santander-data-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Santander Customer Data Analysis\n",
    "\n",
    "##### Conducting feature selection on the customer data from the Spanish bank Santander\n",
    "\n",
    "The data used is available on Kaggle (https://www.kaggle.com/c/santander-customer-transaction-prediction/data).\n",
    "The data has been anonymized, the target column to predict is __target__.\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction and goals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of this study is to apply some feature engineering to a customer data set in order to predict whether customers will make a specific transaction in the future."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import lightgbm as lgb\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['test.csv', 'test.txt', 'train.csv']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('./data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('./data/train.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>15.4305</td>\n",
       "      <td>2.0339</td>\n",
       "      <td>8.1267</td>\n",
       "      <td>8.7889</td>\n",
       "      <td>18.3560</td>\n",
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       "      <td>6.9427</td>\n",
       "      <td>14.6155</td>\n",
       "      <td>...</td>\n",
       "      <td>2.9057</td>\n",
       "      <td>9.7905</td>\n",
       "      <td>1.6704</td>\n",
       "      <td>1.6858</td>\n",
       "      <td>21.6042</td>\n",
       "      <td>3.1417</td>\n",
       "      <td>-6.5213</td>\n",
       "      <td>8.2675</td>\n",
       "      <td>14.7222</td>\n",
       "      <td>0.3965</td>\n",
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       "      <td>17.9697</td>\n",
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       "      <td>12.2772</td>\n",
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       "      <td>5.9405</td>\n",
       "      <td>19.2514</td>\n",
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       "      <td>-1.4905</td>\n",
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       "      <td>-0.1508</td>\n",
       "      <td>9.1942</td>\n",
       "      <td>13.2876</td>\n",
       "      <td>-1.5121</td>\n",
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       "      <td>4.2916</td>\n",
       "      <td>11.1355</td>\n",
       "      <td>-8.0198</td>\n",
       "      <td>6.1961</td>\n",
       "      <td>12.0771</td>\n",
       "      <td>...</td>\n",
       "      <td>8.7830</td>\n",
       "      <td>6.4521</td>\n",
       "      <td>3.5325</td>\n",
       "      <td>0.1777</td>\n",
       "      <td>18.3314</td>\n",
       "      <td>0.5845</td>\n",
       "      <td>9.1104</td>\n",
       "      <td>9.1143</td>\n",
       "      <td>10.8869</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
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       "      <td>8.6543</td>\n",
       "      <td>0.8310</td>\n",
       "      <td>5.6890</td>\n",
       "      <td>22.3262</td>\n",
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       "      <th>8</th>\n",
       "      <td>train_8</td>\n",
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       "      <td>2.4426</td>\n",
       "      <td>13.9307</td>\n",
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       "      <td>8.8014</td>\n",
       "      <td>6.1630</td>\n",
       "      <td>4.4514</td>\n",
       "      <td>10.1854</td>\n",
       "      <td>...</td>\n",
       "      <td>1.4298</td>\n",
       "      <td>14.7510</td>\n",
       "      <td>1.6395</td>\n",
       "      <td>1.4181</td>\n",
       "      <td>14.8370</td>\n",
       "      <td>-1.9940</td>\n",
       "      <td>-1.0733</td>\n",
       "      <td>8.1975</td>\n",
       "      <td>19.5114</td>\n",
       "      <td>4.8453</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>train_9</td>\n",
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       "      <td>12.5088</td>\n",
       "      <td>1.9743</td>\n",
       "      <td>8.8960</td>\n",
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       "      <td>13.6043</td>\n",
       "      <td>-16.2859</td>\n",
       "      <td>6.0637</td>\n",
       "      <td>16.8410</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5543</td>\n",
       "      <td>6.3160</td>\n",
       "      <td>1.0371</td>\n",
       "      <td>3.6885</td>\n",
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       "      <td>0.4467</td>\n",
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       "      <td>16.2375</td>\n",
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       "   ID_code  target    var_0   var_1    var_2   var_3    var_4    var_5  \\\n",
       "0  train_0       0   8.9255 -6.7863  11.9081  5.0930  11.4607  -9.2834   \n",
       "1  train_1       0  11.5006 -4.1473  13.8588  5.3890  12.3622   7.0433   \n",
       "2  train_2       0   8.6093 -2.7457  12.0805  7.8928  10.5825  -9.0837   \n",
       "3  train_3       0  11.0604 -2.1518   8.9522  7.1957  12.5846  -1.8361   \n",
       "4  train_4       0   9.8369 -1.4834  12.8746  6.6375  12.2772   2.4486   \n",
