AI PROJECT SVM ABDUL QUDDUS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ABDUL QUDDUS (64289)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "import plotly.express as px\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "#from xgboost import XGBClassifier\n",
    "#from catboost import CatBoostClassifier\n",
    "import lightgbm as lgb\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_colwidth\", 200)\n",
    "\n",
    "train = pd.read_csv('train.csv')\n",
    "train.drop(\"id\", axis='columns')\n",
    "test = pd.read_csv('test.csv')\n",
    "#test.drop(\"id\", axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>f_29</th>\n",
       "      <th>f_30</th>\n",
       "      <th>target</th>\n",
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       "      <td>-2.230332</td>\n",
       "      <td>-0.545661</td>\n",
       "      <td>1.113173</td>\n",
       "      <td>-1.552175</td>\n",
       "      <td>0.447825</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>2.278315</td>\n",
       "      <td>-0.633658</td>\n",
       "      <td>-1.217077</td>\n",
       "      <td>-3.782194</td>\n",
       "      <td>-0.058316</td>\n",
       "      <td>ACACCADCEB</td>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.681726</td>\n",
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       "      <td>-1.027689</td>\n",
       "      <td>0.810492</td>\n",
       "      <td>-0.609086</td>\n",
       "      <td>0.113965</td>\n",
       "      <td>-0.708660</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.385775</td>\n",
       "      <td>-0.520558</td>\n",
       "      <td>-0.009121</td>\n",
       "      <td>2.788536</td>\n",
       "      <td>-3.703488</td>\n",
       "      <td>AAAEABCKAD</td>\n",
       "      <td>-195.599702</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.118172</td>\n",
       "      <td>-0.587835</td>\n",
       "      <td>-0.804638</td>\n",
       "      <td>2.086822</td>\n",
       "      <td>0.371005</td>\n",
       "      <td>-0.128831</td>\n",
       "      <td>-0.282575</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.572594</td>\n",
       "      <td>-1.653213</td>\n",
       "      <td>1.686035</td>\n",
       "      <td>-2.533098</td>\n",
       "      <td>-0.608601</td>\n",
       "      <td>BDBBAACBCB</td>\n",
       "      <td>210.826205</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.148481</td>\n",
       "      <td>-0.176567</td>\n",
       "      <td>-0.664871</td>\n",
       "      <td>-1.101343</td>\n",
       "      <td>0.467875</td>\n",
       "      <td>0.500117</td>\n",
       "      <td>0.407515</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.912929</td>\n",
       "      <td>-1.430366</td>\n",
       "      <td>2.127649</td>\n",
       "      <td>-3.306784</td>\n",
       "      <td>4.371371</td>\n",
       "      <td>BDBCBBCHFE</td>\n",
       "      <td>-217.211798</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "<p>5 rows × 33 columns</p>\n",
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       "   id      f_00      f_01      f_02      f_03      f_04      f_05      f_06  \\\n",
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       "1   1  1.697021 -1.710322 -2.230332 -0.545661  1.113173 -1.552175  0.447825   \n",
       "2   2  1.681726  0.616746 -1.027689  0.810492 -0.609086  0.113965 -0.708660   \n",
       "3   3 -0.118172 -0.587835 -0.804638  2.086822  0.371005 -0.128831 -0.282575   \n",
       "4   4  1.148481 -0.176567 -0.664871 -1.101343  0.467875  0.500117  0.407515   \n",
       "\n",
       "   f_07  f_08  ...      f_22      f_23      f_24      f_25      f_26  \\\n",
       "0     1     5  ... -2.540739  0.766952 -2.730628 -0.208177  1.363402   \n",
       "1     1     3  ...  2.278315 -0.633658 -1.217077 -3.782194 -0.058316   \n",
       "2     1     0  ... -1.385775 -0.520558 -0.009121  2.788536 -3.703488   \n",
       "3     3     2  ...  0.572594 -1.653213  1.686035 -2.533098 -0.608601   \n",
       "4     3     3  ... -3.912929 -1.430366  2.127649 -3.306784  4.371371   \n",
       "\n",
       "         f_27        f_28  f_29  f_30  target  \n",
       "0  ABABDADBAB   67.609153     0     0       0  \n",
       "1  ACACCADCEB  377.096415     0     0       1  \n",
       "2  AAAEABCKAD -195.599702     0     2       1  \n",
       "3  BDBBAACBCB  210.826205     0     0       1  \n",
       "4  BDBCBBCHFE -217.211798     0     1       1  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.174380</td>\n",
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       "      <td>-0.578898</td>\n",
