data_exploration_and_preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading libraries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.impute import KNNImputer\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test.csv doesn't have values for the target variable\n",
    "# the aim is to predict its failure values\n",
    "train = pd.read_csv('data/train.csv')\n",
    "test = pd.read_csv('data/test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check first 5 observations to make sure that the data was read correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>product_code</th>\n",
       "      <th>loading</th>\n",
       "      <th>attribute_0</th>\n",
       "      <th>attribute_1</th>\n",
       "      <th>attribute_2</th>\n",
       "      <th>attribute_3</th>\n",
       "      <th>measurement_0</th>\n",
       "      <th>measurement_1</th>\n",
       "      <th>measurement_2</th>\n",
       "      <th>...</th>\n",
       "      <th>measurement_9</th>\n",
       "      <th>measurement_10</th>\n",
       "      <th>measurement_11</th>\n",
       "      <th>measurement_12</th>\n",
       "      <th>measurement_13</th>\n",
       "      <th>measurement_14</th>\n",
       "      <th>measurement_15</th>\n",
       "      <th>measurement_16</th>\n",
       "      <th>measurement_17</th>\n",
       "      <th>failure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>80.10</td>\n",
       "      <td>material_7</td>\n",
       "      <td>material_8</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>10.672</td>\n",
       "      <td>15.859</td>\n",
       "      <td>17.594</td>\n",
       "      <td>15.193</td>\n",
       "      <td>15.029</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.034</td>\n",
       "      <td>14.684</td>\n",
       "      <td>764.100</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>84.89</td>\n",
       "      <td>material_7</td>\n",
       "      <td>material_8</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>12.448</td>\n",
       "      <td>17.947</td>\n",
       "      <td>17.915</td>\n",
       "      <td>11.755</td>\n",
       "      <td>14.732</td>\n",
       "      <td>15.425</td>\n",
       "      <td>14.395</td>\n",
       "      <td>15.631</td>\n",
       "      <td>682.057</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>82.43</td>\n",
       "      <td>material_7</td>\n",
       "      <td>material_8</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>12.715</td>\n",
       "      <td>15.607</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.798</td>\n",
       "      <td>16.711</td>\n",
       "      <td>18.631</td>\n",
       "      <td>14.094</td>\n",
       "      <td>17.946</td>\n",
       "      <td>663.376</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>101.07</td>\n",
       "      <td>material_7</td>\n",
       "      <td>material_8</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>12.471</td>\n",
       "      <td>16.346</td>\n",
       "      <td>18.377</td>\n",
       "      <td>10.020</td>\n",
       "      <td>15.250</td>\n",
       "      <td>15.562</td>\n",
       "      <td>16.154</td>\n",
       "      <td>17.172</td>\n",
       "      <td>826.282</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>188.06</td>\n",
       "      <td>material_7</td>\n",
       "      <td>material_8</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>10.337</td>\n",
       "      <td>17.082</td>\n",
       "      <td>19.932</td>\n",
       "      <td>12.428</td>\n",
       "      <td>16.182</td>\n",
       "      <td>12.760</td>\n",
       "      <td>13.153</td>\n",
       "      <td>16.412</td>\n",
       "      <td>579.885</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id product_code  loading attribute_0 attribute_1  attribute_2  attribute_3  \\\n",
       "0   0            A    80.10  material_7  material_8            9            5   \n",
       "1   1            A    84.89  material_7  material_8            9            5   \n",
       "2   2            A    82.43  material_7  material_8            9            5   \n",
       "3   3            A   101.07  material_7  material_8            9            5   \n",
       "4   4            A   188.06  material_7  material_8            9            5   \n",
       "\n",
       "   measurement_0  measurement_1  measurement_2  ...  measurement_9  \\\n",
       "0              7              8              4  ...         10.672   \n",
       "1             14              3              3  ...         12.448   \n",
       "2             12              1              5  ...         12.715   \n",
       "3             13              2              6  ...         12.471   \n",
       "4              9              2              8  ...         10.337   \n",
       "\n",
       "   measurement_10  measurement_11  measurement_12  measurement_13  \\\n",
       "0          15.859          17.594          15.193          15.029   \n",
       "1          17.947          17.915          11.755          14.732   \n",
       "2          15.607             NaN          13.798          16.711   \n",
       "3          16.346          18.377          10.020          15.250   \n",
       "4          17.082          19.932          12.428          16.182   \n",
       "\n",
       "   measurement_14  measurement_15  measurement_16  measurement_17  failure  \n",
       "0             NaN          13.034          14.684         764.100        0  \n",
       "1          15.425          14.395          15.631         682.057        0  \n",
       "2          18.631          14.094          17.946         663.376        0  \n",
       "3          15.562          16.154          17.172         826.282        0  \n",
       "4          12.760          13.153          16.412         579.885        0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the number of observations and the number of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train.shape:  (26570, 26)\n",
      "test.shape:  (20775, 25)\n"
     ]
    }
   ],
   "source": [
    "print('train.shape: ', train.shape)\n",
    "print('test.shape: ', test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate and visulize a correlation matrix for numeric features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>measurement_16</th>\n",
       "      <th>measurement_17</th>\n",
       "      <th>failure</th>\n",
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       "      <th>id</th>\n",
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       "      <td>-0.029611</td>\n",
       "      <td>0.009524</td>\n",
       "      <td>-0.151220</td>\n",
       "      <td>-0.037559</td>\n",
       "      <td>-0.053744</td>\n",
       "      <td>0.083951</td>\n",
       "      <td>-0.059849</td>\n",
       "      <td>-0.000983</td>\n",
       "      <td>-0.007545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loading</th>\n",
       "      <td>0.004305</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008208</td>\n",
       "      <td>-0.000756</td>\n",
       "      <td>-0.005578</td>\n",
       "      <td>0.002532</td>\n",
       "      <td>0.000303</td>\n",
       "      <td>-0.005203</td>\n",
       "      <td>-0.011792</td>\n",
       "      <td>-0.005599</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007751</td>\n",
       "      <td>-0.007525</td>\n",
       "      <td>0.003594</td>\n",
       "      <td>-0.002407</td>\n",
       "      <td>0.006138</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>-0.008087</td>\n",
       "      <td>-0.000633</td>\n",
       "      <td>0.000305</td>\n",
       "      <td>0.129089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attribute_2</th>\n",
       "      <td>-0.741200</td>\n",
       "      <td>-0.008208</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.538697</td>\n",
       "      <td>0.175387</td>\n",
       "      <td>-0.377971</td>\n",
       "      <td>0.030522</td>\n",
       "      <td>0.005849</td>\n",
       "      <td>-0.011797</td>\n",
       "      <td>0.006811</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000098</td>\n",
       "      <td>-0.004953</td>\n",
       "      <td>0.094038</td>\n",
       "      <td>0.226756</td>\n",
       "      <td>0.049639</td>\n",
       "      <td>0.020921</td>\n",
       "      <td>-0.161242</td>\n",
       "      <td>0.035795</td>\n",
       "      <td>0.004729</td>\n",
       "      <td>0.006337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attribute_3</th>\n",
       "      <td>0.558881</td>\n",
       "      <td>-0.000756</td>\n",
       "      <td>-0.538697</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.370232</td>\n",
       "      <td>0.329233</td>\n",
       "      <td>0.013258</td>\n",
       "      <td>-0.002265</td>\n",
       "      <td>0.015470</td>\n",
       "      <td>-0.004034</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002295</td>\n",
       "      <td>-0.100845</td>\n",
       "      <td>-0.025434</td>\n",
       "      <td>-0.132060</td>\n",
       "      <td>0.028472</td>\n",
       "      <td>-0.049711</td>\n",
       "      <td>0.187519</td>\n",
       "      <td>-0.061520</td>\n",
       "      <td>-0.001418</td>\n",
       "      <td>-0.019222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_0</th>\n",
       "      <td>-0.253626</td>\n",
       "      <td>-0.005578</td>\n",
       "      <td>0.175387</td>\n",
       "      <td>-0.370232</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>-0.083813</td>\n",
       "      <td>-0.004890</td>\n",
       "      <td>-0.006459</td>\n",
       "      <td>0.003304</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009112</td>\n",
       "      <td>0.032779</td>\n",
       "      <td>0.001878</td>\n",
       "      <td>0.033496</td>\n",
       "      <td>-0.031527</td>\n",
       "      <td>0.021907</td>\n",
       "      <td>-0.055431</td>\n",
       "      <td>0.003025</td>\n",
       "      <td>0.003351</td>\n",
       "      <td>0.009646</td>\n",
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       "    <tr>\n",
       "      <th>measurement_1</th>\n",
       "      <td>0.400206</td>\n",
       "      <td>0.002532</td>\n",
       "      <td>-0.377971</td>\n",
       "      <td>0.329233</td>\n",
       "      <td>-0.255255</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005032</td>\n",
       "      <td>-0.008363</td>\n",
       "      <td>0.001535</td>\n",
       "      <td>-0.005587</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004529</td>\n",
       "      <td>0.011986</td>\n",
       "      <td>-0.035312</td>\n",
       "      <td>-0.084605</td>\n",
       "      <td>0.007523</td>\n",
       "      <td>-0.011471</td>\n",
       "      <td>0.060870</td>\n",
       "      <td>-0.004509</td>\n",
       "      <td>-0.004651</td>\n",
       "      <td>-0.010810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_2</th>\n",
       "      <td>0.000128</td>\n",
       "      <td>0.000303</td>\n",
       "      <td>0.030522</td>\n",
       "      <td>0.013258</td>\n",
       "      <td>-0.083813</td>\n",
       "      <td>0.005032</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002751</td>\n",
       "      <td>-0.009797</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001884</td>\n",
       "      <td>0.018695</td>\n",
       "      <td>-0.003877</td>\n",
       "      <td>-0.001808</td>\n",
       "      <td>0.029145</td>\n",
       "      <td>0.014623</td>\n",
       "      <td>0.000138</td>\n",
       "      <td>0.033088</td>\n",
       "      <td>-0.009596</td>\n",
       "      <td>0.015808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_3</th>\n",
       "      <td>-0.005923</td>\n",
       "      <td>-0.005203</td>\n",
       "      <td>0.005849</td>\n",
       "      <td>-0.002265</td>\n",
       "      <td>-0.004890</td>\n",
       "      <td>-0.008363</td>\n",
       "      <td>-0.002751</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.009573</td>\n",
       "      <td>0.009864</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008195</td>\n",
       "      <td>-0.004854</td>\n",
       "      <td>-0.004427</td>\n",
       "      <td>0.002445</td>\n",
       "      <td>0.005893</td>\n",
       "      <td>0.002150</td>\n",
       "      <td>-0.010986</td>\n",
       "      <td>-0.002164</td>\n",
       "      <td>0.080902</td>\n",
       "      <td>0.003577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_4</th>\n",
       "      <td>0.011313</td>\n",
       "      <td>-0.011792</td>\n",
       "      <td>-0.011797</td>\n",
       "      <td>0.015470</td>\n",
       "      <td>-0.006459</td>\n",
       "      <td>0.001535</td>\n",
       "      <td>-0.009797</td>\n",
       "      <td>0.009573</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005113</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009080</td>\n",
       "      <td>-0.012998</td>\n",
       "      <td>-0.002784</td>\n",
       "      <td>-0.002586</td>\n",
       "      <td>-0.002454</td>\n",
       "      <td>-0.001713</td>\n",
       "      <td>-0.004567</td>\n",
       "      <td>0.002929</td>\n",
       "      <td>0.193045</td>\n",
       "      <td>-0.010488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_5</th>\n",