       "5  train_5       0  11.4763 -2.3182  12.6080  8.6264  10.9621   3.5609   \n",
       "6  train_6       0  11.8091 -0.0832   9.3494  4.2916  11.1355  -8.0198   \n",
       "7  train_7       0  13.5580 -7.9881  13.8776  7.5985   8.6543   0.8310   \n",
       "8  train_8       0  16.1071  2.4426  13.9307  5.6327   8.8014   6.1630   \n",
       "9  train_9       0  12.5088  1.9743   8.8960  5.4508  13.6043 -16.2859   \n",
       "\n",
       "    var_6    var_7  ...  var_190  var_191  var_192  var_193  var_194  var_195  \\\n",
       "0  5.1187  18.6266  ...   4.4354   3.9642   3.1364   1.6910  18.5227  -2.3978   \n",
       "1  5.6208  16.5338  ...   7.6421   7.7214   2.5837  10.9516  15.4305   2.0339   \n",
       "2  6.9427  14.6155  ...   2.9057   9.7905   1.6704   1.6858  21.6042   3.1417   \n",
       "3  5.8428  14.9250  ...   4.4666   4.7433   0.7178   1.4214  23.0347  -1.2706   \n",
       "4  5.9405  19.2514  ...  -1.4905   9.5214  -0.1508   9.1942  13.2876  -1.5121   \n",
       "5  4.5322  15.2255  ...  -6.3068   6.6025   5.2912   0.4403  14.9452   1.0314   \n",
       "6  6.1961  12.0771  ...   8.7830   6.4521   3.5325   0.1777  18.3314   0.5845   \n",
       "7  5.6890  22.3262  ...  13.1700   6.5491   3.9906   5.8061  23.1407  -0.3776   \n",
       "8  4.4514  10.1854  ...   1.4298  14.7510   1.6395   1.4181  14.8370  -1.9940   \n",
       "9  6.0637  16.8410  ...   0.5543   6.3160   1.0371   3.6885  14.8344   0.4467   \n",
       "\n",
       "   var_196  var_197  var_198  var_199  \n",
       "0   7.8784   8.5635  12.7803  -1.0914  \n",
       "1   8.1267   8.7889  18.3560   1.9518  \n",
       "2  -6.5213   8.2675  14.7222   0.3965  \n",
       "3  -2.9275  10.2922  17.9697  -8.9996  \n",
       "4   3.9267   9.5031  17.9974  -8.8104  \n",
       "5  -3.6241   9.7670  12.5809  -4.7602  \n",
       "6   9.1104   9.1143  10.8869  -3.2097  \n",
       "7   4.2178   9.4237   8.6624   3.4806  \n",
       "8  -1.0733   8.1975  19.5114   4.8453  \n",
       "9  14.1287   7.9133  16.2375  14.2514  \n",
       "\n",
       "[10 rows x 202 columns]"
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       "Index(['ID_code', 'target', 'var_0', 'var_1', 'var_2', 'var_3', 'var_4',\n",
       "       'var_5', 'var_6', 'var_7',\n",
       "       ...\n",
       "       'var_190', 'var_191', 'var_192', 'var_193', 'var_194', 'var_195',\n",
       "       'var_196', 'var_197', 'var_198', 'var_199'],\n",
       "      dtype='object', length=202)"
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     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
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  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
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       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.100490</td>\n",
       "      <td>10.679914</td>\n",
       "      <td>-1.627622</td>\n",
       "      <td>10.715192</td>\n",
       "      <td>6.796529</td>\n",
       "      <td>11.078333</td>\n",
       "      <td>-5.065317</td>\n",
       "      <td>5.408949</td>\n",
       "      <td>16.545850</td>\n",
       "      <td>0.284162</td>\n",
       "      <td>...</td>\n",
       "      <td>3.234440</td>\n",
       "      <td>7.438408</td>\n",
       "      <td>1.927839</td>\n",
       "      <td>3.331774</td>\n",
       "      <td>17.993784</td>\n",
       "      <td>-0.142088</td>\n",
       "      <td>2.303335</td>\n",
       "      <td>8.908158</td>\n",
       "      <td>15.870720</td>\n",
       "      <td>-3.326537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.300653</td>\n",
       "      <td>3.040051</td>\n",
       "      <td>4.050044</td>\n",
       "      <td>2.640894</td>\n",
       "      <td>2.043319</td>\n",
       "      <td>1.623150</td>\n",
       "      <td>7.863267</td>\n",
       "      <td>0.866607</td>\n",
       "      <td>3.418076</td>\n",
       "      <td>3.332634</td>\n",
       "      <td>...</td>\n",
       "      <td>4.559922</td>\n",
       "      <td>3.023272</td>\n",
       "      <td>1.478423</td>\n",
       "      <td>3.992030</td>\n",
       "      <td>3.135162</td>\n",
       "      <td>1.429372</td>\n",
       "      <td>5.454369</td>\n",
       "      <td>0.921625</td>\n",
       "      <td>3.010945</td>\n",
       "      <td>10.438015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.408400</td>\n",
       "      <td>-15.043400</td>\n",
       "      <td>2.117100</td>\n",
       "      <td>-0.040200</td>\n",
       "      <td>5.074800</td>\n",
       "      <td>-32.562600</td>\n",
       "      <td>2.347300</td>\n",
       "      <td>5.349700</td>\n",
       "      <td>-10.505500</td>\n",
       "      <td>...</td>\n",
       "      <td>-14.093300</td>\n",
       "      <td>-2.691700</td>\n",
       "      <td>-3.814500</td>\n",
       "      <td>-11.783400</td>\n",
       "      <td>8.694400</td>\n",
       "      <td>-5.261000</td>\n",
       "      <td>-14.209600</td>\n",
       "      <td>5.960600</td>\n",
       "      <td>6.299300</td>\n",
       "      <td>-38.852800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.453850</td>\n",
       "      <td>-4.740025</td>\n",
       "      <td>8.722475</td>\n",
       "      <td>5.254075</td>\n",