       "      <td>-1.750931</td>\n",
       "      <td>1.355550</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1.883322</td>\n",
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       "      <th>2</th>\n",
       "      <td>900002</td>\n",
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       "      <td>2.445110</td>\n",
       "      <td>0.246515</td>\n",
       "      <td>0.818248</td>\n",
       "      <td>0.359731</td>\n",
       "      <td>-1.331845</td>\n",
       "      <td>1.358622</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.026098</td>\n",
       "      <td>1.312277</td>\n",
       "      <td>-5.157192</td>\n",
       "      <td>1.714005</td>\n",
       "      <td>0.585032</td>\n",
       "      <td>0.066898</td>\n",
       "      <td>BBACABBKEE</td>\n",
       "      <td>-87.405622</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>900003</td>\n",
       "      <td>0.154053</td>\n",
       "      <td>0.260126</td>\n",
       "      <td>-1.367092</td>\n",
       "      <td>-0.093175</td>\n",
       "      <td>-1.111034</td>\n",
       "      <td>-0.948481</td>\n",
       "      <td>1.119220</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.594532</td>\n",
       "      <td>-3.939475</td>\n",
       "      <td>1.754570</td>\n",
       "      <td>-2.364007</td>\n",
       "      <td>-1.003320</td>\n",
       "      <td>3.893099</td>\n",
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       "      <td>-281.293460</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>900004</td>\n",
       "      <td>-1.651904</td>\n",
       "      <td>-0.424266</td>\n",
       "      <td>-0.667356</td>\n",
       "      <td>-0.322124</td>\n",
       "      <td>-0.089462</td>\n",
       "      <td>0.181705</td>\n",
       "      <td>1.784983</td>\n",
       "      <td>2</td>\n",
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       "      <td>-0.985736</td>\n",
       "      <td>-0.130467</td>\n",
       "      <td>-3.557893</td>\n",
       "      <td>1.210687</td>\n",
       "      <td>1.861884</td>\n",
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       "      <td>0</td>\n",
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      "text/plain": [
       "       Id      f_00      f_01      f_02      f_03      f_04      f_05  \\\n",
       "0  900000  0.442517  0.174380 -0.999816  0.762741  0.186778 -1.074775   \n",
       "1  900001 -0.605598 -0.305715  0.627667 -0.578898 -1.750931  1.355550   \n",
       "2  900002  0.303990  2.445110  0.246515  0.818248  0.359731 -1.331845   \n",
       "3  900003  0.154053  0.260126 -1.367092 -0.093175 -1.111034 -0.948481   \n",
       "4  900004 -1.651904 -0.424266 -0.667356 -0.322124 -0.089462  0.181705   \n",
       "\n",
       "       f_06  f_07  f_08  ...      f_21      f_22      f_23      f_24  \\\n",
       "0  0.501888     6     6  ... -1.006400 -1.193879 -2.435736 -2.427430   \n",
       "1 -0.190911     1     3  ...  2.382405  0.149442  1.883322 -2.848714   \n",
       "2  1.358622     3     3  ... -7.026098  1.312277 -5.157192  1.714005   \n",
       "3  1.119220     0     0  ... -0.594532 -3.939475  1.754570 -2.364007   \n",
       "4  1.784983     2     2  ...  0.084906 -0.985736 -0.130467 -3.557893   \n",
       "\n",
       "       f_25      f_26        f_27        f_28  f_29  f_30  \n",
       "0 -1.966887  5.734205  BAAABADLAC   99.478419     0     0  \n",
       "1 -0.725155  3.194219  AFABBAEGCB  -65.993825     1     0  \n",
       "2  0.585032  0.066898  BBACABBKEE  -87.405622     0     1  \n",
       "3 -1.003320  3.893099  AEBEAACQCC -281.293460     0     0  \n",
       "4  1.210687  1.861884  AEBBBBDABF   25.629415     0     2  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Train data: (900000, 33)\n",
      "Shape of Test data: (700000, 32)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of Train data:', train.shape)\n",
    "print('Shape of Test data:', test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null Values in Train data: False\n",
      "Null Values in Test data: False\n"
     ]
    }
   ],
   "source": [
    "print('Null Values in Train data:', train.isnull().values.any())\n",
    "print('Null Values in Test data:', test.isnull().values.any())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicate Values in Train data: 0\n",
      "Duplicate Values in Test data: 0\n"
     ]
    }
   ],
   "source": [
    "print('Duplicate Values in Train data:', train.duplicated().sum())\n",
    "print('Duplicate Values in Test data:', test.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of Unique elements in Train data:\n",