       "      <td>-0.007001</td>\n",
       "      <td>-0.005599</td>\n",
       "      <td>0.006811</td>\n",
       "      <td>-0.004034</td>\n",
       "      <td>0.003304</td>\n",
       "      <td>-0.005587</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>0.009864</td>\n",
       "      <td>0.005113</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003589</td>\n",
       "      <td>0.002170</td>\n",
       "      <td>0.005306</td>\n",
       "      <td>0.000858</td>\n",
       "      <td>-0.009325</td>\n",
       "      <td>0.008720</td>\n",
       "      <td>-0.009101</td>\n",
       "      <td>-0.002996</td>\n",
       "      <td>0.450024</td>\n",
       "      <td>0.018079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_6</th>\n",
       "      <td>0.008734</td>\n",
       "      <td>0.007310</td>\n",
       "      <td>-0.002181</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>0.003731</td>\n",
       "      <td>-0.001562</td>\n",
       "      <td>-0.009157</td>\n",
       "      <td>0.008322</td>\n",
       "      <td>0.001682</td>\n",
       "      <td>0.006405</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006014</td>\n",
       "      <td>0.000138</td>\n",
       "      <td>-0.002167</td>\n",
       "      <td>-0.010889</td>\n",
       "      <td>-0.004767</td>\n",
       "      <td>-0.001128</td>\n",
       "      <td>0.000857</td>\n",
       "      <td>-0.004490</td>\n",
       "      <td>0.328136</td>\n",
       "      <td>0.014791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_7</th>\n",
       "      <td>-0.011881</td>\n",
       "      <td>-0.001773</td>\n",
       "      <td>0.010338</td>\n",
       "      <td>-0.008046</td>\n",
       "      <td>0.000669</td>\n",
       "      <td>-0.016977</td>\n",
       "      <td>0.002012</td>\n",
       "      <td>0.006111</td>\n",
       "      <td>-0.004630</td>\n",
       "      <td>-0.006422</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002793</td>\n",
       "      <td>-0.000729</td>\n",
       "      <td>-0.007548</td>\n",
       "      <td>0.004150</td>\n",
       "      <td>-0.002730</td>\n",
       "      <td>0.003493</td>\n",
       "      <td>-0.003583</td>\n",
       "      <td>-0.009377</td>\n",
       "      <td>0.330328</td>\n",
       "      <td>0.016787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_8</th>\n",
       "      <td>0.000437</td>\n",
       "      <td>-0.002998</td>\n",
       "      <td>-0.001717</td>\n",
       "      <td>-0.006233</td>\n",
       "      <td>-0.005037</td>\n",
       "      <td>0.000286</td>\n",
       "      <td>0.000591</td>\n",
       "      <td>0.001286</td>\n",
       "      <td>0.006374</td>\n",
       "      <td>-0.004960</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000258</td>\n",
       "      <td>-0.003070</td>\n",
       "      <td>-0.014865</td>\n",
       "      <td>-0.001129</td>\n",
       "      <td>-0.007111</td>\n",
       "      <td>-0.003867</td>\n",
       "      <td>0.017337</td>\n",
       "      <td>0.002242</td>\n",
       "      <td>0.492839</td>\n",
       "      <td>0.017119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_9</th>\n",
       "      <td>0.001550</td>\n",
       "      <td>-0.007751</td>\n",
       "      <td>-0.000098</td>\n",
       "      <td>-0.002295</td>\n",
       "      <td>0.009112</td>\n",
       "      <td>0.004529</td>\n",
       "      <td>0.001884</td>\n",
       "      <td>0.008195</td>\n",
       "      <td>0.009080</td>\n",
       "      <td>-0.003589</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.006713</td>\n",
       "      <td>-0.007012</td>\n",
       "      <td>-0.007173</td>\n",
       "      <td>-0.006098</td>\n",
       "      <td>-0.011114</td>\n",
       "      <td>-0.003587</td>\n",
       "      <td>-0.002176</td>\n",
       "      <td>0.145465</td>\n",
       "      <td>-0.003587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_10</th>\n",
       "      <td>-0.029611</td>\n",
       "      <td>-0.007525</td>\n",
       "      <td>-0.004953</td>\n",
       "      <td>-0.100845</td>\n",
       "      <td>0.032779</td>\n",
       "      <td>0.011986</td>\n",
       "      <td>0.018695</td>\n",
       "      <td>-0.004854</td>\n",
       "      <td>-0.012998</td>\n",
       "      <td>0.002170</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006713</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.035420</td>\n",
       "      <td>0.031195</td>\n",
       "      <td>0.031349</td>\n",
       "      <td>0.038033</td>\n",
       "      <td>-0.053829</td>\n",
       "      <td>0.004403</td>\n",
       "      <td>0.000477</td>\n",
       "      <td>-0.001515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_11</th>\n",
       "      <td>0.009524</td>\n",
       "      <td>0.003594</td>\n",
       "      <td>0.094038</td>\n",
       "      <td>-0.025434</td>\n",
       "      <td>0.001878</td>\n",
       "      <td>-0.035312</td>\n",
       "      <td>-0.003877</td>\n",
       "      <td>-0.004427</td>\n",
       "      <td>-0.002784</td>\n",
       "      <td>0.005306</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007012</td>\n",
       "      <td>-0.035420</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.088620</td>\n",
       "      <td>-0.095634</td>\n",
       "      <td>-0.086349</td>\n",
       "      <td>-0.026127</td>\n",
       "      <td>0.091059</td>\n",
       "      <td>-0.011975</td>\n",
       "      <td>-0.004801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_12</th>\n",
       "      <td>-0.151220</td>\n",
       "      <td>-0.002407</td>\n",
       "      <td>0.226756</td>\n",
       "      <td>-0.132060</td>\n",
       "      <td>0.033496</td>\n",
       "      <td>-0.084605</td>\n",
       "      <td>-0.001808</td>\n",
       "      <td>0.002445</td>\n",
       "      <td>-0.002586</td>\n",
       "      <td>0.000858</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007173</td>\n",
       "      <td>0.031195</td>\n",
       "      <td>0.088620</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002399</td>\n",
       "      <td>-0.024977</td>\n",
       "      <td>-0.152583</td>\n",
       "      <td>0.095430</td>\n",
       "      <td>-0.005552</td>\n",
       "      <td>0.004398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_13</th>\n",
       "      <td>-0.037559</td>\n",
       "      <td>0.006138</td>\n",
       "      <td>0.049639</td>\n",
       "      <td>0.028472</td>\n",
       "      <td>-0.031527</td>\n",
       "      <td>0.007523</td>\n",
       "      <td>0.029145</td>\n",
       "      <td>0.005893</td>\n",
       "      <td>-0.002454</td>\n",
       "      <td>-0.009325</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006098</td>\n",
       "      <td>0.031349</td>\n",
       "      <td>-0.095634</td>\n",
       "      <td>0.002399</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005001</td>\n",
       "      <td>0.028483</td>\n",
       "      <td>0.131495</td>\n",
       "      <td>-0.013250</td>\n",
       "      <td>-0.001831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_14</th>\n",
       "      <td>-0.053744</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.020921</td>\n",
       "      <td>-0.049711</td>\n",
       "      <td>0.021907</td>\n",
       "      <td>-0.011471</td>\n",
       "      <td>0.014623</td>\n",
       "      <td>0.002150</td>\n",
       "      <td>-0.001713</td>\n",
       "      <td>0.008720</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011114</td>\n",
       "      <td>0.038033</td>\n",
       "      <td>-0.086349</td>\n",
       "      <td>-0.024977</td>\n",
       "      <td>0.005001</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014073</td>\n",
       "      <td>-0.089798</td>\n",
       "      <td>0.001127</td>\n",
       "      <td>0.006211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_15</th>\n",
       "      <td>0.083951</td>\n",
       "      <td>-0.008087</td>\n",
       "      <td>-0.161242</td>\n",
       "      <td>0.187519</td>\n",
       "      <td>-0.055431</td>\n",
       "      <td>0.060870</td>\n",
       "      <td>0.000138</td>\n",
       "      <td>-0.010986</td>\n",
       "      <td>-0.004567</td>\n",
       "      <td>-0.009101</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003587</td>\n",
       "      <td>-0.053829</td>\n",
       "      <td>-0.026127</td>\n",
       "      <td>-0.152583</td>\n",
       "      <td>0.028483</td>\n",
       "      <td>0.014073</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.028170</td>\n",
       "      <td>0.003160</td>\n",
       "      <td>-0.003544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_16</th>\n",
       "      <td>-0.059849</td>\n",
       "      <td>-0.000633</td>\n",
       "      <td>0.035795</td>\n",
       "      <td>-0.061520</td>\n",
       "      <td>0.003025</td>\n",
       "      <td>-0.004509</td>\n",
       "      <td>0.033088</td>\n",
       "      <td>-0.002164</td>\n",
       "      <td>0.002929</td>\n",
       "      <td>-0.002996</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002176</td>\n",
       "      <td>0.004403</td>\n",
       "      <td>0.091059</td>\n",
       "      <td>0.095430</td>\n",
       "      <td>0.131495</td>\n",
       "      <td>-0.089798</td>\n",
       "      <td>0.028170</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002607</td>\n",
       "      <td>0.002237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_17</th>\n",
       "      <td>-0.000983</td>\n",
       "      <td>0.000305</td>\n",
       "      <td>0.004729</td>\n",
       "      <td>-0.001418</td>\n",
       "      <td>0.003351</td>\n",
       "      <td>-0.004651</td>\n",
       "      <td>-0.009596</td>\n",
       "      <td>0.080902</td>\n",
       "      <td>0.193045</td>\n",
       "      <td>0.450024</td>\n",
       "      <td>...</td>\n",
       "      <td>0.145465</td>\n",
       "      <td>0.000477</td>\n",
       "      <td>-0.011975</td>\n",
       "      <td>-0.005552</td>\n",
       "      <td>-0.013250</td>\n",
       "      <td>0.001127</td>\n",
       "      <td>0.003160</td>\n",
       "      <td>-0.002607</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>failure</th>\n",
       "      <td>-0.007545</td>\n",
       "      <td>0.129089</td>\n",
       "      <td>0.006337</td>\n",
       "      <td>-0.019222</td>\n",
       "      <td>0.009646</td>\n",
       "      <td>-0.010810</td>\n",
       "      <td>0.015808</td>\n",
       "      <td>0.003577</td>\n",
       "      <td>-0.010488</td>\n",
       "      <td>0.018079</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003587</td>\n",
       "      <td>-0.001515</td>\n",
       "      <td>-0.004801</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>-0.001831</td>\n",
       "      <td>0.006211</td>\n",
       "      <td>-0.003544</td>\n",
       "      <td>0.002237</td>\n",
       "      <td>0.033905</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      id   loading  attribute_2  attribute_3  measurement_0  \\\n",
       "id              1.000000  0.004305    -0.741200     0.558881      -0.253626   \n",
       "loading         0.004305  1.000000    -0.008208    -0.000756      -0.005578   \n",
       "attribute_2    -0.741200 -0.008208     1.000000    -0.538697       0.175387   \n",
       "attribute_3     0.558881 -0.000756    -0.538697     1.000000      -0.370232   \n",
       "measurement_0  -0.253626 -0.005578     0.175387    -0.370232       1.000000   \n",
       "measurement_1   0.400206  0.002532    -0.377971     0.329233      -0.255255   \n",
       "measurement_2   0.000128  0.000303     0.030522     0.013258      -0.083813   \n",
       "measurement_3  -0.005923 -0.005203     0.005849    -0.002265      -0.004890   \n",
       "measurement_4   0.011313 -0.011792    -0.011797     0.015470      -0.006459   \n",
       "measurement_5  -0.007001 -0.005599     0.006811    -0.004034       0.003304   \n",
       "measurement_6   0.008734  0.007310    -0.002181     0.000119       0.003731   \n",
       "measurement_7  -0.011881 -0.001773     0.010338    -0.008046       0.000669   \n",
       "measurement_8   0.000437 -0.002998    -0.001717    -0.006233      -0.005037   \n",
       "measurement_9   0.001550 -0.007751    -0.000098    -0.002295       0.009112   \n",
       "measurement_10 -0.029611 -0.007525    -0.004953    -0.100845       0.032779   \n",
       "measurement_11  0.009524  0.003594     0.094038    -0.025434       0.001878   \n",
       "measurement_12 -0.151220 -0.002407     0.226756    -0.132060       0.033496   \n",
       "measurement_13 -0.037559  0.006138     0.049639     0.028472      -0.031527   \n",
       "measurement_14 -0.053744  0.000120     0.020921    -0.049711       0.021907   \n",
       "measurement_15  0.083951 -0.008087    -0.161242     0.187519      -0.055431   \n",
       "measurement_16 -0.059849 -0.000633     0.035795    -0.061520       0.003025   \n",
       "measurement_17 -0.000983  0.000305     0.004729    -0.001418       0.003351   \n",
       "failure        -0.007545  0.129089     0.006337    -0.019222       0.009646   \n",
       "\n",
       "                measurement_1  measurement_2  measurement_3  measurement_4  \\\n",