       "      <td>9.883175</td>\n",
       "      <td>-11.200350</td>\n",
       "      <td>4.767700</td>\n",
       "      <td>13.943800</td>\n",
       "      <td>-2.317800</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.058825</td>\n",
       "      <td>5.157400</td>\n",
       "      <td>0.889775</td>\n",
       "      <td>0.584600</td>\n",
       "      <td>15.629800</td>\n",
       "      <td>-1.170700</td>\n",
       "      <td>-1.946925</td>\n",
       "      <td>8.252800</td>\n",
       "      <td>13.829700</td>\n",
       "      <td>-11.208475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.524750</td>\n",
       "      <td>-1.608050</td>\n",
       "      <td>10.580000</td>\n",
       "      <td>6.825000</td>\n",
       "      <td>11.108250</td>\n",
       "      <td>-4.833150</td>\n",
       "      <td>5.385100</td>\n",
       "      <td>16.456800</td>\n",
       "      <td>0.393700</td>\n",
       "      <td>...</td>\n",
       "      <td>3.203600</td>\n",
       "      <td>7.347750</td>\n",
       "      <td>1.901300</td>\n",
       "      <td>3.396350</td>\n",
       "      <td>17.957950</td>\n",
       "      <td>-0.172700</td>\n",
       "      <td>2.408900</td>\n",
       "      <td>8.888200</td>\n",
       "      <td>15.934050</td>\n",
       "      <td>-2.819550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.758200</td>\n",
       "      <td>1.358625</td>\n",
       "      <td>12.516700</td>\n",
       "      <td>8.324100</td>\n",
       "      <td>12.261125</td>\n",
       "      <td>0.924800</td>\n",
       "      <td>6.003000</td>\n",
       "      <td>19.102900</td>\n",
       "      <td>2.937900</td>\n",
       "      <td>...</td>\n",
       "      <td>6.406200</td>\n",
       "      <td>9.512525</td>\n",
       "      <td>2.949500</td>\n",
       "      <td>6.205800</td>\n",
       "      <td>20.396525</td>\n",
       "      <td>0.829600</td>\n",
       "      <td>6.556725</td>\n",
       "      <td>9.593300</td>\n",
       "      <td>18.064725</td>\n",
       "      <td>4.836800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.315000</td>\n",
       "      <td>10.376800</td>\n",
       "      <td>19.353000</td>\n",
       "      <td>13.188300</td>\n",
       "      <td>16.671400</td>\n",
       "      <td>17.251600</td>\n",
       "      <td>8.447700</td>\n",
       "      <td>27.691800</td>\n",
       "      <td>10.151300</td>\n",
       "      <td>...</td>\n",
       "      <td>18.440900</td>\n",
       "      <td>16.716500</td>\n",
       "      <td>8.402400</td>\n",
       "      <td>18.281800</td>\n",
       "      <td>27.928800</td>\n",
       "      <td>4.272900</td>\n",
       "      <td>18.321500</td>\n",
       "      <td>12.000400</td>\n",
       "      <td>26.079100</td>\n",
       "      <td>28.500700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 201 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              target          var_0          var_1          var_2  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.100490      10.679914      -1.627622      10.715192   \n",
       "std         0.300653       3.040051       4.050044       2.640894   \n",
       "min         0.000000       0.408400     -15.043400       2.117100   \n",
       "25%         0.000000       8.453850      -4.740025       8.722475   \n",
       "50%         0.000000      10.524750      -1.608050      10.580000   \n",
       "75%         0.000000      12.758200       1.358625      12.516700   \n",
       "max         1.000000      20.315000      10.376800      19.353000   \n",
       "\n",
       "               var_3          var_4          var_5          var_6  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        6.796529      11.078333      -5.065317       5.408949   \n",
       "std         2.043319       1.623150       7.863267       0.866607   \n",
       "min        -0.040200       5.074800     -32.562600       2.347300   \n",
       "25%         5.254075       9.883175     -11.200350       4.767700   \n",
       "50%         6.825000      11.108250      -4.833150       5.385100   \n",
       "75%         8.324100      12.261125       0.924800       6.003000   \n",
       "max        13.188300      16.671400      17.251600       8.447700   \n",
       "\n",
       "               var_7          var_8  ...        var_190        var_191  \\\n",
       "count  200000.000000  200000.000000  ...  200000.000000  200000.000000   \n",
       "mean       16.545850       0.284162  ...       3.234440       7.438408   \n",
       "std         3.418076       3.332634  ...       4.559922       3.023272   \n",
       "min         5.349700     -10.505500  ...     -14.093300      -2.691700   \n",
       "25%        13.943800      -2.317800  ...      -0.058825       5.157400   \n",
       "50%        16.456800       0.393700  ...       3.203600       7.347750   \n",
       "75%        19.102900       2.937900  ...       6.406200       9.512525   \n",
       "max        27.691800      10.151300  ...      18.440900      16.716500   \n",
       "\n",
       "             var_192        var_193        var_194        var_195  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        1.927839       3.331774      17.993784      -0.142088   \n",