      "id        900000\n",
      "f_00      900000\n",
      "f_01      900000\n",
      "f_02      900000\n",
      "f_03      900000\n",
      "f_04      900000\n",
      "f_05      900000\n",
      "f_06      900000\n",
      "f_07          16\n",
      "f_08          16\n",
      "f_09          15\n",
      "f_10          15\n",
      "f_11          14\n",
      "f_12          16\n",
      "f_13          13\n",
      "f_14          14\n",
      "f_15          15\n",
      "f_16          16\n",
      "f_17          14\n",
      "f_18          14\n",
      "f_19      900000\n",
      "f_20      900000\n",
      "f_21      900000\n",
      "f_22      900000\n",
      "f_23      900000\n",
      "f_24      900000\n",
      "f_25      900000\n",
      "f_26      900000\n",
      "f_27      741354\n",
      "f_28      900000\n",
      "f_29           2\n",
      "f_30           3\n",
      "target         2\n",
      "dtype: int64\n",
      "------------------------------------\n",
      "No. of Unique elements in Test data:\n",
      "Id      700000\n",
      "f_00    700000\n",
      "f_01    700000\n",
      "f_02    700000\n",
      "f_03    700000\n",
      "f_04    700000\n",
      "f_05    700000\n",
      "f_06    700000\n",
      "f_07        16\n",
      "f_08        14\n",
      "f_09        16\n",
      "f_10        16\n",
      "f_11        15\n",
      "f_12        16\n",
      "f_13        14\n",
      "f_14        13\n",
      "f_15        14\n",
      "f_16        14\n",
      "f_17        14\n",
      "f_18        14\n",
      "f_19    700000\n",
      "f_20    700000\n",
      "f_21    700000\n",
      "f_22    700000\n",
      "f_23    700000\n",
      "f_24    700000\n",
      "f_25    700000\n",
      "f_26    700000\n",
      "f_27    598482\n",
      "f_28    700000\n",
      "f_29         2\n",
      "f_30         3\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('No. of Unique elements in Train data:')\n",
    "print(train.nunique())\n",
    "print('------------------------------------')\n",
    "print('No. of Unique elements in Test data:')\n",
    "print(test.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking the type of our data:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "id          int64\n",
       "f_00      float64\n",
       "f_01      float64\n",
       "f_02      float64\n",
       "f_03      float64\n",
       "f_04      float64\n",
       "f_05      float64\n",
       "f_06      float64\n",
       "f_07        int64\n",
       "f_08        int64\n",
       "f_09        int64\n",
       "f_10        int64\n",
       "f_11        int64\n",
       "f_12        int64\n",
       "f_13        int64\n",
       "f_14        int64\n",
       "f_15        int64\n",
       "f_16        int64\n",
       "f_17        int64\n",
       "f_18        int64\n",
       "f_19      float64\n",
       "f_20      float64\n",
       "f_21      float64\n",
       "f_22      float64\n",
       "f_23      float64\n",
       "f_24      float64\n",
       "f_25      float64\n",
       "f_26      float64\n",
       "f_27       object\n",
       "f_28      float64\n",
       "f_29        int64\n",
       "f_30        int64\n",
       "target      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Checking the type of our data:')\n",
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.513512\n",
       "1    0.486488\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['target'].value_counts(normalize=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1800 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(4,4, figsize = (30,25) , sharey= True)\n",
    "ax = ax.ravel()\n",
    "\n",
    "for i,col in enumerate(train.dtypes[train.dtypes ==\"float64\"].index):\n",
    "    train[col].plot(ax = ax[i], kind = \"hist\", bins = 100, color = \"r\")\n",
    "    ax[i].set_title(f\"{col}\")\n",
    "fig.suptitle(\"Histogram of Continous columns\", fontsize=35)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Id</th>\n",
       "      <th>f_00</th>\n",
       "      <th>f_01</th>\n",
       "      <th>f_02</th>\n",
       "      <th>f_03</th>\n",
       "      <th>f_04</th>\n",
       "      <th>f_05</th>\n",
       "      <th>f_06</th>\n",
       "      <th>f_07</th>\n",
       "      <th>f_08</th>\n",
       "      <th>...</th>\n",
       "      <th>f_22</th>\n",
       "      <th>f_23</th>\n",
       "      <th>f_24</th>\n",
       "      <th>f_25</th>\n",
       "      <th>f_26</th>\n",
       "      <th>f_27</th>\n",
       "      <th>f_28</th>\n",
       "      <th>f_29</th>\n",