       "id                   0.400206       0.000128      -0.005923       0.011313   \n",
       "loading              0.002532       0.000303      -0.005203      -0.011792   \n",
       "attribute_2         -0.377971       0.030522       0.005849      -0.011797   \n",
       "attribute_3          0.329233       0.013258      -0.002265       0.015470   \n",
       "measurement_0       -0.255255      -0.083813      -0.004890      -0.006459   \n",
       "measurement_1        1.000000       0.005032      -0.008363       0.001535   \n",
       "measurement_2        0.005032       1.000000      -0.002751      -0.009797   \n",
       "measurement_3       -0.008363      -0.002751       1.000000       0.009573   \n",
       "measurement_4        0.001535      -0.009797       0.009573       1.000000   \n",
       "measurement_5       -0.005587       0.000241       0.009864       0.005113   \n",
       "measurement_6       -0.001562      -0.009157       0.008322       0.001682   \n",
       "measurement_7       -0.016977       0.002012       0.006111      -0.004630   \n",
       "measurement_8        0.000286       0.000591       0.001286       0.006374   \n",
       "measurement_9        0.004529       0.001884       0.008195       0.009080   \n",
       "measurement_10       0.011986       0.018695      -0.004854      -0.012998   \n",
       "measurement_11      -0.035312      -0.003877      -0.004427      -0.002784   \n",
       "measurement_12      -0.084605      -0.001808       0.002445      -0.002586   \n",
       "measurement_13       0.007523       0.029145       0.005893      -0.002454   \n",
       "measurement_14      -0.011471       0.014623       0.002150      -0.001713   \n",
       "measurement_15       0.060870       0.000138      -0.010986      -0.004567   \n",
       "measurement_16      -0.004509       0.033088      -0.002164       0.002929   \n",
       "measurement_17      -0.004651      -0.009596       0.080902       0.193045   \n",
       "failure             -0.010810       0.015808       0.003577      -0.010488   \n",
       "\n",
       "                measurement_5  ...  measurement_9  measurement_10  \\\n",
       "id                  -0.007001  ...       0.001550       -0.029611   \n",
       "loading             -0.005599  ...      -0.007751       -0.007525   \n",
       "attribute_2          0.006811  ...      -0.000098       -0.004953   \n",
       "attribute_3         -0.004034  ...      -0.002295       -0.100845   \n",
       "measurement_0        0.003304  ...       0.009112        0.032779   \n",
       "measurement_1       -0.005587  ...       0.004529        0.011986   \n",
       "measurement_2        0.000241  ...       0.001884        0.018695   \n",
       "measurement_3        0.009864  ...       0.008195       -0.004854   \n",
       "measurement_4        0.005113  ...       0.009080       -0.012998   \n",
       "measurement_5        1.000000  ...      -0.003589        0.002170   \n",
       "measurement_6        0.006405  ...       0.006014        0.000138   \n",
       "measurement_7       -0.006422  ...       0.002793       -0.000729   \n",
       "measurement_8       -0.004960  ...      -0.000258       -0.003070   \n",
       "measurement_9       -0.003589  ...       1.000000       -0.006713   \n",
       "measurement_10       0.002170  ...      -0.006713        1.000000   \n",
       "measurement_11       0.005306  ...      -0.007012       -0.035420   \n",
       "measurement_12       0.000858  ...      -0.007173        0.031195   \n",
       "measurement_13      -0.009325  ...      -0.006098        0.031349   \n",
       "measurement_14       0.008720  ...      -0.011114        0.038033   \n",
       "measurement_15      -0.009101  ...      -0.003587       -0.053829   \n",
       "measurement_16      -0.002996  ...      -0.002176        0.004403   \n",
       "measurement_17       0.450024  ...       0.145465        0.000477   \n",
       "failure              0.018079  ...      -0.003587       -0.001515   \n",
       "\n",
       "                measurement_11  measurement_12  measurement_13  \\\n",
       "id                    0.009524       -0.151220       -0.037559   \n",
       "loading               0.003594       -0.002407        0.006138   \n",
       "attribute_2           0.094038        0.226756        0.049639   \n",
       "attribute_3          -0.025434       -0.132060        0.028472   \n",
       "measurement_0         0.001878        0.033496       -0.031527   \n",
       "measurement_1        -0.035312       -0.084605        0.007523   \n",
       "measurement_2        -0.003877       -0.001808        0.029145   \n",
       "measurement_3        -0.004427        0.002445        0.005893   \n",
       "measurement_4        -0.002784       -0.002586       -0.002454   \n",
       "measurement_5         0.005306        0.000858       -0.009325   \n",
       "measurement_6        -0.002167       -0.010889       -0.004767   \n",
       "measurement_7        -0.007548        0.004150       -0.002730   \n",
       "measurement_8        -0.014865       -0.001129       -0.007111   \n",
       "measurement_9        -0.007012       -0.007173       -0.006098   \n",
       "measurement_10       -0.035420        0.031195        0.031349   \n",
       "measurement_11        1.000000        0.088620       -0.095634   \n",
       "measurement_12        0.088620        1.000000        0.002399   \n",
       "measurement_13       -0.095634        0.002399        1.000000   \n",
       "measurement_14       -0.086349       -0.024977        0.005001   \n",
       "measurement_15       -0.026127       -0.152583        0.028483   \n",
       "measurement_16        0.091059        0.095430        0.131495   \n",
       "measurement_17       -0.011975       -0.005552       -0.013250   \n",
       "failure              -0.004801        0.004398       -0.001831   \n",
       "\n",
       "                measurement_14  measurement_15  measurement_16  \\\n",
       "id                   -0.053744        0.083951       -0.059849   \n",
       "loading               0.000120       -0.008087       -0.000633   \n",
       "attribute_2           0.020921       -0.161242        0.035795   \n",
       "attribute_3          -0.049711        0.187519       -0.061520   \n",
       "measurement_0         0.021907       -0.055431        0.003025   \n",
       "measurement_1        -0.011471        0.060870       -0.004509   \n",
       "measurement_2         0.014623        0.000138        0.033088   \n",
       "measurement_3         0.002150       -0.010986       -0.002164   \n",
       "measurement_4        -0.001713       -0.004567        0.002929   \n",
       "measurement_5         0.008720       -0.009101       -0.002996   \n",
       "measurement_6        -0.001128        0.000857       -0.004490   \n",
       "measurement_7         0.003493       -0.003583       -0.009377   \n",
       "measurement_8        -0.003867        0.017337        0.002242   \n",
       "measurement_9        -0.011114       -0.003587       -0.002176   \n",
       "measurement_10        0.038033       -0.053829        0.004403   \n",
       "measurement_11       -0.086349       -0.026127        0.091059   \n",
       "measurement_12       -0.024977       -0.152583        0.095430   \n",
       "measurement_13        0.005001        0.028483        0.131495   \n",
       "measurement_14        1.000000        0.014073       -0.089798   \n",
       "measurement_15        0.014073        1.000000        0.028170   \n",
       "measurement_16       -0.089798        0.028170        1.000000   \n",
       "measurement_17        0.001127        0.003160       -0.002607   \n",
       "failure               0.006211       -0.003544        0.002237   \n",
       "\n",
       "                measurement_17   failure  \n",
       "id                   -0.000983 -0.007545  \n",
       "loading               0.000305  0.129089  \n",
       "attribute_2           0.004729  0.006337  \n",
       "attribute_3          -0.001418 -0.019222  \n",
       "measurement_0         0.003351  0.009646  \n",
       "measurement_1        -0.004651 -0.010810  \n",
       "measurement_2        -0.009596  0.015808  \n",
       "measurement_3         0.080902  0.003577  \n",
       "measurement_4         0.193045 -0.010488  \n",
       "measurement_5         0.450024  0.018079  \n",
       "measurement_6         0.328136  0.014791  \n",
       "measurement_7         0.330328  0.016787  \n",
       "measurement_8         0.492839  0.017119  \n",
       "measurement_9         0.145465 -0.003587  \n",
       "measurement_10        0.000477 -0.001515  \n",
       "measurement_11       -0.011975 -0.004801  \n",
       "measurement_12       -0.005552  0.004398  \n",
       "measurement_13       -0.013250 -0.001831  \n",
       "measurement_14        0.001127  0.006211  \n",
       "measurement_15        0.003160 -0.003544  \n",
       "measurement_16       -0.002607  0.002237  \n",
       "measurement_17        1.000000  0.033905  \n",
       "failure               0.033905  1.000000  \n",
       "\n",
       "[23 rows x 23 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x20263f79af0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(train.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Features 0 and 3 show strong negative correlation. Regarding the target variable, the correlation is close to zero for most of the variables and very weak for the loading variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                  int64\n",
       "product_code       object\n",
       "loading           float64\n",
       "attribute_0        object\n",
       "attribute_1        object\n",
       "attribute_2         int64\n",
       "attribute_3         int64\n",
       "measurement_0       int64\n",
       "measurement_1       int64\n",
       "measurement_2       int64\n",
       "measurement_3     float64\n",
       "measurement_4     float64\n",
       "measurement_5     float64\n",
       "measurement_6     float64\n",
       "measurement_7     float64\n",
       "measurement_8     float64\n",
       "measurement_9     float64\n",
       "measurement_10    float64\n",
       "measurement_11    float64\n",
       "measurement_12    float64\n",
       "measurement_13    float64\n",
       "measurement_14    float64\n",
       "measurement_15    float64\n",
       "measurement_16    float64\n",
       "measurement_17    float64\n",
       "failure             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# id variable <br>\n",
    "Check if id us unique for all observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id for the train set is unique: True\n",
      "id for the test set is unique: True\n"
     ]
    }
   ],
   "source": [
    "print('id for the train set is unique:', train.shape[0] == len(train.value_counts('id')))\n",
    "print('id for the test set is unique:', test.shape[0] == len(test.value_counts('id')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Since id is a unique identifier it cannot be used for predicting failures --> remove the id column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['product_code', 'loading', 'attribute_0', 'attribute_1', 'attribute_2',\n",
       "       'attribute_3', 'measurement_0', 'measurement_1', 'measurement_2',\n",
       "       'measurement_3', 'measurement_4', 'measurement_5', 'measurement_6',\n",
       "       'measurement_7', 'measurement_8', 'measurement_9', 'measurement_10',\n",
       "       'measurement_11', 'measurement_12', 'measurement_13', 'measurement_14',\n",
       "       'measurement_15', 'measurement_16', 'measurement_17', 'failure'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop('id', axis=1, inplace=True)\n",
    "test.drop('id', axis=1, inplace=True)\n",
    "\n",
    "# check if the id was dropped\n",
    "train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# product_code <br>\n",
    "Calculate value counts and draw a histogram for product_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product_code\n",
       "C    5765\n",
       "E    5343\n",
       "B    5250\n",
       "D    5112\n",
       "A    5100\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.value_counts('product_code')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='product_code', ylabel='Count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(train['product_code'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram shows that there are 5 possible values for product_code. All categories have enough observations <br>\n",
    "<br>\n",
    "Calculate failure probability for each category of the variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product_code\n",
       "A    0.227255\n",
       "B    0.200381\n",
       "C    0.211622\n",
       "D    0.217527\n",
       "E    0.207000\n",
       "Name: failure, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('product_code', axis=0)['failure'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Failure probability for different product codes is similar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# loading <br>\n",
    "Draw a density plot and calculate statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='loading', ylabel='Density'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(train['loading'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    26320.000000\n",