       "std         1.478423       3.992030       3.135162       1.429372   \n",
       "min        -3.814500     -11.783400       8.694400      -5.261000   \n",
       "25%         0.889775       0.584600      15.629800      -1.170700   \n",
       "50%         1.901300       3.396350      17.957950      -0.172700   \n",
       "75%         2.949500       6.205800      20.396525       0.829600   \n",
       "max         8.402400      18.281800      27.928800       4.272900   \n",
       "\n",
       "             var_196        var_197        var_198        var_199  \n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000  \n",
       "mean        2.303335       8.908158      15.870720      -3.326537  \n",
       "std         5.454369       0.921625       3.010945      10.438015  \n",
       "min       -14.209600       5.960600       6.299300     -38.852800  \n",
       "25%        -1.946925       8.252800      13.829700     -11.208475  \n",
       "50%         2.408900       8.888200      15.934050      -2.819550  \n",
       "75%         6.556725       9.593300      18.064725       4.836800  \n",
       "max        18.321500      12.000400      26.079100      28.500700  \n",
       "\n",
       "[8 rows x 201 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200000, 202)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(train_df.target);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89.95100000000001"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(train_df.target == 0)/train_df.target.shape[0]*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = train_df.columns[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.scatter(train_df[::20], train_df[::20], features[2:13])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking the mean per row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature exgineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Density plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Mean and variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20cdbd02b88>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_df[features].mean(axis=1),color=\"green\", kde=True,bins=120, label='train')\n",
    "#sns.distplot(test_df[features].mean(axis=1),color=\"blue\", kde=True,bins=120, label='test')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now checking the mean per column to understand the extent of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20cc4c94ac8>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_df[features].mean(axis=0),color=\"green\", kde=True,bins=120, label='train')\n",
    "#sns.distplot(test_df[features].mean(axis=1),color=\"blue\", kde=True,bins=120, label='test')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20c949e4408>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_df[features].std(axis=1),color=\"green\", kde=True,bins=120, label='train')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20c94a26808>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zr+HYyWNeRxJpEFT0dWTz/s1s/2p7gxu2qax/6/786ft/Yvnny0lbmKZ3zorUARV9HcnMzQTgguSG9UJsVa7vez0Z12TwRt4bXPmnKzlw/IDXkUR8TUVfRzLzMumd1JuWjVp6HaVeuHnAzcxPm8/Huz5m2PPD2Fq41etIIr6loq8DR4uP8v7n7zO221ivo9Qrky6YxLIpyyg4WsCA2QNYsEGfTiUSCir6OvDu9ncpLi1mTLcxXkepd0Z0GkF2eja9k3tz/SvXM/VvU9l3bJ/XsUR8RUVfBzJzM2kU3YgRnUZ4HaVeSklMYfkNy7n3knt56V8v0eN3PXhm9TOUlpV6HU3EF1T0IXbqsMrvpnyX2KhYr+PUW9GR0cz67izWTl9L6nmp3Lb0NgY9O4i8A3leRxMJeyr6EPts/2ds+2qbxueDdEGrC3j739/m5Qkvs+/YPh5b8RgvrnmRg0UHvY4mErZU9CGWmVd+WKWKPnhmxnXnX8ent3/KmG5jWJ2/mvveu493tr1DmSvzOp5I2FHRh1hmXiY9W/YkJTHF6yhhp3FMY77f6/vcP/J+uiR24eWNL/P4isfZe3Sv19FEwoqKPoSOnTzG+9t1WGVNtW7Smp9c/BNuTL2R/MP5zFo+i492fuR1LJGwoaIPoXe3vcuJ0hOM7a6irykzY0j7Idw38j46JXTixbUvsnjTYg3liARBRR9CmXk6rLK2tYhvwR1D7mBExxG8seUNJiycoPPliJyFij5ETh1WeVnny4iLivM6jq9ERkQyue9krutzHYs/XcyEhRM4UXLC61gi9ZaKPkRyD+SytXCrxudDxMy4vMvlzP7ebP6e+3cm/mUixaXFXscSqZeivA7gV6fOVhmq8fmM7Ixqza/uMjXNUZ31nFoufWD6OWWYfMFk5m2Yx4gXR3Bj6o2YWa3cr4hfqOhDJDMvkx4te9AlsYvXUXxvZOeRHDl5hCWbl3Bek/O4qvtVXkcSqVeCGroxszFmttnM8szsniqujzWzlwPXf2xmnQPzO5vZcTNbG/j6Y+3Gr5+OnTzGe9vf07BNHbqq21Vc3O5iXt38Ktn52V7HEalXzrpHb2aRwNPAFcBOYLWZLXHOfVJhsWlAoXOum5lNAh4Ffhi4botzLrWWc9dr721/r/ywShV9nTEz/r3fv7P/2H7mrptLu2btOK/JeV7HEqkXgtmjvxjIc85tdc4VAwuA8ZWWGQ/MDUwvAi63UwOlDVBmbibxUfGM7DzS6ygNSnRkNLcMuIXoyGhmZ8/Wi7MiAcEUfTtgR4XLOwPzqlzGOVcCHAROfZRSipmtMbP3zeySGuYNC5l5mVyWosMqvZAYn8hNqTeRfziflze87HUckXohmKKvas/cBbnMbqCjc+5C4C5gnpk1+9YKzNLNLMvMsvbuDe/zmOTuz2VL4RYN23jo/FbnM7bbWD7Y8QFZ+VlexxHxXDBFvxPoUOFyeyD/dMuYWRSQABxwzp1wzu0HcM5lA1uAHpVX4JzLcM4Ncs4NSk5Orv5W1CM6W2X9cE2Pa0hpnsJL619i16FdXscR8VQwRb8a6G5mKWYWA0wCllRaZgkwNTA9AXjHOefMLDnwYi5m1gXoDvj6U6D/nvt3erTsQdcWXb2O0qBFRkRyY+qNlJSVcNOSm3Cu8j+hIg3HWYs+MOY+A3gT2AQsdM5tNLOZZjYusNjzQEszy6N8iObUIZgjgH+Z2TrKX6Sd7pw7UNsbUV8cPnGY97a/xzU9rvE6ilB+1ssJfSbw1pa3+MPqP3gdR8QzQb1hyjm3FFhaad59FaaLgIlV3O4V4JUaZgwbb215i+LSYhV9PTKi4wgKjxfys2U/4/Iul9MrqZfXkUTqnM51U4te++w1EuMS+U7H73gdRQLMjOfHPU+j6EZMWTyFk6UnvY4kUudU9LWktKyUpblLGdt9LFEROrNEfdKmaRsyrskgKz+LB5c/6HUckTqnoq8lq3atYu+xvRq2qad+0PsHTO0/lYf++ZA+nUoaHBV9LXnts9eItEhGdx3tdRQ5jSfHPEn7Zu2ZsngKR4uPeh1HpM6o6GvJa5+9xiWdLiExPtHrKHIaCXEJzL12LlsObOHut+72Oo5InVHR14Lc/blsKNjA+J6VTwEk9c3IziO5e9jd/DH7jyzNXXr2G4j4gIq+Fiz6ZBEAab3TPE4iwZh12Sz6turLTa/exL5j+7yOIxJyKvpasGjTIoa0H0KHhA5nX1g8FxsVy59/8GcKiwpJfy1d75oV31PR19DWwq3k7M5hQu8JXkeRaujXuh8PffchFn+6mLnr5p79BiJhTEVfQ6eGbSb0UdGHmzuH3MnITiO5fentrN+z3us4IiGjoq+hRZ8s4qK2F9GpeSevo0g1RUZEMj9tPgmxCVz78rUcOO7b0zBJA6eir4HPv/qc1fmrtTcfxto0bcMr173CjoM7mPzKZErLSr2OJFLrVPQ1MH/DfAAm9vnW+dwkjAztMJSnr3qaN7e8ye1Lb9eLs+I7OinLOXLOMWftHEZ0GkFKYorXcaSGbhl4C9u+2sbDHzxMq8atmHnZTK8jidQaFf05+njXx2zev5n//M5/eh1FaslD332IgqMFzFo+i4TYBH467KdeRxKpFSr6czRn7RwaRTdqcMM2GdkZdbKO9IHptXp/Vam8DjPjj9/7IwdPHOTuZXdz6MQh2jZti5l96z4q3jbYn0nl9VW1nWdaT2Wnlq3Nn5VfNfSflYr+HBw/eZwFGxaQ1juNprFNvY4jtSgqIor5afNpGtOUmctncmmnS7nu/OuIjIj0OprIOVPRn4NXN7/KwRMHuSH1Bq+jSAhERUTx/LjnaRHfgv9e+d/kH8nn5gtvJiEuwetoIudER92cg+dynqNjQkcu7Xyp11EkRMyMx698nBtSb2Bb4TYe+udDfLL3E69jiZwTFX01rd+znre3vc30gdOJMP34/G5o+6HcM/we4qPjefLjJ3lhzQscPnHY61gi1aKhm2r67Ue/JT4qnh8P+rHXUaSOtG/WnnsvuZfMvEzeyHuDdXvWcbzkOHcMuYMmMU28jidyVir6athzZA8vrX+JG1NvpEV8C6/jSB2KjoxmXM9xXNT2IhZ/uphfvvtLnvr4KaYPmk7j6Mb6wBmp11T01fBM1jOcKD3BHUPu8DqKeKRN0zbcdtFt9GvdjweXP8iDyx/EzEg9L5XLOl9G9xbdMTOvY4p8g4o+SMdPHueZrGe4uvvV9Ezq6XUc8diQ9kN4ffLrbC3cyvTXp/Phjg/J2Z1D26ZtGdZ+GIPbD6ZZbDOvY4oAKvqg/X7V7yk4WsDPhv3M6yhSj3RJ7MKEPhMY13Mcq3at4oMvPmDRpkX89dO/0rdVX4Z1GMYFrS4gKkJPNfGOfvuCUHi8kF9/8GvGdhvLyM4jvY4j9VBMZAzDOw5neMfh5B/OZ+XOlXy08yPW7VlHk5gmDG43mMHtBtP/vP5eR5UGSEUfhEc+eISDRQd5ZNQjXkeRMNC2aVvSeqdxbc9r+WTvJ6zYuYL3P3+f1NmpXHjehdyQegOT+072OqY0ICr6s9hxcAdPfvwkU/pPoV/rfl7HkTASGRFJ39Z96du6L0eKjxATGcOctXP4jzf+g7vfupu+rfrSvll7xnQbo/dkSEjpt+sMnHPc+eadOBwzL9Vpa+XcNYlpwoyLZ5CVnsW66euYcfEMcg/kcvW8q+n+u+48sfIJCo8Xeh1TfEp79Gcwb/08Xtn0Co9c/og+KlBqTb/W/Xhi9BN0b9GdpEZJ/G7V7/jpWz/ll+/+koFtBnJZ58to16yd1zHFR1T0p7Hr0C5mZM5gWIdh3D3sbq/jiA9FRkQy8fyJTDx/Iuu+XMfvV/2euevm8s8v/kmPFj24tPOllFxYoiN2pMY0dFOFkrISbnz1RopLi5l77VydolZCrv95/Xl23LM8OupR0nqnsf/4fjJyMkh5MoWHlj/EniN7vI4oYUxFX4lzjtv+fhvLti7jqTFP0a1FN68jSQPSOKYxV3a9kge/+yC3XXQbfZL7cO+799Lh/3dg0qJJvLf9PX2mrVSb/iesZOb7M3k251n+65L/YtqAaV7HkQYqwiLo37o/T1/1NJv3bWZ29mzmrJ3DyxtfpldSL6YPnE6ZK6NxTGOvo0oY0B59QJkr44H3HuCB9x9gav+pzLpslteRRADomdSTJ0Y/wa67djFn/ByaxzXnjjfv4Of/+Dlz1s7hwy8+1F6+nJGKHjhafJQfLvohv3r/V0ztP5Vnr3lWJ6aSeic+Op6pqVNZOW0la368hqEdhpKzO4fhLw4n5ckU7vnHPaz7cp1KX76lwQ/dvJn3Jj954yfk7s/lsSse46dDf6qSl3ov9bxU/q3vv5HWO43EuETmb5jP4yse59EPH6V3Um++3+v7jOk2hiHthxAdGe11XPFYgyx65xwffPEBj698nCWbl9CtRTfemvIWo7qM8jqaSLXERcUxpf8UpvSfwt6je1n0ySIWbFzAox8+yq8/+DXNYpsxqssoRncdzbAOw+id1FtHkTVAQRW9mY0BngQigeecc49Uuj4W+B9gILAf+KFzbnvgul8A04BS4CfOuTdrLX01lJSVkJWfxVtb3mLhxoVs3LuRhNgEHr78Ye4cciexUbFexBKpNcmNk7n1olu59aJb+aroK97e+jZv5L3BG1ve4K+b/gpA4+jGDGgzgEFtBzGwzUC6t+xO18SutIhvEdb/yZa5Mo4UH+Fg0UEOnTj0ja8TpSf4aOdHOOeIjojGUf49LiqO2KhY4qLiiIuKo1lsM5rHNSchNoFmsc189QfxrEVvZpHA08AVwE5gtZktcc5V/KTkaUChc66bmU0CHgV+aGZ9gEnA+UBb4B9m1sM5V1rbG1JSVkLu/lwKjhaw5+geCo4WUHC0gF2HdrFh7wY2FGzg2MljGMbg9oN5ftzzTLpgEo2iG9V2FBHPNY9rTlqfNNL6pOGcY/P+zazetZrV+avJys/imaxnKCop+nr5ZrHN6JrYlU7NO5HcKJnkRskkNUoiuXEyCbEJxEfHEx8VT3x0PI2iG309HRMZQ4RFYBhm9o3vERbx9XRJWQkny05ysvTkN74XlxZ/Y/rwicMcKT7CkeIjHC4unz4179CJQxwqLi/vyoV+uDi4z/Gds25O0D/DpjFNy4s/LoGE2ISvp5vHln9vEtPkW1+NoxvTJKYJsVGxREdEEx0ZTXRENDGRMV9PmxnOOcpcGY7A98DlqIiokHxaWTB79BcDec65rQBmtgAYD1Qs+vHAA4HpRcDvrXz3YDywwDl3AthmZnmB+1tZO/H/z75j++jzhz7fmBdhEbRq3Io+yX24ZcAtDG0/lFFdRtGyUcvaXr1IvWVm9ErqRa+kXkzpPwUo3zH6bP9n5B3IY8uBLWwt3MqWwi3k7s9l5Y6V7Du2j9La3x87J4bROKbx13vap746JHSgWUyzb8xLiPvmMs1imxEbGcvCjQsxMyb3nYxhnCw7yYmSExSVFFFUUsTxkuMcOnGIr4q+4mDRQQ6eOFg+fep70UHyD+ezad+mry+H4uczuN1gPrr5o1q/XzvbK/RmNgEY45y7OXB5CjDYOTejwjIbAsvsDFzeAgymvPw/cs79OTD/eSDTObeo0jrSgfTAxZ7A5sB0ErCvJhtYz/hte8B/2+S37QH/bZPftgdqZ5s6OeeSq7oimD36qgbuKv91ON0ywdwW51wGkPGtFZtlOecGBZExLPhte8B/2+S37QH/bZPftgdCv03BHEe/E+hQ4XJ7IP90y5hZFJAAHAjytiIiEkLBFP1qoLuZpZhZDOUvri6ptMwSYGpgegLwjisfE1oCTDKzWDNLAboDq2onuoiIBOOsQzfOuRIzmwG8SfnhlS845zaa2Uwgyzm3BHge+FPgxdYDlP8xILDcQspfuC0Bbq/mETffGs4Jc37bHvDfNvlte8B/2+S37YEQb9NZX4wVEZHwpnPdiIj4nIpeRMTn6n3Rm9kDZrbLzNYGvq7yOtO5MLMxZrbZzPLM7B6v89QGM9tuZusDj0uW13mqy8xeMLOCwPtATs1rYWbLzCw38L3236YYQqfZprB9DplZBzN718w2mdlGM/uPwPywfJzOsD0hfYzq/Ri9mT0AHHHOPe51lnczsYsAAAJaSURBVHMVOI3EZ1Q4jQRwfaXTSIQdM9sODHLOheWbV8xsBHAE+B/n3AWBeb8BDjjnHgn8QU50zv3cy5zVcZpteoAwfQ6ZWRugjXMux8yaAtnAtcANhOHjdIbtuY4QPkb1fo/eJ74+jYRzrhg4dRoJ8ZBzbjnlR4lVNB6YG5ieS/mTMGycZpvClnNut3MuJzB9GNgEtCNMH6czbE9IhUvRzzCzfwX+LQ2Lf9EqaQfsqHB5J3Xw4NYBB7xlZtmB01j4QWvn3G4of1ICrTzOU1vC/TmEmXUGLgQ+xgePU6XtgRA+RvWi6M3sH2a2oYqv8cAzQFcgFdgN/LenYc9NUKeCCEPfcc4NAMYCtweGDaT+CfvnkJk1AV4B7nDOHfI6T01VsT0hfYzqxQePOOeC+sQPM3sWeD3EcULBl6eCcM7lB74XmNliyoeolnubqsb2mFkb59zuwHhqgdeBaso5t+fUdDg+h8wsmvJSfMk599fA7LB9nKranlA/RvVij/5MAg/iKd8HNpxu2XosmNNIhBUzaxx4MQkzawxcSXg+NpVVPJ3HVOBVD7PUinB+DpmZUf7O+03OuScqXBWWj9PptifUj1E4HHXzJ8r/nXHAduDHp8bmwkngcKnf8n+nkXjI40g1YmZdgMWBi1HAvHDbJjObD1xK+Sli9wD3A38DFgIdgS+Aic65sHlx8zTbdClh+hwys+HAP4H1QFlg9v+jfFw77B6nM2zP9YTwMar3RS8iIjVT74duRESkZlT0IiI+p6IXEfE5Fb2IiM+p6EVEfE5FLyLicyp6ERGf+1/Oz3pUDf+LWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_df[features].std(axis=0),color=\"green\", kde=True,bins=120, label='train')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Kurtosis and skew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Min and max values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20c94afac48>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_df[features].min(axis=1),color=\"green\", kde=True,bins=120, label='train')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20c95217dc8>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_df[features].min(axis=0),color=\"green\", kde=True,bins=120, label='train')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20c94f63f48>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_df[features].max(axis=1),color=\"green\", kde=True,bins=120, label='train')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20c955996c8>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_df[features].max(axis=0),color=\"green\", kde=True,bins=120, label='train')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>var_0</th>\n",