       "      <th>f_30</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>900000</td>\n",
       "      <td>0.442517</td>\n",
       "      <td>0.174380</td>\n",
       "      <td>-0.999816</td>\n",
       "      <td>0.762741</td>\n",
       "      <td>0.186778</td>\n",
       "      <td>-1.074775</td>\n",
       "      <td>0.501888</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.193879</td>\n",
       "      <td>-2.435736</td>\n",
       "      <td>-2.427430</td>\n",
       "      <td>-1.966887</td>\n",
       "      <td>5.734205</td>\n",
       "      <td>BAAABADLAC</td>\n",
       "      <td>99.478419</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>900001</td>\n",
       "      <td>-0.605598</td>\n",
       "      <td>-0.305715</td>\n",
       "      <td>0.627667</td>\n",
       "      <td>-0.578898</td>\n",
       "      <td>-1.750931</td>\n",
       "      <td>1.355550</td>\n",
       "      <td>-0.190911</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.149442</td>\n",
       "      <td>1.883322</td>\n",
       "      <td>-2.848714</td>\n",
       "      <td>-0.725155</td>\n",
       "      <td>3.194219</td>\n",
       "      <td>AFABBAEGCB</td>\n",
       "      <td>-65.993825</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>900002</td>\n",
       "      <td>0.303990</td>\n",
       "      <td>2.445110</td>\n",
       "      <td>0.246515</td>\n",
       "      <td>0.818248</td>\n",
       "      <td>0.359731</td>\n",
       "      <td>-1.331845</td>\n",
       "      <td>1.358622</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1.312277</td>\n",
       "      <td>-5.157192</td>\n",
       "      <td>1.714005</td>\n",
       "      <td>0.585032</td>\n",
       "      <td>0.066898</td>\n",
       "      <td>BBACABBKEE</td>\n",
       "      <td>-87.405622</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>900003</td>\n",
       "      <td>0.154053</td>\n",
       "      <td>0.260126</td>\n",
       "      <td>-1.367092</td>\n",
       "      <td>-0.093175</td>\n",
       "      <td>-1.111034</td>\n",
       "      <td>-0.948481</td>\n",
       "      <td>1.119220</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.939475</td>\n",
       "      <td>1.754570</td>\n",
       "      <td>-2.364007</td>\n",
       "      <td>-1.003320</td>\n",
       "      <td>3.893099</td>\n",
       "      <td>AEBEAACQCC</td>\n",
       "      <td>-281.293460</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>900004</td>\n",
       "      <td>-1.651904</td>\n",
       "      <td>-0.424266</td>\n",
       "      <td>-0.667356</td>\n",
       "      <td>-0.322124</td>\n",
       "      <td>-0.089462</td>\n",
       "      <td>0.181705</td>\n",
       "      <td>1.784983</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.985736</td>\n",
       "      <td>-0.130467</td>\n",
       "      <td>-3.557893</td>\n",
       "      <td>1.210687</td>\n",
       "      <td>1.861884</td>\n",
       "      <td>AEBBBBDABF</td>\n",
       "      <td>25.629415</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Id      f_00      f_01      f_02      f_03      f_04      f_05  \\\n",
       "0  900000  0.442517  0.174380 -0.999816  0.762741  0.186778 -1.074775   \n",
       "1  900001 -0.605598 -0.305715  0.627667 -0.578898 -1.750931  1.355550   \n",
       "2  900002  0.303990  2.445110  0.246515  0.818248  0.359731 -1.331845   \n",
       "3  900003  0.154053  0.260126 -1.367092 -0.093175 -1.111034 -0.948481   \n",
       "4  900004 -1.651904 -0.424266 -0.667356 -0.322124 -0.089462  0.181705   \n",
       "\n",
       "       f_06  f_07  f_08  ...      f_22      f_23      f_24      f_25  \\\n",
       "0  0.501888     6     6  ... -1.193879 -2.435736 -2.427430 -1.966887   \n",
       "1 -0.190911     1     3  ...  0.149442  1.883322 -2.848714 -0.725155   \n",
       "2  1.358622     3     3  ...  1.312277 -5.157192  1.714005  0.585032   \n",
       "3  1.119220     0     0  ... -3.939475  1.754570 -2.364007 -1.003320   \n",
       "4  1.784983     2     2  ... -0.985736 -0.130467 -3.557893  1.210687   \n",
       "\n",
       "       f_26        f_27        f_28  f_29  f_30  target  \n",
       "0  5.734205  BAAABADLAC   99.478419     0     0      -1  \n",
       "1  3.194219  AFABBAEGCB  -65.993825     1     0      -1  \n",
       "2  0.066898  BBACABBKEE  -87.405622     0     1      -1  \n",
       "3  3.893099  AEBEAACQCC -281.293460     0     0      -1  \n",
       "4  1.861884  AEBBBBDABF   25.629415     0     2      -1  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['target'] = -1\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.520558</td>\n",