       "mean       127.826233\n",
       "std         39.030020\n",
       "min         33.160000\n",
       "25%         99.987500\n",
       "50%        122.390000\n",
       "75%        149.152500\n",
       "max        385.860000\n",
       "Name: loading, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['loading'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot and calculated statistics show that outstanding and rare values may be present"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## attribute_0 <br>\n",
    "Draw a histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='attribute_0', ylabel='Count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(train['attribute_0'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram shows that there are 2 possible values for the attribute. All the categories have enough observations<br>\n",
    "<br>\n",
    "Calculate failure probability for each category of the variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "attribute_0\n",
       "material_5    0.200381\n",
       "material_7    0.215619\n",
       "Name: failure, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('attribute_0', axis=0)['failure'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Failure probability is similar for both categories of the attribute"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## attribute_1 <br>\n",
    "Draw a histogram and calculate values counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='attribute_1', ylabel='Count'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(train['attribute_1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "attribute_1\n",
       "material_8    10865\n",
       "material_5    10362\n",
       "material_6     5343\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.value_counts('attribute_1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 3 possible values for the attribute. All categories have enough observations <br>\n",
    "<br>\n",
    "Calculate failure probability for each category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "attribute_1\n",
       "material_5    0.20884\n",
       "material_6    0.20700\n",
       "material_8    0.21896\n",
       "Name: failure, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('attribute_1')['failure'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Failure probability is similar for different attribute categories, but slightly higher for material_8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## attribute_2 <br>\n",
    "Draw a histogram and count values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='attribute_2', ylabel='Count'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEHCAYAAACEKcAKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAVsUlEQVR4nO3df/BddX3n8efLRPkhIiCBxSQ0tM1QgVkrpBSwg2xxl3S3Y6gLa5xaWJc2FtH6Y8cd0J31j53M2NXZtTiFmpGW0GXBSKWkVqg0qJ2uCAZEISBD1gj5SoTUX1DdRULf+8c9WW9v7jffm3y+33vz3TwfM3fuOZ/zOee87ydfeXnOufecVBWSJO2vF026AEnS/GaQSJKaGCSSpCYGiSSpiUEiSWqycNIFjNuxxx5by5Ytm3QZkjSv3HfffX9XVYuGLTvogmTZsmVs3rx50mVI0ryS5PHplnlqS5LUxCCRJDUxSCRJTQwSSVITg0SS1MQgkSQ1MUgkSU0MEklSE4NEktTEINGsWLz0RJLM+Fq89MRJlypplh10t0jR3Hhyajtv+viXZuz3ybedM4ZqJI2TRySSpCYGiSSpiUEiSWpikEiSmsxZkCT54yRPJ3mor+2YJHcmeax7P7pv2VVJtiZ5NMkFfe1nJHmwW3Z1knTthyT5ZNd+T5Jlc/VZJEnTm8sjkuuBlQNtVwKbqmo5sKmbJ8kpwGrg1G6da5Is6Na5FlgDLO9eu7d5GfD9qvp54L8Bvz9nn0SSNK05C5Kq+hvgewPNq4D13fR64MK+9pur6rmq2gZsBc5McgJwZFXdXVUF3DCwzu5t3QKcv/toRZI0PuO+RnJ8Ve0A6N6P69oXA9v7+k11bYu76cH2f7ROVe0Cfgi8YthOk6xJsjnJ5p07d87SR5EkwYFzsX3YkUTtpX1v6+zZWLWuqlZU1YpFi4Y+u16StJ/GHSRPdaer6N6f7tqngKV9/ZYAT3btS4a0/6N1kiwEXs6ep9IkSXNs3EGyEbi0m74UuK2vfXX3TayT6F1Uv7c7/fVskrO66x+XDKyze1sXAXd111EkSWM0Z/faSnITcB5wbJIp4IPAh4ANSS4DngAuBqiqLUk2AA8Du4ArquqFblOX0/sG2GHA7d0L4DrgT5NspXcksnquPoskaXpzFiRV9eZpFp0/Tf+1wNoh7ZuB04a0/x+6IJIkTc6BcrFdkjRPGSSSpCYGiSSpiUEiSWpikEiSmhgkkqQmBokkqYlBIklqYpBIkpoYJJKkJgaJJKmJQSJJamKQSJKaGCSSpCYGiSSpiUEiSWpikEiSmhgkkqQmBokkqYlBIklqYpBIkpoYJJKkJgaJJKmJQSJJamKQSJKaGCSSpCYGiSSpiUEiSWpikEiSmhgkkqQmEwmSJO9JsiXJQ0luSnJokmOS3Jnkse796L7+VyXZmuTRJBf0tZ+R5MFu2dVJMonPI0kHs7EHSZLFwO8BK6rqNGABsBq4EthUVcuBTd08SU7plp8KrASuSbKg29y1wBpgefdaOcaPIklicqe2FgKHJVkIHA48CawC1nfL1wMXdtOrgJur6rmq2gZsBc5McgJwZFXdXVUF3NC3jiRpTMYeJFX1beAjwBPADuCHVfU54Piq2tH12QEc162yGNjet4mprm1xNz3Yvocka5JsTrJ5586ds/lxJOmgN4lTW0fTO8o4CXgl8NIkb9nbKkPaai/tezZWrauqFVW1YtGiRftasiRpLyZxauv1wLaq2llVzwOfBs4BnupOV9G9P931nwKW9q2/hN6psKluerBdkjRGkwiSJ4CzkhzefcvqfOARYCNwadfnUuC2bnojsDrJIUlOondR/d7u9NezSc7qtnNJ3zqSpDFZOO4dVtU9SW4B7gd2AV8F1gFHABuSXEYvbC7u+m9JsgF4uOt/RVW90G3ucuB64DDg9u4lSRqjsQcJQFV9EPjgQPNz9I5OhvVfC6wd0r4ZOG3WC5QkjcxftkuSmhgkkqQmBokkqYlBIklqYpBIkpoYJJKkJgaJJKmJQSJJamKQSJKaGCSSpCYGiSSpiUEiSWpikEiSmhgkkqQmBokkqYlBIklqYpBIkpoYJJKkJgaJJKmJQSJJamKQSJKaGCSSpCYGiSSpiUEiSWpikEiSmhgkkqQmBokkqYlBIklqYpBIkpoYJJKkJgaJJKnJRIIkyVFJbknyjSSPJDk7yTFJ7kzyWPd+dF//q5JsTfJokgv62s9I8mC37OokmcTnkaSD2UhBkuS1o7Ttgz8A7qiqXwBeDTwCXAlsqqrlwKZuniSnAKuBU4GVwDVJFnTbuRZYAyzvXisbapIk7YdRj0g+NmLbjJIcCZwLXAdQVT+pqh8Aq4D1Xbf1wIXd9Crg5qp6rqq2AVuBM5OcABxZVXdXVQE39K0jSRqThXtbmORs4BxgUZL39i06ElgwfK0Z/SywE/iTJK8G7gPeBRxfVTsAqmpHkuO6/ouBL/etP9W1Pd9ND7YP+xxr6B25cOKJJ+5n2ZKkYWY6InkJcAS9wHlZ3+sZ4KL93OdC4HTg2qp6DfAjutNY0xh23aP20r5nY9W6qlpRVSsWLVq0r/VKkvZir0ckVfVF4ItJrq+qx2dpn1PAVFXd083fQi9InkpyQnc0cgLwdF//pX3rLwGe7NqXDGmXJI3RqNdIDkmyLsnnkty1+7U/O6yq7wDbk5zcNZ0PPAxsBC7t2i4FbuumNwKrkxyS5CR6F9Xv7U6DPZvkrO7bWpf0rSNJGpO9HpH0+RTwR8AngBdmYb/vBG5M8hLgm8Bb6YXahiSXAU8AFwNU1ZYkG+iFzS7giqraXcPlwPXAYcDt3UuSNEajBsmuqrp2tnZaVQ8AK4YsOn+a/muBtUPaNwOnzVZdM1m89ESenNo+Y79XLlnKt7c/MYaKJGnyRg2Sv0jyduBW4LndjVX1vTmp6gD15NR23vTxL83Y75NvO2cM1UjSgWHUINl97eJ9fW1F76u8kqSD2EhBUlUnzXUhkqT5aaQgSXLJsPaqumF2y5EkzTejntr6pb7pQ+ldFL+f3m1JJEkHsVFPbb2zfz7Jy4E/nZOKJEnzyv7eRv7H9H4YKEk6yI16jeQv+Ol9rBYArwI2zFVRkqT5Y9RrJB/pm94FPF5VU9N1liQdPEY6tdXdvPEb9O78ezTwk7ksSpL6LV56IklGei1e6qMixm3UU1v/Bvgw8AV6t2//WJL3VdUtc1ibJAGj31UCvLPEJIx6ausDwC9V1dMASRYBf03vFvCSpIPYqN/aetHuEOl8dx/WlSTNoVFP/c3Vab9Rj0juSPJXwE3d/JuAz85JRZKkfTLpG8rO9Mz2n6f3LPX3JXkj8Cv0rpHcDdw4JxVJkuaVmU5PfRR4FqCqPl1V762q99A7Gvno3JYmSZoPZgqSZVX19cHG7oFSy+akIknSvDJTkBy6l2WHzWYhkqT5aaYg+UqS3xls7J6rft/clCRJmk9m+tbWu4Fbk/wmPw2OFcBLgN+Yw7okSfPEXoOkqp4Czknyz4DTuua/rKq75rwySdK8MOrzSD4PfH6Oa5EkzUP+Ol2S1MQgkSQ1MUgkSU0MEklSE4NEktTEIJEkNTFIJElNDBJJUpOJBUmSBUm+muQz3fwxSe5M8lj3fnRf36uSbE3yaJIL+trPSPJgt+zqJJnEZ5Gkg9kkj0jeBTzSN38lsKmqlgObunmSnAKsBk4FVgLXJFnQrXMtsAZY3r1Wjqd0SdJuEwmSJEuAfwV8oq95FbC+m14PXNjXfnNVPVdV24CtwJlJTgCOrKq7q6qAG/rWkSSNyaSOSD4K/AfgH/rajq+qHQDd+3Fd+2Jge1+/qa5tcTc92C5JGqOxB0mSXweerqpRn2cy7LpH7aV92D7XJNmcZPPOnTtH3K0kaRSTOCJ5LfCGJN8CbgZ+Ncl/B57qTlfRvT/d9Z8ClvatvwR4smtfMqR9D1W1rqpWVNWKRYsWzeZnkaSD3tiDpKquqqolVbWM3kX0u6rqLcBG4NKu26XAbd30RmB1kkOSnETvovq93emvZ5Oc1X1b65K+dSRJYzLS80jG5EPAhu4xvk8AFwNU1ZYkG4CHgV3AFVX1QrfO5cD19J4ff3v3kiSN0USDpKq+AHyhm/4ucP40/dYCa4e0b+anT26UJE2Av2yXJDUxSCRJTQwSSVITg0SS1MQgkSQ1MUgkSU0MEklSE4NEktTEIJEkNTFIJElNDBJJUhODRJLUxCCRJDUxSCRJTQwSSVITg0SS1MQgkSQ1MUgkSU0MEklSE4NEktTEIJEkNTFIJElNDBJJUhODRJLUxCCRJDUxSCRJTQwSSVITg0SS1MQgkSQ1MUgkSU0MEklSk7EHSZKlST6f5JEkW5K8q2s/JsmdSR7r3o/uW+eqJFuTPJrkgr72M5I82C27OknG/Xkk6WA3iSOSXcC/r6pXAWcBVyQ5BbgS2FRVy4FN3TzdstXAqcBK4JokC7ptXQusAZZ3r5Xj/CCSpAkESVXtqKr7u+lngUeAxcAqYH3XbT1wYTe9Cri5qp6rqm3AVuDMJCcAR1bV3VVVwA1960iSxmSi10iSLANeA9wDHF9VO6AXNsBxXbfFwPa+1aa6tsXd9GD7sP2sSbI5yeadO3fO6meQpIPdxIIkyRHAnwHvrqpn9tZ1SFvtpX3Pxqp1VbWiqlYsWrRo34uVJE1rIkGS5MX0QuTGqvp01/xUd7qK7v3prn0KWNq3+hLgya59yZB2SdIYTeJbWwGuAx6pqv/at2gjcGk3fSlwW1/76iSHJDmJ3kX1e7vTX88mOavb5iV960iSxmThBPb5WuC3gAeTPNC1vR/4ELAhyWXAE8DFAFW1JckG4GF63/i6oqpe6Na7HLgeOAy4vXtJksZo7EFSVX/L8OsbAOdPs85aYO2Q9s3AabNXnSRpX/nLdklSE4NEktTEIJEkNTFIJElNDBJJUhODRJLUxCCRJDUxSCRJTQwSSVITg0SS1MQgkSQ1MUgkSU0MEklSE4NEktTEIJEkNTFIJElNDBJJUhODRJLUxCCRJDUxSCRJTQwSSVITg0SS1MQgkSQ1MUgkSU0MEklSE4NEktTEIJEkNTFIJElNDBJJUhODRJLUxCCRJDWZ90GSZGWSR5NsTXLlpOuRpIPNvA6SJAuAPwR+DTgFeHOSUyZblSQdXOZ1kABnAlur6ptV9RPgZmDVhGuSpINKqmrSNey3JBcBK6vqt7v53wJ+uareMdBvDbCmmz0ZeHQ/d3ks8Hf7ue5csq59Y1377kCtzbr2TUtdP1NVi4YtWLj/9RwQMqRtj2SsqnXAuuadJZurakXrdmabde0b69p3B2pt1rVv5qqu+X5qawpY2je/BHhyQrVI0kFpvgfJV4DlSU5K8hJgNbBxwjVJ0kFlXp/aqqpdSd4B/BWwAPjjqtoyh7tsPj02R6xr31jXvjtQa7OufTMndc3ri+2SpMmb76e2JEkTZpBIkpoYJEMk+VaSB5M8kGTzkOVJcnV3W5avJzn9AKnrvCQ/7JY/kOQ/jamuo5LckuQbSR5JcvbA8kmN10x1jX28kpzct78HkjyT5N0DfcY+XiPWNam/r/ck2ZLkoSQ3JTl0YPmk/r5mqmtS4/WurqYtg/+G3fLZH6+q8jXwAr4FHLuX5f8SuJ3e71jOAu45QOo6D/jMBMZrPfDb3fRLgKMOkPGaqa6JjFff/hcA36H3Q6+Jj9cIdY19vIDFwDbgsG5+A/BvJz1eI9Y1ifE6DXgIOJzel6n+Glg+1+PlEcn+WQXcUD1fBo5KcsKki5qEJEcC5wLXAVTVT6rqBwPdxj5eI9Y1aecD/6uqHh9on/Tf13R1TcpC4LAkC+n9B3Lwt2KTGq+Z6pqEVwFfrqofV9Uu4IvAbwz0mfXxMkiGK+BzSe5L7/YqgxYD2/vmp7q2SdcFcHaSryW5PcmpY6jpZ4GdwJ8k+WqSTyR56UCfSYzXKHXB+Mer32rgpiHtk/r72m26umDM41VV3wY+AjwB7AB+WFWfG+g29vEasS4Y/9/XQ8C5SV6R5HB6Rx9LB/rM+ngZJMO9tqpOp3dX4SuSnDuwfKRbs8yBmeq6n97piFcDHwP+fAw1LQROB66tqtcAPwIGb+c/ifEapa5JjBcA6f2A9g3Ap4YtHtI2lu/pz1DX2McrydH0/h/0ScArgZcmectgtyGrzul4jVjX2Merqh4Bfh+4E7gD+Bqwa6DbrI+XQTJEVT3ZvT8N3ErvLsP9JnJrlpnqqqpnqurvu+nPAi9OcuwclzUFTFXVPd38LfT+Az7YZ9zjNWNdExqv3X4NuL+qnhqybJK3/pm2rgmN1+uBbVW1s6qeBz4NnDPQZxLjNWNdk/r7qqrrqur0qjoX+B7w2ECXWR8vg2RAkpcmednuaeBf0Dtc7LcRuKT79sNZ9A5rd0y6riT/JEm66TPp/ft+dy7rqqrvANuTnNw1nQ88PNBt7OM1Sl2TGK8+b2b600djH69R6prQeD0BnJXk8G7f5wOPDPSZxHjNWNek/r6SHNe9nwi8kT3/PWd9vOb1LVLmyPHArd2//0Lgf1TVHUl+F6Cq/gj4LL1zj1uBHwNvPUDqugi4PMku4H8Dq6v7msYceydwY3da5JvAWw+A8RqlromMV3fu+p8Db+trm/h4jVDX2Merqu5Jcgu900S7gK8C6yY9XiPWNan/Pf5ZklcAzwNXVNX353q8vEWKJKmJp7YkSU0MEklSE4NEktTEIJEkNTFIJElNDBJJUhODRNpPSd7fN31UkrfP0P9L3ft5ST6zj/u6MMkp+1nne5M8nN4twzcl+Zn92Y40HYNE2n/v75s+ChgaJEkWAFTV4K099sWFwH4FCb0fy62oqn9K71Yx/6WhDmkP/rJdGkGSP6d3f6JDgT+gd3fhw5I8AGyh9wyPn+vm7wT+EvggvTvD/iJwSpK/r6ojuk0emeRW4GTgb4C3V9U/9PdJchHw68A6ejdSfF2S/wj8624bfwgsovfr5N+pqm8Mq72qPt83+2Vg8OaCUhODRBrNv6uq7yU5DPgK8DrgHVX1iwBJlgGn9c2fR++mmqdV1bYh2zuT3hHG4/Tu0vpGekcLe6iqLyXZSO8hSbd0298E/G5VPZbkl4FrgF8d4XNcRu+hRtKsMUik0fxekt0PCFoKLB9hnXunCZHdy74JkOQm4FeYJkgGJTmC3p1mP9Xdew3gkBHWewuwgl4ISrPGIJFm0B1dvB44u6p+nOQL9E5xzeRHe1k2eJO7GtI+3T5eBPxg99HPKJK8HvgA8Lqqem7U9aRReLFdmtnLge93IfIL9J5zDfB8khd3088CL9uHbZ6Z5KQkLwLeBPxt1/5Ukld17f2PSP1/26+qZ4BtSS4G6G4H/urpdpTkNcDHgTd0z7KRZpVBIs3sDmBhkq8D/5neBWvoXQT/epIbq+q7wP9M8lCSD4+wzbuBD9F7psw2eg8qg95THD8D3EXvQv1uNwPvS++xwT8H/CZwWZKv0bvYv2ov+/owcAS9U2EPdNdbpFnjbeQlSU08IpEkNfFiu/T/iSQfAC4eaP5UVa2dRD06eHhqS5LUxFNbkqQmBokkqYlBIklqYpBIkpr8X2tc5m4tLbWQAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(train['attribute_2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "attribute_2\n",
       "9     5100\n",
       "8     5250\n",
       "5     5765\n",
       "6    10455\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.value_counts('attribute_2', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 4 possible values for the attribute. All categories have enough observations <br>\n",
    "<br>\n",
    "Calculate failure probability for each category of the variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "attribute_2\n",
       "5    0.211622\n",
       "6    0.212147\n",
       "8    0.200381\n",
       "9    0.227255\n",
       "Name: failure, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('attribute_2')['failure'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Failure probability is similar for different attribute_2 values, but the relationship is not linear"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the dtype from int to object to make it easier for algorithms to learn nonlinear dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['attribute_2'] = train['attribute_2'].astype(object)\n",
    "test['attribute_2'] = test['attribute_2'].astype(object)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## attribute_3 <br>\n",
    "Draw a histogram and calculate value counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='attribute_3', ylabel='Count'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(train['attribute_3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "attribute_3\n",
       "5     5100\n",
       "6     5112\n",
       "9     5343\n",
       "8    11015\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.value_counts('attribute_3', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 4 possible values for the attribute. All categories have enough observations<br>\n",
    "<br>\n",
    "Calculate failure probability for each category of the variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "attribute_3\n",
       "5    0.227255\n",
       "6    0.217527\n",
       "8    0.206264\n",
       "9    0.207000\n",
       "Name: failure, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('attribute_3')['failure'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the dtype of attribute_3 from int to object to make it easier for algorithms to learn nonlinear dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['attribute_3'] = train['attribute_3'].astype(object)\n",
    "test['attribute_3'] = test['attribute_3'].astype(object)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Measurements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## measurement_0 <br>\n",
    "Draw a histogram, calculate statistics and failure probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='measurement_0', ylabel='Count'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(train['measurement_0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    26570.000000\n",
