj59uiQpLy9PLS0tmjVrlhoaGpSWlqZNmzYpLi6uj9MCAAA7srTMmKb5nccYhiG/3y+/39/7gQAAgOPY4gJgWMs0TbW2tlod45Qd+zNEws8jSW63u8tP7gEng/e3ffH+7jmGeTLTIw7W1NSkhIQENTY2Kj4+3uo4ttTS0qKJEydaHQOdWL9+vTwej9Ux4GC8v+2L9/eJfZ/f37ZZZwYAAKA7mJlBxExDm6apQCAg6ch6Q5Ewfcs0NE4V72/74v19Yt/n9zfXzECGYUTMVGdsbKzVEQBb4f2N0wGnmQAAgKNRZgAAgKNRZgAAgKNRZgAAgKNRZgAAgKNRZgAAgKNRZgAAgKNRZgAAgKNRZgAAgKNRZgAAgKNRZgAAgKNRZgAAgKNRZgAAgKNF/F2zTdOUdORW4gAAwBmO/t4++nv8RCK+zDQ3N0uSkpKSLE4CAAC+r+bmZiUkJJzwGMM8mcrjYO3t7fryyy8VFxcnwzCsjoNe1tTUpKSkJNXW1io+Pt7qOAB6EO/v04tpmmpublZiYqKiok58VUzEz8xERUVp6NChVsdAH4uPj+cfOyBC8f4+fXzXjMxRXAAMAAAcjTIDAAAcjTKDiOJyufTII4/I5XJZHQVAD+P9ja5E/AXAAAAgsjEzAwAAHI0yAwAAHI0yAwAAHI0yg4jx1FNPKTk5WW63W5dddpn++te/Wh0JQA946623dP311ysxMVGGYeiPf/yj1ZFgM5QZRIRXXnlFubm5ys/P17Zt23TllVdq4sSJqqmpsToagFN08OBBXXLJJSopKbE6CmyKTzMhIqSlpemHP/yhli1bFhobOXKkbrzxRhUWFlqYDEBPMgxDa9eu1Y033mh1FNgIMzNwvLa2Nr333nvKzs4OG8/OzlZFRYVFqQAAfYUyA8f76quvFAwG5fV6w8a9Xq/q6uosSgUA6CuUGUSM4++Kbpomd0oHgNMAZQaON3DgQEVHR3eYhamvr+8wWwMAiDyUGTjemWeeqcsuu0xlZWVh42VlZUpPT7coFQCgr8RYHQDoCXPnztUdd9yhsWPHasKECVq+fLlqamp09913Wx0NwCn65ptv9Omnn4YeV1dXa/v27erfv7+GDRtmYTLYBR/NRsR46qmnVFRUpH379ik1NVX/+Z//qauuusrqWABO0ebNm3X11Vd3GJ82bZqef/75vg8E26HMAAAAR+OaGQAA4GiUGQAA4GiUGQAA4GiUGQAA4GiUGQAA4GiUGQAA4GiUGQAA4GiUGQAA4GiUGQC29vnnn8swDG3fvt3qKLbKAuCfuDcTAFtLSkrSvn37NHDgQKujALApygwAW4uOjpbP57M6BgAb4zQTgB5jmqaKioo0fPhweTweXXLJJfrDH/7wnc9raGjQ1KlTNWjQIHk8HqWkpGjlypWSOj+189prryklJUUej0dXX321Vq1aJcMw9PXXX0uSnn/+eZ199tnauHGjRo4cqX79+ulf//VftW/fvrDvu3LlSo0cOVJut1sXXnihnnrqqbD97777rsaMGSO3262xY8dq27Ztp/YHBKBXMDMDoMc8+OCDevXVV7Vs2TKlpKTorbfe0u23365BgwYpIyOjy+c99NBD2rVrl9avX6+BAwfq008/VUtLS6fHfv7557rllls0Z84czZgxQ9u2bdMDDzzQ4bhDhw7pySef1IsvvqioqCjdfvvteuCBB/TSSy9Jkp599lk98sgjKikp0ZgxY7Rt2zb96le/0llnnaVp06bp4MGDmjRpkq655hqtXr1a1dXVmjNnTs/8QQHoWSYA9IBvvvnGdLvdZkVFRdj4XXfdZU6ZMuWEz73++uvNX/7yl53uq66uNiWZ27ZtM03TNOfNm2empqaGHZOfn29KMhsaGkzTNM2VK1eaksxPP/00dMzvf/970+v1hh4nJSWZpaWlYa+zYMECc8KECaZpmuYzzzxj9u/f3zx48GBo/7Jly8KyALAHZmYA9Ihdu3aptbVVWVlZYeNtbW0aM2bMCZ/761//WjfffLPef/99ZWdn68Ybb1R6enqnx+7evVvjxo0LGxs/fnyH42JjYzVixIjQ48GDB6u+vl6S9Pe//121tbW666679Ktf/Sp0zOHDh5WQkCBJ+uijj3TJJZcoNjY2tH/ChAkn/DkAWIMyA6BHtLe3S5L+8pe/aMiQIWH7XC7XCZ87ceJE7dmzR3/5y1/0xhtv6Mc//rFmz56tJ598ssOxpmnKMIwOY8c744wzwh4bhhE67mjWZ599VmlpaWHHRUdHd/maAOyJMgOgR4waNUoul0s1NTUnvD6mK4MGDdL06dM1ffp0XXnllfrtb3/baZm58MIL9frrr4eNbd269Xt9L6/XqyFDhuizzz7T1KlTOz1m1KhRevHFF9XS0iKPxyNJ2rJly/f6PgD6BmUGQI+Ii4vTAw88oPvuu0/t7e264oor1NTUpIqKCvXr10/Tpk3r8rkPP/ywLrvsMl100UUKBAJat26dRo4c2emxM2fOVHFxsebNm6e77rpL27dv1/PPPy9JHWZsTsTv9+vee+9VfHy8Jk6cqEAgoK1bt6qhoUFz585VTk6O8vPzddddd+nBBx/U559/3mm5AmA9PpoNoMcsWLBADz/8sAoLCzVy5Ej95Cc/0Z///GclJyef8Hlnnnmm5s+fr9GjR+uqq65SdHS01qxZ0+mxycnJ+sMf/qBXX31Vo0eP1rJly5Sfny/pu09nHWvGjBl67rnn9Pzzz+viiy9WRkaGnn/++VDWfv366c9//rN27dqlMWPGKD8/X4sWLTrp1wfQdwyTE8MAHG7hwoV6+umnVVtba3UUABbgNBMAx3nqqac0btw4DRgwQH/729+0ePFi3XPPPVbHAmARTjMB6HV33323+vXr1+l29913f+/X++STT3TDDTdo1KhRWrBgge6//375/f6eDw7AETjNBKDX1dfXq6mpqdN98fHxOuecc/o4EYBIQpkBAACOxmkmAADgaJQZAADgaJQZAADgaJQZAADgaJQZAADgaJQZAADgaJQZAADgaJQZAADgaP8PrejlRMPvaeAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=\"age\",data=df,x='e_signed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "65a73bb9-7e18-4fe4-9feb-a401be6096c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='e_signed', ylabel='request_ratio'>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=\"age\",data=df,x='e_signed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d82ad327-5f9e-49b7-8c46-5e4f38577643",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='pay_schedule', ylabel='income'>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=\"income\",data=df,x='pay_schedule')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a098f2fe-539c-4a49-bf8d-b3eb52b3f710",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['pay_schedule'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "c4a6fec3-5d27-4665-a07a-584e1fd0cf15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (10012, 23)\n",