       "      <td>-0.009121</td>\n",
       "      <td>2.788536</td>\n",
       "      <td>-3.703488</td>\n",
       "      <td>AAAEABCKAD</td>\n",
       "      <td>-195.599702</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
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       "      <td>-0.587835</td>\n",
       "      <td>-0.804638</td>\n",
       "      <td>2.086822</td>\n",
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       "      <td>210.826205</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
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       "      <td>-0.176567</td>\n",
       "      <td>-0.664871</td>\n",
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       "      <td>0.467875</td>\n",
       "      <td>0.500117</td>\n",
       "      <td>0.407515</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
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       "      <td>2.127649</td>\n",
       "      <td>-3.306784</td>\n",
       "      <td>4.371371</td>\n",
       "      <td>BDBCBBCHFE</td>\n",
       "      <td>-217.211798</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "    id      f_00      f_01      f_02      f_03      f_04      f_05      f_06  \\\n",
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       "\n",
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       "4     3     3  ... -1.430366  2.127649 -3.306784  4.371371  BDBCBBCHFE   \n",
       "\n",
       "         f_28  f_29  f_30  target  Id  \n",
       "0   67.609153     0     0       0 NaN  \n",
       "1  377.096415     0     0       1 NaN  \n",
       "2 -195.599702     0     2       1 NaN  \n",
       "3  210.826205     0     0       1 NaN  \n",
       "4 -217.211798     0     1       1 NaN  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([train,test])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id         900000\n",
       "f_00      1600000\n",
       "f_01      1600000\n",
       "f_02      1600000\n",
       "f_03      1600000\n",
       "f_04      1600000\n",
       "f_05      1600000\n",
       "f_06      1600000\n",
       "f_07           17\n",
       "f_08           16\n",
       "f_09           16\n",
       "f_10           16\n",
       "f_11           15\n",
       "f_12           17\n",
       "f_13           14\n",
       "f_14           14\n",
       "f_15           15\n",
       "f_16           16\n",
       "f_17           15\n",
       "f_18           14\n",
       "f_19      1600000\n",
       "f_20      1600000\n",
       "f_21      1600000\n",
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       "f_23      1600000\n",
       "f_24      1600000\n",
       "f_25      1600000\n",
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       "f_27      1181880\n",
       "f_28      1600000\n",
       "f_29            2\n",
       "f_30            3\n",
       "target          3\n",
       "Id         700000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df.query(\"target != -1\").reset_index(drop=True)\n",
    "test = df.query(\"target == -1\").reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.697021</td>\n",
       "      <td>-1.710322</td>\n",
       "      <td>-2.230332</td>\n",
       "      <td>-0.545661</td>\n",
       "      <td>1.113173</td>\n",
       "      <td>-1.552175</td>\n",
       "      <td>0.447825</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.633658</td>\n",
       "      <td>-1.217077</td>\n",
       "      <td>-3.782194</td>\n",
       "      <td>-0.058316</td>\n",
       "      <td>ACACCADCEB</td>\n",
       "      <td>377.096415</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.681726</td>\n",
       "      <td>0.616746</td>\n",
       "      <td>-1.027689</td>\n",
       "      <td>0.810492</td>\n",
       "      <td>-0.609086</td>\n",
       "      <td>0.113965</td>\n",
       "      <td>-0.708660</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.520558</td>\n",
       "      <td>-0.009121</td>\n",
       "      <td>2.788536</td>\n",
       "      <td>-3.703488</td>\n",
       "      <td>AAAEABCKAD</td>\n",
       "      <td>-195.599702</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>-0.118172</td>\n",
       "      <td>-0.587835</td>\n",
       "      <td>-0.804638</td>\n",
       "      <td>2.086822</td>\n",
       "      <td>0.371005</td>\n",
       "      <td>-0.128831</td>\n",
       "      <td>-0.282575</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.653213</td>\n",
       "      <td>1.686035</td>\n",
       "      <td>-2.533098</td>\n",
       "      <td>-0.608601</td>\n",