       "mean         7.415883\n",
       "std          4.116690\n",
       "min          0.000000\n",
       "25%          4.000000\n",
       "50%          7.000000\n",
       "75%         10.000000\n",
       "max         29.000000\n",
       "Name: measurement_0, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['measurement_0'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='measurement_0', ylabel='failure'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=train['measurement_0'], y=train.groupby('measurement_0')['failure'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Values of measurement_0 around 11 correspond with the highest failure probability. Low failure probability for 17 may be considered as a result of low number of observations with that value of the variable <br>\n",
    "It may be useful to group variable's values using its quantiles. This way we'll treat the variable almost like categorical (taking into account nonlinear dependencies), but avoid creating categories with low number of observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group values into 10 quantiles and calculate failure probability for each obtained category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "measurement_0_grouped\n",
       "(-0.001, 3.0]    0.205929\n",
       "(3.0, 4.0]       0.213596\n",
       "(4.0, 5.0]       0.212379\n",
       "(5.0, 6.0]       0.218040\n",
       "(6.0, 7.0]       0.215520\n",
       "(7.0, 8.0]       0.207163\n",
       "(8.0, 9.0]       0.196230\n",
       "(9.0, 11.0]      0.220663\n",
       "(11.0, 13.0]     0.211714\n",
       "(13.0, 29.0]     0.225314\n",
       "Name: failure, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['measurement_0_grouped'] = pd.qcut(train['measurement_0'], 10)\n",
    "test['measurement_0_grouped'] = pd.cut(\n",
    "    test['measurement_0'], \n",
    "    pd.IntervalIndex(train['measurement_0_grouped'].unique()))\n",
    "    \n",
    "train.groupby('measurement_0_grouped')['failure'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## measurement_1 <br>\n",
    "Draw a histogram, calculate statistics and failure probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='measurement_1', ylabel='Count'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(train['measurement_1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    26570.000000\n",
       "mean         8.232518\n",
       "std          4.199401\n",
       "min          0.000000\n",
       "25%          5.000000\n",
       "50%          8.000000\n",
       "75%         11.000000\n",
       "max         29.000000\n",
       "Name: measurement_1, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['measurement_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='measurement_1', ylabel='failure'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=train['measurement_1'], y=train.groupby('measurement_0')['failure'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generally the lowest probability of a failure correnponds with values of measurement_1 around 4-5. As before, we can group variable's values and calculate failure probability for each obtained category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "measurement_1_grouped\n",
       "(-0.001, 4.0]    0.218951\n",
       "(4.0, 6.0]       0.215498\n",
       "(6.0, 7.0]       0.217476\n",
       "(7.0, 9.0]       0.210335\n",
       "(9.0, 10.0]      0.220680\n",
       "(10.0, 13.0]     0.199688\n",
       "(13.0, 29.0]     0.210691\n",
       "Name: failure, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group values into quantiles\n",
    "train['measurement_1_grouped'] = pd.qcut(train['measurement_1'], 7)\n",
    "test['measurement_1_grouped'] = pd.cut(\n",
    "    test['measurement_1'], \n",
    "    pd.IntervalIndex(train['measurement_1_grouped'].unique()))\n",
    "\n",
    "# calculate failure probability for each quantile\n",
    "train.groupby('measurement_1_grouped')['failure'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## measurement_2 <br>\n",
    "Draw a histogram, calculate statistics and failure probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='measurement_2', ylabel='Count'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(train['measurement_2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    26570.000000\n",
       "mean         6.256568\n",
       "std          3.309109\n",
       "min          0.000000\n",
       "25%          4.000000\n",
       "50%          6.000000\n",
       "75%          8.000000\n",
       "max         24.000000\n",
       "Name: measurement_2, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['measurement_2'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='measurement_2', ylabel='failure'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iaJ9u+z02sFcB04r78Y8V24i3cuUeCgSoseZzxnRYk0EgInWHf/4KFALXAUcClwFXeF+ayWaRuNvmzoNPvL+BYED4SsmIRh8/c9IQKmrjzF+zo1WvFw6KjRwyJg2a2yM4UkRGAZeQnIymBvhP4CpgdQZqM1msNuYQaMM+wfod1by5qpxzDh9K/x6N94WYMqIPQ3sXMLeVJ41tCKkx6dFcENwHvAyMBxamvkrrfTddWGXEIdyGhm+PvbuBwrwgX5w6vMllAiKcMWkIK7dWsra8qsXXDAWEWieB61pXdGM6oslPsqr+XlUPBR5S1TGqOrr+99a8uIjMEJFVIrJGRG5qZrlpIpIQkS+243cwPqiKOq0eMbRiy17eX7+LC6cOp0dB820kTh0/iLxQoFVDSUUE1E4YG9NRLX6SVfWb7XlhEQkCfwDOACYAF4vIhCaW+29gXnvex2SeqlIda901BKrKo++sp09hmHMmD21x+R4FIU4cV8Q/V5e3qpeQjRwypuO8vCTzKGCNqq5V1RjwFI1PaPMd4Dlgu4e1mDSKOi6otKp99MKNu1m+eS8XTRtJQSvnNT5z0hBijstrK7e1anm7lsCYjvEyCIYBm+rdLkvdt4+IDAPOJ3k+okkiMktESkWktLy8PO2FmrZJ9hhq+bi8q8pj72xgUK98TpswqNWvf1BRD8YP7sncpVtwtfn3CYhQG7MhpMZ0hJdB0NjmYsNP9V3Ajara7CdZVe9X1RJVLSkqKkpXfaadok6iVUNH3/54B2t3VHPp0aPaPHnNWZOGsLkiwpJNe5pdzkYOGdNxXgZBGVB/wPhwYHODZUqAp0RkPfBF4B4ROc/Dmkwa7I3ECbfQbM5JuDz+3gaK+xdy/Li2h3dr+w8lryWwPQJjOsLLIFgAjBWR0SKSB1wEzKm/QGoEUrGqFgPPAt9S1Rc8rMmkQVXUaXGu4FdWbmNLRYTLjilu18Q1re0/FA4GqInbHoHp/CIezsjnWRCoqgNcS3I00ErgaVVdLiLXiMg1Xr2v8V5VCxPW1006c+jgnkwrbl1b6ca0pv9QQISEq3bC2HRqVVGHmXfP5/63PvHk9Vs/N2A7qOpcYG6D+xo9Mayq/+FlLSY94gkXx1WCgaa38usmnfnh6Ye0e2J62L//0MVHjWw2fGIJt8W9FGNykesq33tqMWvKq5g4tLcn72GfHNMm0Ra2vOsmnTkyNelMR7W2/1Dc9ghMJ/W7V1bz6spt/OScCXzu4AGevIcFgWmTlo5T1k06c3lq0pmOqus/1NxJ44AI0Q7Mn2xMtpqzZDN3v7GGi48auW8iJy9YEJg2qYk6BJs43PPZpDMD9k0601F1/Yc+2lrJJ030HwoFpFVXIRuTS5aWVfCDZ5ZwVHE/fnruYR06zNoSCwLTJpWRpkcMfTbpTHq3XOr6DzXVlTQUCNgQUtOpbK+M8I1HSxnQI597L53q+fkvCwLTKqpKRU2c3TXxRk/abq2IMG/5VqYfeuCkMx21X/+hyIFb/uGgUBOzPQLTOUSdBFc/tpC9kTgPXF7SZNv2dLIgMM1yXWVnVZSFG3ezcONuggFpNAieeH8DAREumtb4pDMdta//0EcH9h8KBQNEHZeEtaM2OU5VuXn2Mj7YuIfffXkyE4b2ysj7WhCYRjkJly17anlv3U6WflqBm4CiHvn0yD9wxPG+SWcmD/Fs66Wl/kOCNZ8zue9Pb6/juUVlXH/qOGZMHJKx97UgMPuJOgk27qzm3bU7+WhrJXnBIP2759Mtr+nOoXWTzlzYzKQz6VDXf2hxE/2HLAjSx/auMu+fq7bzy7krOXPSYL5z8sEZfW8LAgNATcxhzfZK3v1kJ2t3VNM9L8SAHvktnqRamZp05oKpw+lZEPa0xrr+Q42dNFYgmrATxh21bW+EWY+WcsmD77J+R7Xf5XQZn5RX8Z0nP2D84F785kuTCTRzwaYXLAi6uIraOCs2V/De2l1srYjQpzCP/t3zCbWiW6iq8khq0plzWzHpTEc1138oFAhQYyOH2k1VeXrBJk793Zu8ubqchCq3vLAUbaENuOm4ito433iklLxggAeuKKEwz9OGD42yIOiCXFfZVR1j0cbdLNqwiz01cfp3z6N3t7w2NYhbtHFPctKZkhGtnnSmo5rqP5TsQmojh9pj064aLn/ofX743IccOqQXL3/veK783Gjmr9nJ7EWf+l1ep+YkXL7z5Ads2l3DfZcdybA0j7hrrcxHj/FNIjUCaN2OampiCQrzggzoUdCu13JTU1AO6pXPaamVcyY01X/I5iVoO9dN/h/+et4qBPjFeRP56lEjCQSEGRMH8966nfzipRWcNH4g/brn+V1up3TH3z/irdXl/PeFk5hW3M+3OmyPoAuIOS6bdtXwzic7WLFlL8GAMKBHfod2QeevSU46c0k7Jp3pqMb6D4UCQq2TsEMZrfRJeRVf/uM73PbiimSw3nAClx4zat+x6YAIv7rgcCojDr94aYXP1XZOz5Ru4sG31/EfnyvmK9NG+lqL7RF0Yq6rrNtRzad7alCFngXhtKy0nYTLY++mJp0Zm/kZ4+r3HzrxkIEAycvvNdmFND+UmcNUuchJuNz/r7Xc9erHdAsH+e2XJnPB1GGNti84ZHBPrj5hDH944xMunDqc4zxqeNYVLdywm1ueX8bnDx7ArWcd6nc5tkfQmUWcBBt31dCrII9+3fPTtuX+6srtqUlnRjXbjtorARHObKT/kGJDSJuzfHMF590zn1+/vIpTxg/klRuO58Ijhzfbw+Y7J4+luH8hNz+/1NOJUbqSzXtqufqxhQztU8DdXz2iVQMzvOZ/BcZTASGtK+uok+DJBRtTk874d0zzlCb6D1kQHCjqJPjNvFXMvHs+Wyui3HvJVO699EgG9mz5/FBBOMjt509iw84a/u/1jzNQbedWG0sw67FSIvEED15RQp/C7Dj3YkFg2uSlD5OTzlx+bLGn3RBb0lj/oQBiW60NLNq4m7N//zZ3v7GGc6cM5dUbjueMSW27YvW4gwdw4dTh/PHNtXy0da9HlXZ+qsoPnl3C8s17+f3FUzh4YE+/S9rHgsC0yuY9tfx18ac8vXATR47qy8Rh3syU1BZnpfoPvZrqPxQOBqiOWRBA8gLBn/9tBRfe+2+qow5/vnIav/vylHZvgd5y1qH0LAjxo9lLce2q43a555+f8LcPt3DjjPGcPH6Q3+Xsx04Wm0bFEy7LN+9lwfpdlK7fxeaK5AVcI/sV8vXjRvtcXdKYVP+hvy/dwrmThxIK2rwEAP/+ZAc3PbeUjbtquPSYkdw4Y3yHr/ru1z2PH589gRueXsIT723gsmOL01NsF/GP5Vu5c94qzj9iGFcfP8bvcg5gQWD22VkVpXTDbko37GLJpgpq4wnCQWHSsD6cM3koJaP6Mbh3+6478MpZk4bw21dWs3jTHiYP70NVNO53Sb7ZG4nzq7kf8eT7GynuX8hTs47hmDH90/b65x8xjOcWlfHrl1cxfcLgrPtbyFYfbd3L9X9ZzOQRffjVBZN8PaTaFAuCLizhKh9vq2RBauW/tjzZW2ZAjzxOGFfEtOK+HD68T8auGm6P4w4ewINvr2Pu0i1MHdkXx1XiCTfj1zb47bWV27jl+WVsr4ww6/gxXH/quGYbBbaHiHD7eZM4/a63uG3Ocu677Mi0vn5ntKs6xjceLaV7foj7Lzsyaz9LFgRdTFXEYdHG3SzYsItFG3azN+IQEBg/uBeXHzuKaaP6Map/YVZutTSmrv/Qc4vK2L43QigoxJyuEQROwuWNVeU8+f5GXv9oO+MG9eC+y45jyog+nr1n8YDuXHfKWO6ct4p/LN+a0avKc0084fKtJxaybW+Up68+lkG9sncPyoKgk1NV1u+oZsGGXZSu381HW/fiKvQsCHHkqL5MG9WPI0b28bxzqJdmTBzMc4vKeHn5Vs4+PHkCubv3kzr5Zm15FU+XlvHcojLKK6MM6JHPf04fx6wTxmTkYrpZx49hzuLN/GTOcj538IBG56gw8NMXl/Pu2l3c9ZUpnoZzOtj/YCdVGYnzwFtreeK9jeysjgEwpqg7XzpyBCWj+jJ2UE9fLgbzwsCen/UfOv2wQUSdzjdyqCbmMHfpVp5esIn31+8iGBBOOqSIL5eM4KTxAzO6BxQOBvjVhZO48N5/85t5q7jt3MMy9t654rF3N/D4uxu55oSDOO+IYX6X0yILgk5mbyTOI/PX8+Db66iojTNpWC8uPmokJaP6ZmTuU7+cOWkI763bxaINexhd1N3vctJCVfmwrIK/lG5izuLNVEUdivsX8sMZh/DFqcMZ6OOhhqkj+3LZMaN45J31nHfEsKzf4s2kdz7ZyU/nLOfk8QP5wemH+F1Oq1gQdBJ7I3Eenr+eB/+1lr0Rh1MPHcTVJ4ymJpqgX2c+TpJS13/olZXbOH9q9m+BNWd3dYznP/iUp0s38dHWSgrCAc6cNISvlIzgqNH9sub8zQ9OP4R5y7fyo9lLmXPtcV3ivExLNu2q4VtPLKR4QHf+96IpObPXbUGQ4ypqkwHwp7c/C4DvnjKWScN7UxNzWLBul98lZkRd/6EH317Hmu1VjB2UPVdttobrKm+v2cFfSjfxyvJtxBIuk4f35vbzJ3LO5KH0ysJzOD0Lwvz03Ilc8/hCHnp7HVefcJDfJfmqKupw1SOluAoPXl6SU+fdLAhyVEVtnD/PX8ef3l5HZcRh+oRkAGTDFb9+OWX8IB59ZwOPv7eBgb0KGNqngKIerZttzS9lu2t4prSMZxeW8emeWvoUhrnkmJF8ZdoIxg/u5Xd5LZoxcTDTJwzif15dzZmThjCiX6HfJfnCdZXr/7KYNeVVPHLlURQPyK3DkxYEOaaiNs5Db6/jofnJADhtwiCu6+IBUKdHQYhTDh3I35dtZf6afwPJpnsDexYwpE8BQ3oXMLhXN4b0rne7dzcG9cxsWESdBK+s2MZfFmzi7dScCp8/eAA3nTGe0w4blBVttAf2av3hxJ/NPIxTf/smt7ywjEeunJY1h64y6X9eXc0rK7Zx2zkT+PzY3GvXbUGQIypq4vxp/jr+nAqA0w9LBsBhQy0A6rvs6FGcN2Uo4VCALRURtlZE2FIRYUtFLR9treSNj8qpbdCYLiBQ1DOfwb27MaTX/iExtHcBA3rkE0u41MQS1MQcamMJamKJ1HeHmnhi3301sQSReOr+2Gf319a7ryaWIOEqw/p047qTx/KlkuEM75tdW9JtGfM+pHc3fnD6Idz24grmLNnMzCm5fY6mrV5cspn/e30NF00bwRWfK/a7nHbxNAhEZAbwv0AQeFBV72jw+CXAjambVcA3VXWJlzXlmoqaOH96ey1/nr+eyqh/AaCquJqcojIYkDbNbZxJIsKYoh5NniNQVfZGHLZU1O4fFHtq2bo3wpryKv71cXmbm9eJQGE4SLe8EN3yAhSGQ3TLC1KYF6RPYZhueaHU40G65wcZ2LOAS32az8ELlx1bzPOLN/OzF1dwwriirGmv7LVln1bwg2eXMK24Lz+bOTFn94Y8CwIRCQJ/AKYDZcACEZmjqvXnvVsHnKCqu0XkDOB+4Ggv6ok6CaKOS0EoSDgoWf8ftqcmxkNvr9sXADMOG8x1p4xlwtD0HDd2VUm4yS9XFSehJDT5M4CQnOhFUj+JJMePh4MBqiMOiXodKAMi5AUD5IUChAKZ+7dNpNpJJL8UJfk7BKTpwxoiQu9uYXp3Czd7DH5vJL4vJHZWRckLBSjMC9ItHKIwtYJPruiTt/NDgTb93tv2RjpNCEByzotfnT+Jc+5+m1/OXcmvvzjZ75I8t70ywjceLaV/93zuvfRI8kLZey6qJV7uERwFrFHVtQAi8hQwE9gXBKr673rLvwsM96qY11Zu51tPLAKShwLyQ0EKwgEKwkEKwskPcn44SEG978nHAm1YNvlY3fPq327timJPTYw/pQKgKupwxsRkABw6pH0BkHBhV3U0tVL/bOUeDJBceYcD5IWC5AWTv2d+KEAwIISCyZV6MCD7vtfVr6rEE7ovXGtjCSqjcWqiCXbXxkGT76RAKBAgHEwGRVuPw9cFVN3K3q03H7EC+aEA3fKC9CnMp0d+iPxwkLxQgII0fCB7FYTpVRBmnEejj7K53UB7TRjai6u+MJo/vrmW848YzrEHpa/hXbaJOgmueWwhe2riPPvNYxmQ49foeBkEw4BN9W6X0fzW/teBvzf2gIjMAmYBjBzZvkmexw/uya1nHUokniASd4nEkyuxSDxBxHGJpr5H4gkqamJsr3ss7hJxEkRT3zsyN3rdSqqx0MgPJVdi76/bRVXU4cxJg/nOye0PAICCUJDDR/QmKEIoKIQCgX0r9kAHtkZFhLyQkBcK0HA16bpKLOESdVyiToKaaILKqENVJE40Eqf+u9btYagm+7LU3yOBZGAX5oXoVRiiR16Ibnkh8kKBfXsfnWmLurP43injmLt0C7c8v5S53/1C1jZZ6whV5Zbnl7Fo4x7uvWRqpzhP52UQNPYpbXQ1KiInkQyCzzf2uKreT/KwESUlJe1aFY8p6sGYoh7teWr9OoglXCLx5EouGv8sLKJOYwGz/2PReGL/x+uFTHXMYWe1y0njB/Ltkw5Ky9DBQEAyvqUSCAgFgWBqBRCmflIk3OReRCz1+1dGHKpjDoLQr0c4uVUfChJOrexz4RCe2V+3vCC3nzeJyx96n3v++Qk3TB/nd0lp99D89Ty7sIzvnjK2zbO9ZSsvg6AMGFHv9nBgc8OFRORw4EHgDFXd6WE9HSYiqcMnqZWcaZNgQFLH1JO3B+f+hpRpxPHjijhvylDu/ecazjl8SM5d3NecN1eXc/tLK5hx2GC+e8pYv8tJGy/PbiwAxorIaBHJAy4C5tRfQERGArOBy1R1tYe1GGMy6NazJ9A9P8TNz3eeqS3Xlldx7f9bxLhBPfntlyd36PBqtvEsCFTVAa4F5gErgadVdbmIXCMi16QW+y+gP3CPiCwWkVKv6jHGZM6AHvncfOahLFi/m6cWbGr5CVmuojbOVY+WkhcM8OAVJXTvZK23RTty9tMHJSUlWlpqeWFMtlNVLn7gXZZv3strN5zga7fUjki4ytceXsD8NTv4f984hqNG9/O7pHYRkYWqWtLYY7k78NUYk9VEhF+eP4mo4/LTv61o+QlZ6o6/r+TN1eX8/LyJORsCLbEgMMZ4ZkxRD6496WBe+nALb3y03e9y2uzZhWU88K91XHHsKC4+qn1D13OBBYExxlPXnHAQYwf24NYXllEddfwup9UWbdzNzbOXctzB/fnx2RP8LsdTFgTGGE/lhQL88oJJfLqnlv95JTcGB26pqOXqxxYypE8Bd188NatbmadD5/7tjDFZYVpxP7569Egemr+OZZ9W+F1Os2pjCWY9upDaWIIHLi+hb/fO30DPgsAYkxE3zhhP/x753DT7Q5yE63c5jVJVfvjchyzbXMFdX5niWa+pbGNBYIzJiN7dwtx2zmEs+3QvD/97vd/lNOqef37Ci0s284PTD+HUCYP8LidjLAiMMRlz5qTBnDx+IL/9x2rKdtf4Xc5+Xlmxjd/8YxUzpwzlm11s/mULAmNMxogIP5t5GAD/9dflZMsFrau3VfK9pz5g0rDe/PeFh3e5ZocWBMaYjBret5D/PG0cr3+0nZeWbvG7HHZXx7jqkVIK80Pcf1lJp2yd3RILAmNMxv3H54qZOKwXt81ZQUVN3Lc64gmXbz2xiK17I9x/2ZEM7p2bbTA6yoLAGJNxoWCAOy44nF3VUe54+SPf6vj531bwztqd3HHBJI4Y2de3OvxmQWCM8cXEYb352nGjefL9jSxYvyvj7//Eext49J0NXH38GC6Y6tksuTnBgsAY45vrp49jWJ9u/Gj2UqJOImPv++7anfzkr8s58ZAifjhjfMbeN1tZEBhjfNM9P8QvzpvImu1V/PHNtRl5z027avjm4wsZ2b+Q3198hM19jQWBMcZnJ40fyNmHD+Hu19fwSXmVp+9VHXX4xqOlJFzlT1dMo1eBTTkLFgTGmCzwX+dMoCAc4ObZSz27tsB1lRueXszqbZX84ZKpjB7Q3ZP3yUUWBMYY3w3sWcCPzjyU99bt4pnSMk/e465XVzNv+TZuPWsCXxhb5Ml75CoLAmNMVvhKyQimFffl9rkr2VEVTetr/+3Dzfz+9TV8uWQ4Vx5XnNbX7gwsCIwxWSEQEH51wSRqYg6/SOPUlss+reD7zyzhyFF9+fl5E7tc+4jWsCAwxmSNgwf25JsnHswLizfz5uryDr9eeWWUWY+W0q8wj/suPZL8UNdrH9EaFgTGmKzyrRMPYkxRd259YSm1sfZfWxB1Elzz+EJ21cS4//ISinrmp7HKzsWCwBiTVQrCQX55/iQ27arlrtfaN7WlqnLr88tYuGE3v/3SFCYO653mKjsXCwJjTNY5Zkx/vlIyggf/tY4Vm/e2+fl/nr+eZxaWcd3JB3PW4UM8qLBzsSAwxmSlH505nr6FYX40+0MSbuuvLXhrdTm/eGkFpx82iO