       "      <th>var_1</th>\n",
       "      <th>var_2</th>\n",
       "      <th>var_3</th>\n",
       "      <th>var_4</th>\n",
       "      <th>var_5</th>\n",
       "      <th>var_6</th>\n",
       "      <th>var_7</th>\n",
       "      <th>var_8</th>\n",
       "      <th>var_9</th>\n",
       "      <th>...</th>\n",
       "      <th>var_190</th>\n",
       "      <th>var_191</th>\n",
       "      <th>var_192</th>\n",
       "      <th>var_193</th>\n",
       "      <th>var_194</th>\n",
       "      <th>var_195</th>\n",
       "      <th>var_196</th>\n",
       "      <th>var_197</th>\n",
       "      <th>var_198</th>\n",
       "      <th>var_199</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>var_0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000544</td>\n",
       "      <td>0.006573</td>\n",
       "      <td>0.003801</td>\n",
       "      <td>0.001326</td>\n",
       "      <td>0.003046</td>\n",
       "      <td>0.006983</td>\n",
       "      <td>0.002429</td>\n",
       "      <td>0.004962</td>\n",
       "      <td>-0.002613</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002752</td>\n",
       "      <td>0.000206</td>\n",
       "      <td>-0.005373</td>\n",
       "      <td>0.001616</td>\n",
       "      <td>-0.001514</td>\n",
       "      <td>0.002073</td>\n",
       "      <td>0.004386</td>\n",
       "      <td>-0.000753</td>\n",
       "      <td>-0.005776</td>\n",
       "      <td>0.003850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var_1</th>\n",
       "      <td>-0.000544</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003980</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000303</td>\n",
       "      <td>-0.000902</td>\n",
       "      <td>0.003258</td>\n",
       "      <td>0.001511</td>\n",
       "      <td>0.004098</td>\n",
       "      <td>-0.000832</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006627</td>\n",
       "      <td>0.003621</td>\n",
       "      <td>-0.002604</td>\n",
       "      <td>0.001153</td>\n",
       "      <td>-0.002557</td>\n",
       "      <td>-0.000785</td>\n",
       "      <td>-0.000377</td>\n",
       "      <td>-0.004157</td>\n",
       "      <td>-0.004861</td>\n",
       "      <td>0.002287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var_2</th>\n",
       "      <td>0.006573</td>\n",
       "      <td>0.003980</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>0.000723</td>\n",
       "      <td>0.001569</td>\n",
       "      <td>0.000883</td>\n",
       "      <td>-0.000991</td>\n",
       "      <td>0.002648</td>\n",
       "      <td>-0.001932</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.001285</td>\n",
       "      <td>-0.003400</td>\n",
       "      <td>0.000549</td>\n",
       "      <td>0.002104</td>\n",
       "      <td>-0.001070</td>\n",
       "      <td>0.003952</td>\n",
       "      <td>0.001078</td>\n",
       "      <td>-0.000877</td>\n",
       "      <td>0.003855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var_3</th>\n",
       "      <td>0.003801</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000322</td>\n",
       "      <td>0.003253</td>\n",
       "      <td>-0.000774</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.003553</td>\n",
       "      <td>-0.000826</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>0.002445</td>\n",
       "      <td>-0.001530</td>\n",
       "      <td>-0.001699</td>\n",
       "      <td>-0.001054</td>\n",
       "      <td>0.001206</td>\n",
       "      <td>-0.002800</td>\n",
       "      <td>0.001164</td>\n",
       "      <td>-0.001651</td>\n",
       "      <td>0.000506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var_4</th>\n",
       "      <td>0.001326</td>\n",
       "      <td>0.000303</td>\n",
       "      <td>0.000723</td>\n",
       "      <td>-0.000322</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001368</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.004549</td>\n",
       "      <td>0.001194</td>\n",
       "      <td>-0.000918</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001514</td>\n",
       "      <td>0.004357</td>\n",
       "      <td>0.003347</td>\n",
       "      <td>0.000813</td>\n",
       "      <td>-0.000068</td>\n",
       "      <td>0.003706</td>\n",
       "      <td>0.000513</td>\n",
       "      <td>-0.000046</td>\n",
       "      <td>-0.001821</td>\n",
       "      <td>-0.000786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var_195</th>\n",
       "      <td>0.002073</td>\n",
       "      <td>-0.000785</td>\n",
       "      <td>-0.001070</td>\n",