      "y_train shape: (10012,)\n",
      "X_test shape: (2504, 23)\n",
      "y_test shape: (2504,)\n"
     ]
    }
   ],
   "source": [
    "target = \"e_signed\"\n",
    "X = df.drop(columns=target)\n",
    "y = df[target]\n",
    "X_train, X_val, y_train, y_val = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42\n",
    ")\n",
    "\n",
    "print(\"X_train shape:\", X_train.shape)\n",
    "print(\"y_train shape:\", y_train.shape)\n",
    "print(\"X_test shape:\", X_val.shape)\n",
    "print(\"y_test shape:\", y_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "93f77892-f34b-4d7e-ab02-70d28d5f37ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/category_encoders/one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for cat_name, class_ in values.iteritems():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.55\n",
      "Validation Accuracy: 0.5674920127795527\n"
     ]
    }
   ],
   "source": [
    "target = \"e_signed\"\n",
    "X = df.drop(columns=target)\n",
    "y = df[target]\n",
    "X_train, X_val, y_train, y_val = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42\n",
    ")\n",
    "model1 = make_pipeline(OneHotEncoder(use_cat_names=True),\n",
    "                       SVC())\n",
    "model1.fit(X_train,y_train)\n",
    "acc_train = accuracy_score(y_train, model1.predict(X_train))\n",
    "acc_val = model1.score(X_val, y_val)\n",
    "\n",
    "print(\"Training Accuracy:\", round(acc_train, 2))\n",
    "print(\"Validation Accuracy:\", acc_val)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "79df9f8b-52e2-4a85-8e91-000999f6a576",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline Accuracy: 0.54\n"
     ]
    }
   ],
   "source": [
    "acc_baseline = y_train.value_counts(normalize=True).max()\n",
    "print(\"Baseline Accuracy:\", round(acc_baseline, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "0b737237-90f8-43e0-b2fa-22ddddb21868",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/category_encoders/one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for cat_name, class_ in values.iteritems():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.68\n",
      "Validation Accuracy: 0.6461661341853036\n"
     ]
    }
   ],
   "source": [
    "model1 = make_pipeline(OneHotEncoder(use_cat_names=True),\n",
    "                       GradientBoostingClassifier(random_state=3))\n",
    "model1.fit(X_train,y_train)\n",
    "acc_train = accuracy_score(y_train, model1.predict(X_train))\n",
    "acc_val = model1.score(X_val, y_val)\n",
    "\n",
    "print(\"Training Accuracy:\", round(acc_train, 2))\n",
    "print(\"Validation Accuracy:\", acc_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6bf2184-c715-44f5-a129-92e5154a641f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = pd.read_csv('test.csv')\n",
    "predictions1 = model1.predict(df_test[X_train.columns])\n",
    "results_df = pd.DataFrame({'e_signed':predictions1}).set_index(df_test['Entry_id'])\n",
    "results_df.to_csv('submissions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6af44578-556b-4390-a20e-8ff6645216f5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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