+dOs7DCjsPCwJjTFbqU5jHj8+ewJKyCh57Z32rnrO2vIpr/98ixg3qye++PIWAtY9oFQsCY0zWOnfyUI4fV8Sd81axeU9ts8vujcS56tFSQsEAD1xeQvf8UIaqzH0WBMaYrCUi3H7eRBKqzU5tmXCV6578gI07a7jnkqmM6FeY4UpzmwWBMSarjehXyPWnjuPVlduYt3xro8v8+uWP+Oeqcn468zCOGdM/wxXmPgsCY0zW+9rnR3PokF78ZM5y9kb2n9py9qIy/vjWWi4/dhSXHD3KpwpzmwWBMSbrhYMB7rhgEtsro9z58qp993+wcTc3zV7KsWP68+OzJ/hYYW7zNAhEZIaIrBKRNSJyUyOPi4j8PvX4hyIy1ct6jDG5a/KIPlxxbDGPv7eBhRt2s7UiwqzHFjKoVz73XDKVcNC2a9vLs9PqIhIE/gBMB8qABSIyR1Xrd5M6Axib+joauDf13RhjDvD90w9h3vKt3Dx7KfnhADVRh8e/fhx9u+f5XVpO8zJCjwLWqOpaVY0BTwEzGywzE3hUk94F+oiIXQZojGlUj/wQP5s5kVXbKln6aQV3XXQEhwzu6XdZOc/LgbbDgE31bpdx4NZ+Y8sMA7bUX0hEZgGzAEaOHJn2Qo0xuWP6hEF895SxDO1TwPQJg/wup1PwMggau6Sv4SDg1iyDqt4P3A9QUlLizTx2xpiccf10ax2RTl4eGioDRtS7PRzY3I5ljDHGeMjLIFgAjBWR0SKSB1wEzGmwzBzg8tTooWOAClXd0vCFjDHGeMezQ0Oq6ojItcA8IAg8pKrLReSa1OP3AXOBM4E1QA1wpVf1GGOMaZynXZlUdS7JlX39++6r97MC3/ayBmOMMc2zKzCMMaaLsyAwxpguzoLAGGO6OAsCY4zp4qSpiR6ylYiUAxva+fQBwI40luMFq7Hjsr0+yP4as70+yP4as62+Uapa1NgDORcEHSEipapa4ncdzbEaOy7b64PsrzHb64PsrzHb66vPDg0ZY0wXZ0FgjDFdXFcLgvv9LqAVrMaOy/b6IPtrzPb6IPtrzPb69ulS5wiMMcYcqKvtERhjjGnAgsAYY7q4LhMEIjJDRFaJyBoRucnvehoSkREi8oaIrBSR5SLyXb9raoyIBEXkAxH5m9+1NEZE+ojIsyLyUerf8li/a6pPRK5P/f8uE5EnRaQgC2p6SES2i8iyevf1E5FXROTj1Pe+WVjjnan/5w9F5HkR6ZNN9dV77PsioiIywI/aWqNLBIGIBIE/AGcAE4CLRWSCv1UdwAH+U1UPBY4Bvp2FNQJ8F1jpdxHN+F/gZVUdD0wmi2oVkWHAdUCJqk4k2Z79In+rAuBhYEaD+24CXlPVscBrqdt+epgDa3wFmKiqhwOrgR9luqh6HubA+hCREcB0YGOmC2qLLhEEwFHAGlVdq6ox4Clgps817UdVt6jqotTPlSRXYMP8rWp/IjIcOAt40O9aGiMivYDjgT8BqGpMVff4WtSBQkA3EQkBhWTBjHyq+hawq8HdM4FHUj8/ApyXyZoaaqxGVf2Hqjqpm++SnOHQF038GwL8D/BDGpmCN5t0lSAYBmyqd7uMLFvJ1icixcARwHs+l9LQXST/qF2f62jKGKAc+HPq8NWDItLd76LqqOqnwG9Ibh1uITkj3z/8rapJg+pmC0x9H+hzPS35GvB3v4uoT0TOBT5V1SV+19KSrhIE0sh9WZnQItIDeA74nqru9bueOiJyNrBdVRf6XUszQsBU4F5VPQKoxv9DGvukjrPPBEYDQ4HuInKpv1XlPhG5heSh1Sf8rqWOiBQCtwD/5XctrdFVgqAMGFHv9nCyYJe8IREJkwyBJ1R1tt/1NHAccK6IrCd5aO1kEXnc35IOUAaUqWrdntSzJIMhW5wKrFPVclWNA7OBz/lcU1O2icgQgNT37T7X0ygRuQI4G7hEs+uiqINIBv6S1GdmOLBIRAb7WlUTukoQLADGishoEckjeYJujs817UdEhOSx7ZWq+ju/62lIVX+kqsNVtZjkv9/rqppVW7OquhXYJCKHpO46BVjhY0kNbQSOEZHC1P/3KWTRyewG5gBXpH6+Avirj7U0SkRmADcC56pqjd/11KeqS1V1oKoWpz4zZcDU1N9o1ukSQZA6oXQtMI/kB+9pVV3ub1UHOA64jOSW9uLU15l+F5WDvgM8ISIfAlOAX/pbzmdSeyrPAouApSQ/f763IRCRJ4F3gENEpExEvg7cAUwXkY9Jjnq5IwtrvBvoCbyS+rzc1+yLZL6+nGEtJowxpovrEnsExhhjmmZBYIwxXZwFgTHGdHEWBMYY08VZEBhjTBdnQWCMMV2cBYExOUZEThSRZq9IFpEbRGRFqkXzayIyKlP1mdxjQWBMB6S6iGbaibTcmuIDku2uDyd5EduvvS7K5C4LApNTRKQ4NRnJg6nJXZ4QkVNFZH5qEpWjRKR7aqKQBakupDPrPfdfIrIo9fW51P1DROSt1NWpy0TkC6n7q+q97xdF5OHUzw+LyO9E5A3gv0XkIBF5WUQWpl5/fL3l7pXkhENrReSEVF0r614rtdxpIvJOqqZnUo0HEZH1IvLT1P1LRWR8qjPtNcD1qXq/0Ni/k6q+Ua/tgq8tmk3282NrxpiOOhj4EjCLZB+prwKfB84FbibZX+h1Vf1aataq90XkVZKN06arakRExgJPAiWp589T1dtTkxgVtqKGccCpqpoQkdeAa1T1YxE5GrgHODm1XN/Uz+cCL5JsJXIVsEBEppDsQXNr6rWqReRG4AbgZ6nn71DVqSLyLeD7qnpVqpVClar+ppX/Xl8ny1o0m+xiQWBy0TpVXQogIstJzqSlIrIUKCa59XuuiHw/tXwBMJJkx9m7UyvgBMmVOSTD5KFU99cXVHVxK2p4JhUCPUgepnkm2UcOgPx6y71Yr7ZtDequq3UCMD/1/DySPWvq1HWhXQhc0Iq69pNqc10CnNDW55quw4LA5KJovZ/derddkn/TCeBCVV1V/0kichuwjeQUlgEgAsnZpUTkeJKzrz0mIneq6qPsP2dFw7mFq1PfA8AeVZ3SQq3162xY6yuqenELz0/Qxs+riJxKsif+CaoabWl503XZOQLTGc0DvpNq9YyIHJG6vzewRVVdkp1eg6nHR5GcdOcBkq3A6+Yw2CYih4pIADi/sTdKTR60TkS+lHotEZHJbaj1XeA4ETk49fxCERnXwnMqSXbdbFLqd/4jyRbNWTmXgMkeFgSmM/o5EAY+FJFlqduQPHZ/hYi8S/KwUN1W/YnAYhH5ALgQ+N/U/TcBfwNeJzm1ZFMuAb4uIkuA5bRhPmxVLQf+A3gy1Tr7XWB8C097ETi/uZPFwJ1AD5KHrBaLSFbNv2Gyi7WhNsaYLs72CIwxpouzk8XG5DBJTtz+pQZ3P6Oqt/tRj8lNdmjIGGO6ODs0ZIwxXZwFgTHGdHEWBMYY08VZEBhjTBf3/wGsNUlSwDtzswAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=train['measurement_2'], y=train.groupby('measurement_2')['failure'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Failure probability is generally the same for all values of measurement_2 excl. 5, 10, 12, 13. As previously, we can group values of the variable using its quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "measurement_2_grouped\n",
       "(-0.001, 2.0]    0.201050\n",
       "(2.0, 3.0]       0.219149\n",
       "(3.0, 4.0]       0.212815\n",
       "(4.0, 5.0]       0.203949\n",
       "(5.0, 6.0]       0.201585\n",
       "(6.0, 7.0]       0.224758\n",
       "(7.0, 8.0]       0.220606\n",
       "(8.0, 9.0]       0.214400\n",
       "(9.0, 11.0]      0.200408\n",
       "(11.0, 24.0]     0.243109\n",
       "Name: failure, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group values into quantiles\n",
    "train['measurement_2_grouped'] = pd.qcut(train['measurement_2'], 10)\n",
    "test['measurement_2_grouped'] = pd.cut(\n",
    "    test['measurement_2'], \n",
    "    pd.IntervalIndex(train['measurement_2_grouped'].unique()))\n",
    "    \n",
    "# calculate failure probability for each group\n",
    "train.groupby('measurement_2_grouped')['failure'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the highest values of measurement_2 (values between 11 and 24) the product reaches the highest failure probability (0.243). <br>\n",
    "<br>\n",
    "The dtype of other measurements is float. Because of the continuous type of these features, visualisations of failure probability for different feature values would be unclear. For now, density plots and variables statistics will be provided. The results are summarized at the exploration summary part of the notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## measurement_3 <br>\n",
    "Draw a density plot and calculate statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='measurement_3', ylabel='Density'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(train['measurement_3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    26189.000000\n",
       "mean        17.791528\n",
       "std          1.001200\n",
       "min         13.968000\n",
       "25%         17.117000\n",
       "50%         17.787000\n",
       "75%         18.469000\n",
       "max         21.499000\n",
       "Name: measurement_3, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['measurement_3'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## measurement_4 <br>\n",
    "Draw a density plot and calculate statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='measurement_4', ylabel='Density'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(train['measurement_4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    26032.000000\n",
       "mean        11.731988\n",
       "std          0.996085\n",
       "min          8.008000\n",
       "25%         11.051000\n",
       "50%         11.733000\n",
       "75%         12.410000\n",
       "max         16.484000\n",
       "Name: measurement_4, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['measurement_4'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## measurement_5 <br>\n",
    "Draw a density plot and calculate statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='measurement_5', ylabel='Density'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(train['measurement_5'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    25894.000000\n",
       "mean        17.127804\n",
       "std          0.996414\n",
       "min         12.073000\n",
       "25%         16.443000\n",
       "50%         17.132000\n",
       "75%         17.805000\n",
       "max         21.425000\n",
       "Name: measurement_5, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['measurement_5'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## measurement_6 <br>\n",
    "Draw a density plot and calculate statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='measurement_6', ylabel='Density'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(train['measurement_6'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    25774.000000\n",
       "mean        17.510759\n",
       "std          0.995980\n",
       "min         12.715000\n",
       "25%         16.839000\n",
       "50%         17.516000\n",
       "75%         18.178000\n",
       "max         21.543000\n",
       "Name: measurement_6, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['measurement_6'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## measurement_7 <br>\n",
    "Draw a density plot and calculate statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='measurement_7', ylabel='Density'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(train['measurement_7'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    25633.000000\n",
       "mean        11.716624\n",
       "std          1.000836\n",
       "min          7.968000\n",
       "25%         11.045000\n",
       "50%         11.712000\n",
       "75%         12.391000\n",
       "max         15.419000\n",
       "Name: measurement_7, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['measurement_7'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## measurement_8 <br>\n",
    "Draw a density plot and calculate statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='measurement_8', ylabel='Density'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(train['measurement_8'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    25522.000000\n",
       "mean        19.024714\n",
       "std          1.008591\n",
       "min         15.217000\n",
       "25%         18.340250\n",
       "50%         19.021000\n",
       "75%         19.708000\n",
       "max         23.807000\n",
       "Name: measurement_8, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['measurement_8'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## measurement_9 <br>\n",
    "Draw a density plot and calculate statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='measurement_9', ylabel='Density'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(train['measurement_9'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    25343.000000\n",
       "mean        11.430725\n",
       "std          0.999137\n",
       "min          7.537000\n",
       "25%         10.757000\n",
       "50%         11.430000\n",
       "75%         12.102000\n",
       "max         15.412000\n",
       "Name: measurement_9, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['measurement_9'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## measurement_10 <br>\n",
    "Draw a density plot and calculate statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='measurement_10', ylabel='Density'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(train['measurement_10'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    25270.000000\n",
       "mean        16.117711\n",
       "std          1.405978\n",
       "min          9.323000\n",
       "25%         15.209000\n",
       "50%         16.127000\n",
       "75%         17.025000\n",
       "max         22.479000\n",
       "Name: measurement_10, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['measurement_10'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## measurement_11 <br>\n",
    "Draw a density plot and calculate statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='measurement_11', ylabel='Density'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3q8fw5HyDmZKWxMSUBOuoNgEJ9Bf+T4A/B8pE5N9EZE4QMxkTNNvK63DFCMuLMp2O4ggRYfG0dDuDMAEJqECo6quqeg+wCDgNvCIi20XkMyISPeMETcTbUlbH/LxUUpOi98d28bR0ztW3U9PS4XQUE+YCbjISkUzg08C9wDvAD/AWjFeCksyYUdbY1sWhykZWzozO5qVeC/N9/RA23NUMIdA+iKeBrcA44DZVvV1Vf6OqXwDGBzOgMaNl+8lLeBRWzsxyOoqjrpkygXhXjDUzmSEFegbxiKoWq+q/qmoVgIgkAKhqyUA7icjNIlIqIuUi8qCf9XNEZIeIdIrIl/utOy0ih0Rkv4jsGca/yRi/tpbVkZIQy/ypaU5HcVRCrIu5eanWUW2GFGiB+Gc/y3YMtoOIuIAfAbcAxcDdIlLcb7N64K+B7w3wNmtVdcFgRciYQKgqW8tqubYokziXDcZblJ/GofNNdLp7nI5iwtigR4qITBKRxUCSiCwUkUW+xxq8zU2DWQqUq2qFqnYBTwDr+26gqjWquhvovuJ/gTEBOHOpjcqGdlZFefNSr8XT0ulyezh8vtnpKCaMxQ6x/oN4O6bzgO/3Wd4CfG2IfacA5/q8rgSWDSObAi+LiAIPq+pGfxuJyAZgA0B+fv4w3t5Ek61ltQBcH+Ud1L1KCjIA2H26/t2L54zpb9ACoaqPAo+KyB2q+tQw39vfZarDuWP6ClW9ICIT8Q6rPa6qW/xk3AhsBCgpKbE7shu/tpbVkZeeREHmUCe+0SFrfAJF2cnsOlXPfauLnI5jwtSgBUJEPq6qvwIKRORv+69X1e/72a1XJTC1z+s84EKgwVT1gu9rjYj8Hm+T1fsKhDFDcfd42HHyErfOz43K6TUGsrQwkz8evECPR3FFwV31zPAN1VuX7Ps6Hkjx8xjMbmCmiBSKSDxwF/BsIKFEJFlEUnqfAzcBhwPZ15j+DlQ20tLpjvrrH/pbVphBS4eb49XWD2H8G6qJ6WHf138c7hurqltEHgBeAlzAz1X1iIjc51v/kIhMAvYAEwCPiHwR74inLOD3vr/2YoH/UdUXh5vBGIAtJ+oQgeuidHqNgSwt9PZD7DpVz9WTUx1OY8LRUJ3UAIjId/AOdW0HXgTmA1/0NT8NSFU3AZv6LXuoz/NqvE1P/TX7PsOYEdtWXse8vDTSxsU7HSWsTE5LYkpaErtO1fOZFYVOxzFhKNAB4TepajNwK96+hVnA3wUtlTGjpLmjm/3nGlk5w4a3+rOsMINdp+pRtfEd5v0CLRC9M5utA36tqvVBymPMqNpx8hI9Ho366TUGsrQwg0utXZysbXU6iglDgRaI50TkOFACvCYi2YBNBWnC3tayWsbFu96doM68V99+CGP6C3S67weB5UCJqnYDrfS7KtqYcLStrI7l0zOJj7XpNfwpzEoma3wCu09bgTDvF1Antc9VeK+H6LvPY6Ocx5hRc66+jdOX2vjUdQVORwlbIvJuP4Qx/QU6iumXQBGwH+id3UuxAmHC2NayOgC7/mEISwszeP5QFefq25iaYVeam/8V6BlECVCsNtTBRJCtZbXkpiZSlJ089MZRrLcf4u1T9VYgzHsE2jB7GJgUzCDGjKYej/JWeR0rZ2bZ9BpDmJ2TQkZyPNtP1jkdxYSZQM8gsoCjIrIL6OxdqKq3ByWVMSN0sLKR5g63zd4agJgYYfn0THacvISqWkE17wq0QPxDMEMYM9q2lXmn17jeLpALyPKiTJ4/VMXpS20UZlmTnPEKdJjrm8BpIM73fDewL4i5jBmRrWV1XD15AhnJNr1GIJb75qnacfKSw0lMOAmoQIjIXwC/Ax72LZoCPBOkTMaMyOVON/vONtjopWGYnpVMzoQE64cw7xFoJ/X9wAq8k+ihqmXAxGCFMmYkdp68hNujNv/SMIgI1xVlsbPiks3LZN4VaIHo9N1XGgDfxXL2U2TC0rbyOhLjYlhcYNNrDMfyokzqLndRVnPZ6SgmTARaIN4Uka8BSSJyI/Ak8FzwYhlz5baU1bKsMJOEWJfTUSLK8unefojt5dbMZLwCLRAPArXAIeAv8d7j4RvBCmXMlTrf2E5FbavN3noFpmaMY2pGEtuto9r4BDTMVVU9IvIM8Iyq1gY3kjFXbluZ98fTOqivzHXTs3jhcJXdp9oAQ5xBiNc/iEgdcBwoFZFaEflmaOIZMzxby+qYmJLArJzxTkeJSNfNyKS5w82xKrtPtRm6iemLeEcvLVHVTFXNAJYBK0TkS8EOZ8xweHzTa1xv02tcsXf7IWy4q2HoAvFJ4G5VPdW7QFUrgI/71hkTNo5caKahrdv6H0Zg4gTv5IbWD2Fg6AIRp6rv+1PC1w8R52d7Yxzz5okawPofRuq6oix2naqnu8fjdBTjsKEKRNcVrjMm5N4orWXulFSyxic4HSWiXVeUSVtXDwcrm5yOYhw2VIGYLyLNfh4twNxQBDQmEE1t3ew728Ca2Xb2MFLXTu+dl8n6IaLdoAVCVV2qOsHPI0VVrYnJhI1t5XV4FFbPsgIxUunJ8VyVO8H6IUzAF8oZE9bePFHDhMRYFkxNczrKmHBdUSZ7zzTQ0d0z9MZmzLICYSKeqvLmiVpWzswm1mU/0qPhuqJMOt0e3jnb6HQU4yA7mkzEO17dwsXmTmteGkVLCzNwxYj1Q0Q5KxAm4r15wju9xmrroB41KYlxXDMl1fohopwVCBPx3iitYc6kFHImJDodZUy5riiT/ecaaetyOx3FOMQKhIlolzvd7DndYGcPQXBdUSZuj7L7dIPTUYxDglogRORmESkVkXIRedDP+jkiskNEOkXky8PZ1xiAt8rrcHvU+h+CoGRaBnEusXmZoljQCoSIuIAfAbcAxcDdIlLcb7N64K+B713Bvsbw2rGLpCTGsqQgw+koY05SvIuFU9PZYf0QUSuYZxBLgXJVrfDdrvQJYH3fDVS1RlV3A93D3dcYj0d5/Xgtq2dlE2fDW4NieVEmh8830dTe/xA10SCYR9UU4Fyf15W+ZaO6r4hsEJE9IrKnttbuZRRNDp5vou5yJ39y1USno4xZ1xVl4lHYdare6SjGAcEsEP4m5NfR3ldVN6pqiaqWZGdbO3Q0ee3YRWIE1syyAhEsC/LTSIyLsX6IKBXMAlEJTO3zOg+4EIJ9TZR49VgNJdMySE+OdzrKmJUQ62JJQQbby60fIhoFs0DsBmaKSKGIxAN3Ac+GYF8TBc43tnOsqtmal0JgxYwsSi+2UN3U4XQUE2JBKxCq6gYeAF4CjgG/VdUjInKfiNwHICKTRKQS+FvgGyJSKSITBto3WFlN5Hn92EUAKxAhsHa293vce0MmEz1ig/nmqroJ2NRv2UN9nlfjbT4KaF9jer12vIZpmeMoyh7vdJQxb1bOeCanJrL5eC1/tiTf6TgmhGxsoIk4bV1utp+8xJ/MyUHE33gGM5pEhNWzJ7KtvI4ut92GNJpYgTARZ2uZ9xfVDda8FDJrZ2d7pzU5Y8Ndo4kVCBNxXjxcTdq4OJYU2tXTobJiRhZxLuHNUrvWKJpYgTARpdPdw6tHL3JTcY5dPR1CyQmxLCvMZHOpdVRHEzvCTETZVlZHS6ebW+bmOh0l6qyZnc2Ji5epbGhzOooJESsQJqI8f6iKCYmxrCjKcjpK1FnjG+76hjUzRQ0rECZidLk9vHL0IjcWTyI+1n50Q60oO5mpGUlWIKKIHWUmYrx1so6WDjfr5k5yOkpUEhHWzp7IW+V1tHf1OB3HhIAVCBMxNh2sIiUhlutnWvOSU24qnkR7d8+79wE3Y5sVCBMRuns8vHz0IjcU55AQ63I6TtRaNj2D9HFxvHi4yukoJgSsQJiIsP3kJZrau7nlGmteclKcK4Ybi3N47VgNnW5rZhrrrECYiPCHd86TkhDLKrv3tONumZtLS6ebt8rtHhFjnRUIE/Yud7p54XA1t87PJTHOmpectqIoi5TEWF44VO10FBNkViBM2HvxcDXt3T3cscjvxL8mxOJjY7jxqhxePnqR7h6bvG8sswJhwt5TeyspyBzH4mnpTkcxPjdfM4mm9m52Vtid5sYyKxAmrJ2ua2VHxSXuWJRnU3uHkVWzskmOd7HJmpnGNCsQJqz9evdZXDHCx5ZMHXpjEzKJcS7WzpnIy0eqcVsz05hlBcKErU53D0/uqeTGq3LImZDodBzTz4fm5nKptYudFXaPiLHKCoQJWy8erqa+tYt7rrXbXIajtXMmMj4hlucOXHA6igkSKxAmbP339tMUZiXbzK1hKjHOxU3FObxwuMpuRTpGWYEwYWnf2QbeOdvIZ1YUEBNjndPh6rb5k2nucLO1zOZmGousQJiw9PNtp0hJjLVrH8LcihlZpI2L41lrZhqTrECYsHOuvo0XDldz15KpJCfEOh3HDCI+NoZbrsnllaMXbQrwMcgKhAk7j2ytIEbgs9cXOh3FBOC2+bm0dfXw+nG7X/VYYwXChJVLlzv5zZ5zfHjBFHJTk5yOYwKwrDCT7JQEG800BlmBMGHl0e2n6XR7+MvV052OYgLkihE+NDeX10traO7odjqOGUVWIEzYuNzp5tEdZ7ipOIcZE1OcjmOG4fYFk733DD9y0ekoZhRZgTBh44ldZ2lq7+a+1UVORzHDtHBqGlPSknjuoDUzjSVWIExY6HT38NOtFSyfnsnCfJu1NdKICLfNn8y2sjrqW7ucjmNGiRUIExb+8M4FLjZ38vk1dvYQqW6bn4vbo7xg96seM4JaIETkZhEpFZFyEXnQz3oRkf/0rT8oIov6rDstIodEZL+I7AlmTuOsHo/y0JaTXD15Aitn2rQakao4dwJF2ck2mmkMCVqBEBEX8CPgFqAYuFtEivttdgsw0/fYAPyk3/q1qrpAVUuCldM475Wj1VTUtnLf6iK750ME621mevtUPRebO5yOY0ZBMM8glgLlqlqhql3AE8D6ftusBx5Tr51AmojkBjGTCTOqyg83lzMtcxy3XDPJ6ThmhG6dNxlVeP6gNTONBcEsEFOAc31eV/qWBbqNAi+LyF4R2TDQh4jIBhHZIyJ7amttwrBIs7m0hsPnm7l/7QxiXdYlFulmTBxPce4Em5tpjAjmEemvrUCHsc0KVV2EtxnqfhFZ5e9DVHWjqpaoakl2dvaVpzUhp6r84NUy8tKT+MjC/n87mEh12/zJ7D/XyJlLrU5HMSMUzAJRCfS9T2Qe0P/PigG3UdXerzXA7/E2WZkx5M0TtRyobOL+tTOIs7OHMePDCycjAk/trXQ6ihmhYB6Vu4GZIlIoIvHAXcCz/bZ5FvikbzTTtUCTqlaJSLKIpACISDJwE3A4iFlNiKkqP3itjClpSTal9xiTm5rEypnZPLXvPB5P/0YDE0mCViBU1Q08ALwEHAN+q6pHROQ+EbnPt9kmoAIoB34K/JVveQ6wTUQOALuA51X1xWBlNaH3Vvkl3jnbyOfXFBEfa2cPY82di/M439jO9pOXnI5iRiCok+2r6ia8RaDvsof6PFfgfj/7VQDzg5nNOEdV+d7LpeSmJnJniZ09jEU3FueQmhTHE7vPcr1d2xKx7E83E3LPH6pi/7lGvnTjLBJiXU7HMUGQGOfio4vzePFwtV0TEcGsQJiQ6nT38O0XjzNnUor1PYxxn1w+jR5VHn/7rNNRzBWyAmFC6pc7znCuvp2vrbsKV4xdNT2WTctMZu3sifzP22fpdNvtSCORFQgTMo1tXfzX6+WsmpXNqll2zUo0+PR1BdRd7uT3+847HcVcASsQJmT+6/VyWjq6+dq6OU5HMSGycmYW8/NS+eHmcrp7PE7HMcNkBcKExJELTfz39tP82ZJ85kya4HQcEyIiwt/cMJPKhnae3mcXzkUaKxAm6Ho8ytd+f5j0cXF89ebZTscxIbZ29kTm56Xy76+U0drpdjqOGQYrECbofvHWKQ6ca+T/ubWYtHHxTscxISYifPO2q6lu7uC/Xi93Oo4ZBisQJqjKLrbwnZdKubE4h9vnT3Y6jnHI4mnpfHRxHj/bVkFpdYvTcUyArECYoOl09/Cl3+5nfEIs//cjc+1mQFHuwVvmkJoUxxd+vY+Obhv2GgmsQJig+b/PH+Pw+Wa+fcc8slMSnI5jHJY1PoHvf2wBJy5e5h+ePYJ3ph0TzqxAmKD4w/7zPLrjDH+xspAbi3OcjmPCxKpZ2dy/togndp9j45YKp+OYIQR1sj4TnQ6ca+QrvzvI0sIM/u6Dds2Dea//c+NsTl9q419fOE5Gcjx3lkwdeifjCCsQZlSdudTKvY/tITslgZ/cs8im8jbvExMj/H93zqe5vZuvPHUQBT5mRSIs2dFrRk1Ncwef+Nkuuns8/OLTS8gcb/0Oxr/EOBc//WQJ18/I4qtPHeQ3u21Cv3BkBcKMiuqmDu7auJO6y5384tNLmJmT4nQkE+Z6i8Sqmdl89alD/GrnGacjmX6sQJgRq6i9zJ0Pb6empZNffm4pC/PTnY5kIkRinIuHP7GYD8yZyDeeOcwjW63jOpxYgTAj8nbFJe74yXbaOnv41b3LWDwtw+lIJsIkxrl46OOLWTd3Ev/8/DF+8GqZDYENE9ZJba6IqvLI1lP824vHmZYxjl98ZgnTMpOdjmUiVHxsDP9510KS4g7x76+eoK3LzYO3zLGLKx1mBcIMW0tHN1996iCbDlVz89WT+O6d80hJjHM6lolwsa4YvvvReSTFx/Dwlgoud7r5p/XX2I2lHGQFwgzL7tP1fOk3+6lq6uDr667i3pWF9leeGTUxMcK31l9DcnwsD2+p4HxjO/9190L7A8Qh1gdhAtLl9vCdF4/zZw/vIEaE3/7ltfzFqulWHMyoExH+ft1V/POHr2FrWR13/GQ75+rbnI4VlaxAmCGV17Twpz95ix+/cZKPLs5j09+stM5oE3Qfv3Yaj312KdVNHdz+w228cKjK6UhRxwqEGVB7Vw/ff+UE6/5zGxcaO3j4E4v5zkfnMz7BWiZNaKyYkcUfHrievPRxfP7xfXzpN/tpau92OlbUsCPdvI+q8tKRar71x2Ocb2zn9vmT+catVzExJdHpaCYKFWYl8/RfXccPXy/nh5vL2XHyEn+/bg63z59sTZxBJmNpvHFJSYnu2bPH6RgRq6O7h2feOc/Ptp2irOYys3NS+Mf1V3Pt9EynoxkDeCeC/Pozhzh8vpmF+Wl889ZiuzBzhERkr6qW+F1nBSK6uXs87DpVz/OHqnjhcDX1rV0U507gc9cXcvuCycS5rBXShBePR/ndvkq++1IptS2drJiRyb3XT2f1rGxibEjssFmBMO9R39rF1rJa3iyt5Y0TtdS3dpEU5+IDcyZyz7X5LJ+eaafuJuxd7nTz2I7TPLb9DNXNHUzPTub2+ZO5+ZpJzM5JsZ/hAFmBiHI9HuVAZeO7BeFgZSOqkD4ujlWzsrn56kmsmT2RpHiX01GNGbbuHg+bDlXxq51n2HOmAVUoyBzH8qJMlhRksKQgg7z0JCsYA7ACEWXautwcudDM/rON7D/XyPaTdTS0dRMjMH9qGmtmTWT17GzmTkm1q1TNmFLT0sErRy/y2rEadp+up6XDDcCkCYmUFKSzKD+dRdPSKc6dYPcq8XGsQIjIzcAPABfwiKr+W7/14lu/DmgDPq2q+wLZ159oKBBtXW6qmjqo7n00d1DV1E5VYwdVTd7nDW3/OwwwLz2JpYUZrJk9kZUzskhPjncwvTGh4/EopRdb2HO6nl2nG9h7up4LTR0AJMTGMHdKKounpTMvL42CrHEUZCaTHIVDuB0pECLiAk4ANwKVwG7gblU92mebdcAX8BaIZcAPVHVZIPv6E+oCoap41NuE41Glx6P0fjcFEAHB+xd679lt/2Xe7YT27h6a27tp7uimud1NQ1sXFxrbqWxo53xDO5WNbZxveO8v/16pSXHkpiYyOS3p3a+zclJYMDWN7BS7aY8xvaqbOth3toF9ZxrYe7aBI+eb6erxvLs+IzmetKQ4JiTFkep7JCe4SIh1kRjnIiE2hsQ4F4lxMb5l3q/xsTG4YsAVE0NsjBAjQqzL9zVGcPkesTFCTMx7l7liBJcIsTExuFze573LY4SgN40NViCCWS6XAuWqWuEL8QSwHuj7S3498Jh6q9ROEUkTkVygIIB9R80H/30Lbd1uPB7waO8v+94C4PvFr951Pap4PNDjWx5siXEx5KWPY0paEvPy0piSlsTktEQmTUhiUmoikyYkWt+BMQGalJrIurm5rJubC0Cnu4eyi5c5c6mN05daOd/YTlN7N83t3TS0dXH6UittXT10dvfQ4fbQ5fYM8Qmjr29xcYm8p4j0Fpys8Qk8c/+KUf/sYBaIKcC5Pq8r8Z4lDLXNlAD3BUBENgAbfC8vi0jpCDL3ygLqRuF9RsUg/6CwyjmISMkJkZPVco6+SMnqN6c8cMXvN22gFcEsEP7Oi/r/yT3QNoHs612ouhHYOLxogxORPQOdcoUTyzn6IiWr5Rx9kZI1lDmDWSAqgal9XucBFwLcJj6AfY0xxgRRMMd57QZmikihiMQDdwHP9tvmWeCT4nUt0KSqVQHua4wxJoiCdgahqm4ReQB4Ce9Q1Z+r6hERuc+3/iFgE94RTOV4h7l+ZrB9g5XVj1Ftsgoiyzn6IiWr5Rx9kZI1ZDnH1IVyxhhjRo9dSmiMMcYvKxDGGGP8ivoCISI/F5EaETncZ9l3ReS4iBwUkd+LSJqDEXszvS9nn3VfFhEVkSwnsvXL4jeniHxBREpF5IiIfMepfH3y+Pt/XyAiO0Vkv4jsEZGlTmb0ZZoqIptF5Jjve/c3vuUZIvKKiJT5vjp+U4RBsobV8TRQzj7rw+J4GixnyI4nVY3qB7AKWAQc7rPsJiDW9/zbwLfDMadv+VS8nflngKxwzAmsBV4FEnyvJ4ZpzpeBW3zP1wFvhEHOXGCR73kK3iloioHvAA/6lj8YJj+jA2UNq+NpoJy+12FzPA3y/QzZ8RT1ZxCqugWo77fsZVV1+17uxHsdhqP85fT5d+ArDHAhYagNkPPzwL+paqdvm5qQB+tngJwKTPA9TyUMrr1R1Sr1TWCpqi3AMbwzDawHHvVt9ijwYUcC9jFQ1nA7ngb5nkIYHU+D5AzZ8RT1BSIAnwVecDqEPyJyO3BeVQ84nWUIs4CVIvK2iLwpIkucDjSALwLfFZFzwPeAv3c2znuJSAGwEHgbyFHvNUP4vk50MNr79MvaV1gdT31zhvPx1O/7GbLjKfrmth0GEfk64AYedzpLfyIyDvg63tP3cBcLpAPXAkuA34rIdPWdH4eRzwNfUtWnRORjwM+AGxzOBICIjAeeAr6oqs3hfPOb/ln7LA+r46lvTry5wvJ48vN/H7Ljyc4gBiAinwJuBe4Jw19kAEVAIXBARE7jPW3fJyKTHE3lXyXwtHrtAjx4JxwLN58CnvY9fxLvjMSOE5E4vL8gHlfV3nwXfTMf4/vqeLMdDJg17I4nPznD8nga4PsZsuPJCoQf4r1Z0VeB21W1zek8/qjqIVWdqKoFqlqA94dmkapWOxzNn2eADwCIyCy8c22F46yZF4DVvucfAMoczAK8e1OtnwHHVPX7fVY9i7eg4fv6h1Bn62+grOF2PPnLGY7H0yD/988QquPJyV76cHgAvwaqgG68PxSfwzv1xzlgv+/xUDjm7Lf+NOExisnf9zMe+BVwGNgHfCBMc14P7AUO4G3rXRwGOa/H22F6sM/P4zogE3gNbxF7DcgI46xhdTwNlLPfNo4fT4N8P0N2PNlUG8YYY/yyJiZjjDF+WYEwxhjjlxUIY4wxflmBMMYY45cVCGOMMX5ZgTDGGOOXFQhjIpiIrBGR64bYZpWI7BMRt4h8tN+6F0WkUUT+GNykJhJZgTBmlPjmyAm1NcCgBQI4C3wa+B8/674LfGJ0I5mxwgqEiVgiUuC7Ec0jInJYRB4XkRtE5C3fjXSWikiy7+ZAu0XkHRFZ32ffrb6/rPf1/hUuIrkissV306DDIrLSt/xyn8/9qIj8t+/5f4vI90VkM/BtESny/VW+1/f+c/ps9xPfDWAqRGS1L9ex3vfybXeTiOzwZXrSN1EbInJaRP7Rt/yQiMzxzfB5H/AlX96V/r5PqnpaVQ/inbOn/7rXgJaR/2+YschmczWRbgZwJ7AB2A38Od4pCm4HvgYcBV5X1c+K905mu0TkVbyT292oqh0iMhPv1Bslvv1fUtV/EREXMC6ADLOAG1S1R0ReA+5T1TIRWQb8GN+8OXhn4PyAL9tzwArgXmC3iCzAO+XHN3zv1SoiXwX+Fvgn3/51qrpIRP4K+LKq3isiDwGXVfV7V/C9M2ZQViBMpDulqocAROQI8JqqqogcAgrwzsp5u4h82bd9IpCPd2K+H/p+Mffg/SUP3iLzc98sms+o6v4AMjzpKw7j8Tb3PNlnOu6EPts91yfbxX65e7MWA2/59o8HdvTZv3c2z73AnwaQy5gRsQJhIl1nn+eePq89eH++e4A7VLW0704i8g/ARWA+3qbWDvDeaU5EVgEfAn4pIt9V1cd47x3GEvtlaPV9jQEaVXXBEFn75uyf9RVVvXuI/XuwY9eEgPVBmLHuJeALvqmTEZGFvuWpQJWqevB20rp866cBNar6U7xTLS/ybX9RRK4SkRjgI/4+SL03xzklInf63ktEZP4wsu4EVojIDN/+43zTOQ+mBe/9io0ZdVYgzFj3LSAOOCgih32vwds38CkR2Ym3ean3LGANsF9E3gHuAH7gW/4g8EfgdbzThA/kHuBzInIAOIL33tEBUdVavKONfi0iB/EWjDlD7PYc8JHBOqlFZImIVOLtq3nY16TVu24r3psj/YmIVIrIBwPNa8Y+m+7bGGOMX3YGYYwxxi/r6DJmjBCRr+NtRurrSVX9FyfymMhnTUzGGGP8siYmY4wxflmBMMYY45cVCGOMMX5ZgTDGGOPX/w+MlqLcRHxelAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(train['measurement_11'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    25102.000000\n",
       "mean        19.172085\n",
       "std          1.520785\n",
       "min         12.461000\n",
       "25%         18.170000\n",
       "50%         19.211500\n",
       "75%         20.207000\n",
       "max         25.640000\n",
       "Name: measurement_11, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['measurement_11'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## measurement_12 <br>\n",
    "Draw a density plot and calculate statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='measurement_12', ylabel='Density'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(train['measurement_12'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    24969.000000\n",
       "mean        11.702464\n",
       "std          1.488838\n",
       "min          5.167000\n",
       "25%         10.703000\n",
       "50%         11.717000\n",
       "75%         12.709000\n",
       "max         17.663000\n",
       "Name: measurement_12, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['measurement_12'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## measurement_13 <br>\n",
    "Draw a density plot and calculate statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='measurement_13', ylabel='Density'>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(train['measurement_13'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    24796.000000\n",
       "mean        15.652904\n",
       "std          1.155247\n",
       "min         10.890000\n",
       "25%         14.890000\n",
       "50%         15.628500\n",
       "75%         16.374000\n",
       "max         22.713000\n",
       "Name: measurement_13, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['measurement_13'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## measurement_14 <br>\n",
    "Draw a density plot and calculate statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='measurement_14', ylabel='Density'>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(train['measurement_14'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    24696.000000\n",
       "mean        16.048444\n",
       "std          1.491923\n",
       "min          9.140000\n",
       "25%         15.057000\n",
       "50%         16.040000\n",
       "75%         17.082000\n",
       "max         22.303000\n",
       "Name: measurement_14, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['measurement_14'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## measurement_15 <br>\n",
    "Draw a density plot and calculate statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='measurement_15', ylabel='Density'>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(train['measurement_15'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    24561.000000\n",
       "mean        14.995554\n",
       "std          1.549226\n",
       "min          9.104000\n",
       "25%         13.957000\n",
       "50%         14.969000\n",
       "75%         16.018000\n",
       "max         21.626000\n",
       "Name: measurement_15, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['measurement_15'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## measurement_15 <br>\n",
    "Draw a density plot and calculate statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='measurement_15', ylabel='Density'>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(train['measurement_15'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    24561.000000\n",
       "mean        14.995554\n",
       "std          1.549226\n",
       "min          9.104000\n",
       "25%         13.957000\n",
       "50%         14.969000\n",
       "75%         16.018000\n",
       "max         21.626000\n",
       "Name: measurement_15, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['measurement_15'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## measurement_16 <br>\n",
    "Draw a density plot and calculate statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='measurement_16', ylabel='Density'>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(train['measurement_16'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    24460.000000\n",
       "mean        16.460727\n",
       "std          1.708935\n",
       "min          9.701000\n",
       "25%         15.268000\n",
       "50%         16.436000\n",
       "75%         17.628000\n",
       "max         24.094000\n",
       "Name: measurement_16, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['measurement_16'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## measurement_17 <br>\n",