       "      <td>0.001206</td>\n",
       "      <td>0.003706</td>\n",
       "      <td>-0.001274</td>\n",
       "      <td>0.001244</td>\n",
       "      <td>0.001854</td>\n",
       "      <td>0.001396</td>\n",
       "      <td>-0.000868</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004571</td>\n",
       "      <td>0.000870</td>\n",
       "      <td>-0.004745</td>\n",
       "      <td>-0.003143</td>\n",
       "      <td>-0.001201</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002517</td>\n",
       "      <td>-0.004170</td>\n",
       "      <td>-0.000536</td>\n",
       "      <td>0.002042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var_196</th>\n",
       "      <td>0.004386</td>\n",
       "      <td>-0.000377</td>\n",
       "      <td>0.003952</td>\n",
       "      <td>-0.002800</td>\n",
       "      <td>0.000513</td>\n",
       "      <td>0.002880</td>\n",
       "      <td>0.005378</td>\n",
       "      <td>0.001045</td>\n",
       "      <td>-0.003242</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000847</td>\n",
       "      <td>0.002466</td>\n",
       "      <td>-0.001386</td>\n",
       "      <td>-0.005308</td>\n",
       "      <td>-0.005040</td>\n",
       "      <td>0.002517</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000454</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>0.000607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var_197</th>\n",
       "      <td>-0.000753</td>\n",
       "      <td>-0.004157</td>\n",
       "      <td>0.001078</td>\n",
       "      <td>0.001164</td>\n",
       "      <td>-0.000046</td>\n",
       "      <td>-0.000535</td>\n",
       "      <td>-0.003565</td>\n",
       "      <td>0.003466</td>\n",
       "      <td>-0.004583</td>\n",
       "      <td>0.003701</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004974</td>\n",
       "      <td>0.000906</td>\n",
       "      <td>-0.000527</td>\n",
       "      <td>0.005068</td>\n",
       "      <td>0.000884</td>\n",
       "      <td>-0.004170</td>\n",
       "      <td>-0.000454</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001183</td>\n",
       "      <td>0.004991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var_198</th>\n",
       "      <td>-0.005776</td>\n",
       "      <td>-0.004861</td>\n",
       "      <td>-0.000877</td>\n",
       "      <td>-0.001651</td>\n",
       "      <td>-0.001821</td>\n",
       "      <td>-0.000953</td>\n",
       "      <td>-0.003025</td>\n",
       "      <td>0.000650</td>\n",
       "      <td>0.002950</td>\n",
       "      <td>0.002343</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000153</td>\n",
       "      <td>-0.000067</td>\n",
       "      <td>0.003451</td>\n",
       "      <td>0.001646</td>\n",
       "      <td>0.003194</td>\n",
       "      <td>-0.000536</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>0.001183</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.004731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var_199</th>\n",
       "      <td>0.003850</td>\n",
       "      <td>0.002287</td>\n",
       "      <td>0.003855</td>\n",
       "      <td>0.000506</td>\n",
       "      <td>-0.000786</td>\n",
       "      <td>0.002767</td>\n",
       "      <td>0.006096</td>\n",
       "      <td>-0.001457</td>\n",
       "      <td>0.000854</td>\n",
       "      <td>0.001070</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000404</td>\n",
       "      <td>0.003595</td>\n",
       "      <td>-0.001239</td>\n",
       "      <td>-0.000552</td>\n",
       "      <td>-0.005615</td>\n",
       "      <td>0.002042</td>\n",
       "      <td>0.000607</td>\n",
       "      <td>0.004991</td>\n",
       "      <td>-0.004731</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 200 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            var_0     var_1     var_2     var_3     var_4     var_5     var_6  \\\n",
       "var_0    1.000000 -0.000544  0.006573  0.003801  0.001326  0.003046  0.006983   \n",
       "var_1   -0.000544  1.000000  0.003980  0.000010  0.000303 -0.000902  0.003258   \n",
       "var_2    0.006573  0.003980  1.000000  0.001001  0.000723  0.001569  0.000883   \n",
       "var_3    0.003801  0.000010  0.001001  1.000000 -0.000322  0.003253 -0.000774   \n",
       "var_4    0.001326  0.000303  0.000723 -0.000322  1.000000 -0.001368  0.000049   \n",
       "...           ...       ...       ...       ...       ...       ...       ...   \n",
       "var_195  0.002073 -0.000785 -0.001070  0.001206  0.003706 -0.001274  0.001244   \n",
       "var_196  0.004386 -0.000377  0.003952 -0.002800  0.000513  0.002880  0.005378   \n",
       "var_197 -0.000753 -0.004157  0.001078  0.001164 -0.000046 -0.000535 -0.003565   \n",
       "var_198 -0.005776 -0.004861 -0.000877 -0.001651 -0.001821 -0.000953 -0.003025   \n",
       "var_199  0.003850  0.002287  0.003855  0.000506 -0.000786  0.002767  0.006096   \n",
       "\n",
       "            var_7     var_8     var_9  ...   var_190   var_191   var_192  \\\n",
       "var_0    0.002429  0.004962 -0.002613  ...  0.002752  0.000206 -0.005373   \n",
       "var_1    0.001511  0.004098 -0.000832  ...  0.006627  0.003621 -0.002604   \n",