       "      <td>BDBBAACBCB</td>\n",
       "      <td>210.826205</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.148481</td>\n",
       "      <td>-0.176567</td>\n",
       "      <td>-0.664871</td>\n",
       "      <td>-1.101343</td>\n",
       "      <td>0.467875</td>\n",
       "      <td>0.500117</td>\n",
       "      <td>0.407515</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.430366</td>\n",
       "      <td>2.127649</td>\n",
       "      <td>-3.306784</td>\n",
       "      <td>4.371371</td>\n",
       "      <td>BDBCBBCHFE</td>\n",
       "      <td>-217.211798</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    id      f_00      f_01      f_02      f_03      f_04      f_05      f_06  \\\n",
       "0  0.0 -1.373246  0.238887 -0.243376  0.567405 -0.647715  0.839326  0.113133   \n",
       "1  1.0  1.697021 -1.710322 -2.230332 -0.545661  1.113173 -1.552175  0.447825   \n",
       "2  2.0  1.681726  0.616746 -1.027689  0.810492 -0.609086  0.113965 -0.708660   \n",
       "3  3.0 -0.118172 -0.587835 -0.804638  2.086822  0.371005 -0.128831 -0.282575   \n",
       "4  4.0  1.148481 -0.176567 -0.664871 -1.101343  0.467875  0.500117  0.407515   \n",
       "\n",
       "   f_07  f_08  ...      f_23      f_24      f_25      f_26        f_27  \\\n",
       "0     1     5  ...  0.766952 -2.730628 -0.208177  1.363402  ABABDADBAB   \n",
       "1     1     3  ... -0.633658 -1.217077 -3.782194 -0.058316  ACACCADCEB   \n",
       "2     1     0  ... -0.520558 -0.009121  2.788536 -3.703488  AAAEABCKAD   \n",
       "3     3     2  ... -1.653213  1.686035 -2.533098 -0.608601  BDBBAACBCB   \n",
       "4     3     3  ... -1.430366  2.127649 -3.306784  4.371371  BDBCBBCHFE   \n",
       "\n",
       "         f_28  f_29  f_30  target  Id  \n",
       "0   67.609153     0     0       0 NaN  \n",
       "1  377.096415     0     0       1 NaN  \n",
       "2 -195.599702     0     2       1 NaN  \n",
       "3  210.826205     0     0       1 NaN  \n",
       "4 -217.211798     0     1       1 NaN  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>f_00</th>\n",
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       "      <th>f_02</th>\n",
       "      <th>f_03</th>\n",
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       "      <th>f_07</th>\n",
       "      <th>f_08</th>\n",
       "      <th>...</th>\n",
       "      <th>f_21</th>\n",
       "      <th>f_22</th>\n",
       "      <th>f_23</th>\n",
       "      <th>f_24</th>\n",
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       "      <th>f_29</th>\n",
       "      <th>f_30</th>\n",
       "      <th>Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0.442517</td>\n",
       "      <td>0.174380</td>\n",
       "      <td>-0.999816</td>\n",
       "      <td>0.762741</td>\n",
       "      <td>0.186778</td>\n",
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       "      <td>-1.193879</td>\n",
       "      <td>-2.435736</td>\n",
       "      <td>-2.427430</td>\n",
       "      <td>-1.966887</td>\n",
       "      <td>5.734205</td>\n",
       "      <td>99.478419</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.605598</td>\n",
       "      <td>-0.305715</td>\n",
       "      <td>0.627667</td>\n",
       "      <td>-0.578898</td>\n",
       "      <td>-1.750931</td>\n",
       "      <td>1.355550</td>\n",
       "      <td>-0.190911</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2.382405</td>\n",
       "      <td>0.149442</td>\n",
       "      <td>1.883322</td>\n",
       "      <td>-2.848714</td>\n",
       "      <td>-0.725155</td>\n",
       "      <td>3.194219</td>\n",
       "      <td>-65.993825</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>900001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.303990</td>\n",
       "      <td>2.445110</td>\n",
       "      <td>0.246515</td>\n",
       "      <td>0.818248</td>\n",
       "      <td>0.359731</td>\n",
       "      <td>-1.331845</td>\n",
       "      <td>1.358622</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.026098</td>\n",
       "      <td>1.312277</td>\n",
       "      <td>-5.157192</td>\n",
       "      <td>1.714005</td>\n",
       "      <td>0.585032</td>\n",
       "      <td>0.066898</td>\n",
       "      <td>-87.405622</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>900002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.154053</td>\n",
       "      <td>0.260126</td>\n",
       "      <td>-1.367092</td>\n",
       "      <td>-0.093175</td>\n",
       "      <td>-1.111034</td>\n",
       "      <td>-0.948481</td>\n",