       "var_2   -0.000991  0.002648 -0.001932  ...  0.000197  0.001285 -0.003400   \n",
       "var_3    0.002500  0.003553 -0.000826  ...  0.000151  0.002445 -0.001530   \n",
       "var_4    0.004549  0.001194 -0.000918  ...  0.001514  0.004357  0.003347   \n",
       "...           ...       ...       ...  ...       ...       ...       ...   \n",
       "var_195  0.001854  0.001396 -0.000868  ...  0.004571  0.000870 -0.004745   \n",
       "var_196  0.001045 -0.003242  0.000052  ... -0.000847  0.002466 -0.001386   \n",
       "var_197  0.003466 -0.004583  0.003701  ... -0.004974  0.000906 -0.000527   \n",
       "var_198  0.000650  0.002950  0.002343  ... -0.000153 -0.000067  0.003451   \n",
       "var_199 -0.001457  0.000854  0.001070  ... -0.000404  0.003595 -0.001239   \n",
       "\n",
       "          var_193   var_194   var_195   var_196   var_197   var_198   var_199  \n",
       "var_0    0.001616 -0.001514  0.002073  0.004386 -0.000753 -0.005776  0.003850  \n",
       "var_1    0.001153 -0.002557 -0.000785 -0.000377 -0.004157 -0.004861  0.002287  \n",
       "var_2    0.000549  0.002104 -0.001070  0.003952  0.001078 -0.000877  0.003855  \n",
       "var_3   -0.001699 -0.001054  0.001206 -0.002800  0.001164 -0.001651  0.000506  \n",
       "var_4    0.000813 -0.000068  0.003706  0.000513 -0.000046 -0.001821 -0.000786  \n",
       "...           ...       ...       ...       ...       ...       ...       ...  \n",
       "var_195 -0.003143 -0.001201  1.000000  0.002517 -0.004170 -0.000536  0.002042  \n",
       "var_196 -0.005308 -0.005040  0.002517  1.000000 -0.000454  0.000253  0.000607  \n",
       "var_197  0.005068  0.000884 -0.004170 -0.000454  1.000000  0.001183  0.004991  \n",
       "var_198  0.001646  0.003194 -0.000536  0.000253  0.001183  1.000000 -0.004731  \n",
       "var_199 -0.000552 -0.005615  0.002042  0.000607  0.004991 -0.004731  1.000000  \n",
       "\n",
       "[200 rows x 200 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[features].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Checking for duplicats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [c for c in train_df.columns if c not in ['ID_code', 'target']]\n",
    "target = train_df['target']\n",
    "train_df =train_df[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "param = {\n",
    "    'bagging_freq': 5,\n",
    "    'bagging_fraction': 0.4,\n",
    "    'boost_from_average':'false',\n",
    "    'boost': 'gbdt',\n",
    "    'feature_fraction': 0.05,\n",
    "    'learning_rate': 0.01,\n",
    "    'max_depth': -1,  \n",
    "    'metric':'auc',\n",
    "    'min_data_in_leaf': 80,\n",
    "    'min_sum_hessian_in_leaf': 10.0,\n",
    "    'num_leaves': 13,\n",
    "    'num_threads': 8,\n",
    "    'tree_learner': 'serial',\n",
    "    'objective': 'binary', \n",
    "    'verbosity': 1\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in c:\\programdata\\anaconda3\\lib\\site-packages (1.0.2)\n",
      "Requirement already satisfied: scipy in c:\\programdata\\anaconda3\\lib\\site-packages (from xgboost) (1.4.1)\n",
      "Requirement already satisfied: numpy in c:\\programdata\\anaconda3\\lib\\site-packages (from xgboost) (1.18.2)\n"
     ]
    }
   ],
   "source": [
    "#!pip install xgboost\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(train_df, target, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "D_train = xgb.DMatrix(train_df, label=target)\n",
    "D_test = xgb.DMatrix(X_test, label=Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "param = {\n",
    "    'eta': 0.3, \n",
    "    'max_depth': 3,  \n",
    "    'objective': 'multi:softprob',  \n",
    "    'num_class': 3} \n",
    "\n",
    "steps = 20  # The number of training iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = xgb.train(param, D_train, steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision = 0.8747891252707727\n",
      "Recall = 0.5034472965330984\n",
      "Accuracy = 0.90105\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import precision_score, recall_score, accuracy_score\n",
    "\n",
    "preds = model.predict(D_test)\n",
    "best_preds = np.asarray([np.argmax(line) for line in preds])\n",
    "\n",
    "print(\"Precision = {}\".format(precision_score(Y_test, best_preds, average='macro')))\n",
    "print(\"Recall = {}\".format(recall_score(Y_test, best_preds, average='macro')))\n",
    "print(\"Accuracy = {}\".format(accuracy_score(Y_test, best_preds)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18173778432410836"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = LinearRegression().fit(train_df.values, target.values)\n",
    "reg.score(train_df.values, target.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from matplotlib import pyplot\n",
    "# define dataset\n",
    "# evaluate the model\n",
    "model = GradientBoostingClassifier()\n",
    "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "n_scores = cross_val_score(model, train_df.values, target.values, scoring='accuracy', cv=cv, n_jobs=-1, error_score='raise')\n",
    "print('Accuracy: %.3f (%.3f)' % (mean(n_scores), std(n_scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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