       "      <td>1.119220</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.594532</td>\n",
       "      <td>-3.939475</td>\n",
       "      <td>1.754570</td>\n",
       "      <td>-2.364007</td>\n",
       "      <td>-1.003320</td>\n",
       "      <td>3.893099</td>\n",
       "      <td>-281.293460</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>900003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.651904</td>\n",
       "      <td>-0.424266</td>\n",
       "      <td>-0.667356</td>\n",
       "      <td>-0.322124</td>\n",
       "      <td>-0.089462</td>\n",
       "      <td>0.181705</td>\n",
       "      <td>1.784983</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.084906</td>\n",
       "      <td>-0.985736</td>\n",
       "      <td>-0.130467</td>\n",
       "      <td>-3.557893</td>\n",
       "      <td>1.210687</td>\n",
       "      <td>1.861884</td>\n",
       "      <td>25.629415</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>900004.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id      f_00      f_01      f_02      f_03      f_04      f_05      f_06  \\\n",
       "0 NaN  0.442517  0.174380 -0.999816  0.762741  0.186778 -1.074775  0.501888   \n",
       "1 NaN -0.605598 -0.305715  0.627667 -0.578898 -1.750931  1.355550 -0.190911   \n",
       "2 NaN  0.303990  2.445110  0.246515  0.818248  0.359731 -1.331845  1.358622   \n",
       "3 NaN  0.154053  0.260126 -1.367092 -0.093175 -1.111034 -0.948481  1.119220   \n",
       "4 NaN -1.651904 -0.424266 -0.667356 -0.322124 -0.089462  0.181705  1.784983   \n",
       "\n",
       "   f_07  f_08  ...      f_21      f_22      f_23      f_24      f_25  \\\n",
       "0     6     6  ... -1.006400 -1.193879 -2.435736 -2.427430 -1.966887   \n",
       "1     1     3  ...  2.382405  0.149442  1.883322 -2.848714 -0.725155   \n",
       "2     3     3  ... -7.026098  1.312277 -5.157192  1.714005  0.585032   \n",
       "3     0     0  ... -0.594532 -3.939475  1.754570 -2.364007 -1.003320   \n",
       "4     2     2  ...  0.084906 -0.985736 -0.130467 -3.557893  1.210687   \n",
       "\n",
       "       f_26        f_28  f_29  f_30        Id  \n",
       "0  5.734205   99.478419     0     0  900000.0  \n",
       "1  3.194219  -65.993825     1     0  900001.0  \n",
       "2  0.066898  -87.405622     0     1  900002.0  \n",
       "3  3.893099 -281.293460     0     0  900003.0  \n",
       "4  1.861884   25.629415     0     2  900004.0  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = test.drop(['f_27','target'], axis=1)\n",
    "\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['target'].value_counts(normalize=True)\n",
    "X = train.drop(['Id','target'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop(['f_27','target'], axis=1)\n",
    "y = train['target']\n",
    "\n",
    "X_train,X_valid,y_train,y_valid = train_test_split(X,y,random_state=42,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(720000, 32) (720000,)\n",
      "(180000, 32) (180000,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape,y_train.shape)\n",
    "print(X_valid.shape,y_valid.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              id      f_00      f_01      f_02      f_03      f_04      f_05  \\\n",
      "394657  394657.0  0.889025  0.156984 -0.463970 -0.359268 -0.318893  0.278296   \n",
      "517437  517437.0 -0.566257  0.835972  1.520039 -2.525316  0.101200  0.039224   \n",
      "208118  208118.0 -0.495783  0.578138 -1.639720 -1.013108  0.666244  0.889416   \n",
      "626437  626437.0 -1.504166  0.207928  0.504399 -0.505581  0.360182  0.351198   \n",
      "699970  699970.0 -1.166926  1.774299  0.527605  0.117530 -0.080474  0.408386   \n",
      "...          ...       ...       ...       ...       ...       ...       ...   \n",
      "259178  259178.0  0.485585  1.197382 -1.739263  0.107500  1.207696  0.716411   \n",
      "365838  365838.0 -0.885035  0.865033  1.736442 -0.878123 -0.309957 -0.434530   \n",
      "131932  131932.0  0.341007  1.459316  2.308001 -0.066509 -1.895432  0.130976   \n",
      "671155  671155.0 -1.058976 -0.324759 -0.484320  0.143031 -0.440077 -0.780925   \n",
      "121958  121958.0 -0.164670  0.262780  0.648553  0.061342 -1.545156 -0.398207   \n",
      "\n",
      "            f_06  f_07  f_08  ...      f_21      f_22      f_23      f_24  \\\n",
      "394657 -0.026423     2     3  ... -0.551347 -0.501219 -3.300893 -0.813057   \n",
      "517437 -1.060842     5     5  ...  0.240548 -2.025923  1.560714 -3.095291   \n",
      "208118  0.157305     1     4  ...  4.442386 -1.768494 -2.852431  2.556138   \n",
      "626437 -1.486638     1     0  ... -2.138314  3.732876 -3.799764 -0.313938   \n",
      "699970 -1.100115     2     2  ... -0.911261  0.370423 -3.969640 -2.005435   \n",
      "...          ...   ...   ...  ...       ...       ...       ...       ...   \n",
      "259178  0.727497     1     0  ... -0.991202 -2.324838 -1.371476  1.538016   \n",
      "365838  0.406020     1     4  ... -0.924428  1.386583 -4.142607  1.174160   \n",
      "131932  0.104697     0     2  ... -0.690553 -1.558742 -1.525733  0.574304   \n",
      "671155 -0.105343     3     2  ... -0.303323  0.224903  3.239380 -2.193098   \n",
      "121958 -0.613869     4     2  ...  0.595438 -2.305073 -3.690293  3.018154   \n",
      "\n",
      "            f_25      f_26        f_28  f_29  f_30  Id  \n",
      "394657  6.812184  1.238560 -289.959313     1     2 NaN  \n",
      "517437  0.401258 -0.597129  -55.055301     0     0 NaN  \n",
      "208118 -1.051545  2.965414  606.826875     1     0 NaN  \n",
      "626437  0.367115  4.343333  -10.355052     0     2 NaN  \n",
      "699970  2.167104 -0.899756  -64.846260     1     2 NaN  \n",
      "...          ...       ...         ...   ...   ...  ..  \n",
      "259178 -0.253654  1.797116   42.841962     0     0 NaN  \n",
      "365838  0.956354  1.725719  -73.475808     0     1 NaN  \n",
      "131932 -0.153414  3.816025  436.943005     0     1 NaN  \n",
      "671155 -0.650998  0.313314 -315.658876     1     2 NaN  \n",
      "121958  2.133606  0.867956   -3.620835     0     0 NaN  \n",
      "\n",
      "[720000 rows x 32 columns]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "print(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-38-8424c8dc7231>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mclassification_rbf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSVC\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkernel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'rbf'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mclassification_rbf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    158\u001b[0m             \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    159\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 160\u001b[1;33m             X, y = self._validate_data(X, y, dtype=np.float64,\n\u001b[0m\u001b[0;32m    161\u001b[0m                                        \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'C'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'csr'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    162\u001b[0m                                        accept_large_sparse=False)\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    430\u001b[0m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    431\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 432\u001b[1;33m                 \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    433\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    434\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m    793\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"y cannot be None\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    794\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 795\u001b[1;33m     X = check_array(X, accept_sparse=accept_sparse,\n\u001b[0m\u001b[0;32m    796\u001b[0m                     \u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    797\u001b[0m                     \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    642\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    643\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 644\u001b[1;33m             _assert_all_finite(array,\n\u001b[0m\u001b[0;32m    645\u001b[0m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0;32m    646\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[0;32m     94\u001b[0m                 not allow_nan and not np.isfinite(X).all()):\n\u001b[0;32m     95\u001b[0m             \u001b[0mtype_err\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'infinity'\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mallow_nan\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m'NaN, infinity'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 96\u001b[1;33m             raise ValueError(\n\u001b[0m\u001b[0;32m     97\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m                     (type_err,\n",
      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
     ]
    }
   ],
   "source": [
    "classification_rbf = SVC(kernel='rbf')\n",
    "classification_rbf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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