    "Draw a density plot and calculate statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='measurement_17', ylabel='Density'>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(train['measurement_17'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    24286.000000\n",
       "mean       701.269059\n",
       "std        123.304161\n",
       "min        196.787000\n",
       "25%        618.961500\n",
       "50%        701.024500\n",
       "75%        784.090250\n",
       "max       1312.794000\n",
       "Name: measurement_17, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['measurement_17'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# failure <br>\n",
    "Change dtype of the target variable from int to object to make some functions work properly later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['failure'] = train['failure'].astype(object)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw a histogram for the target variable and calculate probability of a failure a priori"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='failure', ylabel='Count'>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(train['failure'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A priori probability of a failure equals 0.2126\n"
     ]
    }
   ],
   "source": [
    "print('A priori probability of a failure equals', \n",
    "      round(train['failure'].sum() / train.shape[0], 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For modeling it's more convinient to have a target variable as a separate dataframe or an array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['product_code', 'loading', 'attribute_0', 'attribute_1', 'attribute_2',\n",
       "       'attribute_3', 'measurement_0', 'measurement_1', 'measurement_2',\n",
       "       'measurement_3', 'measurement_4', 'measurement_5', 'measurement_6',\n",
       "       'measurement_7', 'measurement_8', 'measurement_9', 'measurement_10',\n",
       "       'measurement_11', 'measurement_12', 'measurement_13', 'measurement_14',\n",
       "       'measurement_15', 'measurement_16', 'measurement_17',\n",
       "       'measurement_0_grouped', 'measurement_1_grouped',\n",
       "       'measurement_2_grouped'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# separate the target variable\n",
    "target = train['failure']\n",
    "train.drop('failure', axis=1, inplace=True)\n",
    "\n",
    "# make sure that it was separated\n",
    "train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting the data <br>\n",
    "The test dataframe does not contain labels - we need to predict them. But in order to evaluate performance of models, we need a separate test set. Therefore, the train dataset will be split into 2 separate sets. <br>\n",
    "<br>\n",
    "At the begginning we have created categorical features measurement_0_grouped, measurement_1_grouped, measurement_2_grouped using quantilies calculated on the whole dataset. Now this dataset will be split into training and test sets. This way data leakage problem occurs, when evaluating a model on the test set. But we assume that it has a low impact on models performance evaluation and can be ignored. Moreover, in most cases cross-validation will be used to evaluate quality of a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>product_code</th>\n",
       "      <th>loading</th>\n",
       "      <th>attribute_0</th>\n",
       "      <th>attribute_1</th>\n",
       "      <th>attribute_2</th>\n",
       "      <th>attribute_3</th>\n",
       "      <th>measurement_0</th>\n",
       "      <th>measurement_1</th>\n",
       "      <th>measurement_2</th>\n",
       "      <th>measurement_3</th>\n",
       "      <th>...</th>\n",
       "      <th>measurement_11</th>\n",
       "      <th>measurement_12</th>\n",
       "      <th>measurement_13</th>\n",
       "      <th>measurement_14</th>\n",
       "      <th>measurement_15</th>\n",
       "      <th>measurement_16</th>\n",
       "      <th>measurement_17</th>\n",
       "      <th>measurement_0_grouped</th>\n",
       "      <th>measurement_1_grouped</th>\n",
       "      <th>measurement_2_grouped</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1630</th>\n",
       "      <td>A</td>\n",
       "      <td>107.53</td>\n",
       "      <td>material_7</td>\n",
       "      <td>material_8</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>18.014</td>\n",
       "      <td>14.962</td>\n",
       "      <td>16.743</td>\n",
       "      <td>14.487</td>\n",
       "      <td>14.351</td>\n",
       "      <td>15.737</td>\n",
       "      <td>705.885</td>\n",
       "      <td>(13.0, 29.0]</td>\n",
       "      <td>(-0.001, 4.0]</td>\n",
       "      <td>(3.0, 4.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18030</th>\n",
       "      <td>D</td>\n",
       "      <td>128.99</td>\n",
       "      <td>material_7</td>\n",
       "      <td>material_5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>17.242</td>\n",
       "      <td>...</td>\n",
       "      <td>18.873</td>\n",
       "      <td>11.384</td>\n",
       "      <td>15.002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.237</td>\n",
       "      <td>NaN</td>\n",
       "      <td>841.809</td>\n",
       "      <td>(-0.001, 3.0]</td>\n",
       "      <td>(7.0, 9.0]</td>\n",
       "      <td>(4.0, 5.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26078</th>\n",
       "      <td>E</td>\n",
       "      <td>128.33</td>\n",
       "      <td>material_7</td>\n",
       "      <td>material_6</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>16.094</td>\n",
       "      <td>...</td>\n",
       "      <td>19.696</td>\n",
       "      <td>10.480</td>\n",
       "      <td>15.294</td>\n",
       "      <td>16.715</td>\n",
       "      <td>15.292</td>\n",
       "      <td>15.861</td>\n",
       "      <td>632.835</td>\n",
       "      <td>(3.0, 4.0]</td>\n",
       "      <td>(13.0, 29.0]</td>\n",
       "      <td>(4.0, 5.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19823</th>\n",
       "      <td>D</td>\n",
       "      <td>125.21</td>\n",
       "      <td>material_7</td>\n",
       "      <td>material_5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>16.677</td>\n",
       "      <td>...</td>\n",
       "      <td>21.130</td>\n",
       "      <td>13.625</td>\n",
       "      <td>15.095</td>\n",
       "      <td>15.394</td>\n",
       "      <td>12.846</td>\n",
       "      <td>19.156</td>\n",
       "      <td>643.322</td>\n",
       "      <td>(9.0, 11.0]</td>\n",
       "      <td>(7.0, 9.0]</td>\n",
       "      <td>(5.0, 6.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15788</th>\n",
       "      <td>C</td>\n",
       "      <td>106.12</td>\n",
       "      <td>material_7</td>\n",
       "      <td>material_8</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>16.963</td>\n",
       "      <td>...</td>\n",
       "      <td>17.362</td>\n",
       "      <td>10.523</td>\n",
       "      <td>15.199</td>\n",
       "      <td>17.476</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.472</td>\n",
       "      <td>545.206</td>\n",
       "      <td>(4.0, 5.0]</td>\n",
       "      <td>(4.0, 6.0]</td>\n",
       "      <td>(5.0, 6.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      product_code  loading attribute_0 attribute_1 attribute_2 attribute_3  \\\n",
       "1630             A   107.53  material_7  material_8           9           5   \n",
       "18030            D   128.99  material_7  material_5           6           6   \n",
       "26078            E   128.33  material_7  material_6           6           9   \n",
       "19823            D   125.21  material_7  material_5           6           6   \n",
       "15788            C   106.12  material_7  material_8           5           8   \n",
       "\n",
       "       measurement_0  measurement_1  measurement_2  measurement_3  ...  \\\n",
       "1630              16              2              4            NaN  ...   \n",
       "18030              3              9              5         17.242  ...   \n",
       "26078              4             16              5         16.094  ...   \n",
       "19823             11              9              6         16.677  ...   \n",
       "15788              5              6              6         16.963  ...   \n",
       "\n",
       "       measurement_11  measurement_12  measurement_13  measurement_14  \\\n",
       "1630           18.014          14.962          16.743          14.487   \n",
       "18030          18.873          11.384          15.002             NaN   \n",
       "26078          19.696          10.480          15.294          16.715   \n",
       "19823          21.130          13.625          15.095          15.394   \n",
       "15788          17.362          10.523          15.199          17.476   \n",
       "\n",
       "       measurement_15  measurement_16  measurement_17  measurement_0_grouped  \\\n",
       "1630           14.351          15.737         705.885           (13.0, 29.0]   \n",
       "18030          19.237             NaN         841.809          (-0.001, 3.0]   \n",
       "26078          15.292          15.861         632.835             (3.0, 4.0]   \n",
       "19823          12.846          19.156         643.322            (9.0, 11.0]   \n",
       "15788             NaN          15.472         545.206             (4.0, 5.0]   \n",
       "\n",
       "       measurement_1_grouped  measurement_2_grouped  \n",
       "1630           (-0.001, 4.0]             (3.0, 4.0]  \n",
       "18030             (7.0, 9.0]             (4.0, 5.0]  \n",
       "26078           (13.0, 29.0]             (4.0, 5.0]  \n",
       "19823             (7.0, 9.0]             (5.0, 6.0]  \n",
       "15788             (4.0, 6.0]             (5.0, 6.0]  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(train, target, random_state=0)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Categorical features \n",
    "Since many algorithms don't handle categorical features, we need to present them as one-hot vectors. For now pd.get_dummies() will be used to get more interpretable results (a dataframe with named columns)<br>\n",
    "<br>\n",
    "List of categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['product_code', 'attribute_0', 'attribute_1', 'attribute_2',\n",
       "       'attribute_3', 'measurement_0_grouped', 'measurement_1_grouped',\n",
       "       'measurement_2_grouped'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns[[0, 2, 3, 4, 5, 24, 25, 26]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['loading', 'measurement_0', 'measurement_1', 'measurement_2',\n",
       "       'measurement_3', 'measurement_4', 'measurement_5', 'measurement_6',\n",
       "       'measurement_7', 'measurement_8', 'measurement_9', 'measurement_10',\n",
       "       'measurement_11', 'measurement_12', 'measurement_13', 'measurement_14',\n",
       "       'measurement_15', 'measurement_16', 'measurement_17', 'product_code_A',\n",
       "       'product_code_B', 'product_code_C', 'product_code_D', 'product_code_E',\n",
       "       'attribute_0_material_5', 'attribute_0_material_7',\n",
       "       'attribute_1_material_5', 'attribute_1_material_6',\n",
       "       'attribute_1_material_8', 'attribute_2_5', 'attribute_2_6',\n",
       "       'attribute_2_8', 'attribute_2_9', 'attribute_3_5', 'attribute_3_6',\n",
       "       'attribute_3_8', 'attribute_3_9', 'measurement_0_grouped_(-0.001, 3.0]',\n",
       "       'measurement_0_grouped_(3.0, 4.0]', 'measurement_0_grouped_(4.0, 5.0]',\n",
       "       'measurement_0_grouped_(5.0, 6.0]', 'measurement_0_grouped_(6.0, 7.0]',\n",
       "       'measurement_0_grouped_(7.0, 8.0]', 'measurement_0_grouped_(8.0, 9.0]',\n",
       "       'measurement_0_grouped_(9.0, 11.0]',\n",
       "       'measurement_0_grouped_(11.0, 13.0]',\n",
       "       'measurement_0_grouped_(13.0, 29.0]',\n",
       "       'measurement_1_grouped_(-0.001, 4.0]',\n",
       "       'measurement_1_grouped_(4.0, 6.0]', 'measurement_1_grouped_(6.0, 7.0]',\n",
       "       'measurement_1_grouped_(7.0, 9.0]', 'measurement_1_grouped_(9.0, 10.0]',\n",
       "       'measurement_1_grouped_(10.0, 13.0]',\n",
       "       'measurement_1_grouped_(13.0, 29.0]',\n",
       "       'measurement_2_grouped_(-0.001, 2.0]',\n",
       "       'measurement_2_grouped_(2.0, 3.0]', 'measurement_2_grouped_(3.0, 4.0]',\n",
       "       'measurement_2_grouped_(4.0, 5.0]', 'measurement_2_grouped_(5.0, 6.0]',\n",
       "       'measurement_2_grouped_(6.0, 7.0]', 'measurement_2_grouped_(7.0, 8.0]',\n",
       "       'measurement_2_grouped_(8.0, 9.0]', 'measurement_2_grouped_(9.0, 11.0]',\n",
       "       'measurement_2_grouped_(11.0, 24.0]'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create dummy variables\n",
    "X_train_dummies = pd.get_dummies(X_train)\n",
    "X_test_dummies = pd.get_dummies(X_test)\n",
    "\n",
    "# check if dummies were created\n",
    "X_train_dummies.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Features normalization <br>\n",
    "Since some algorithms (e.g. KNN) use 'distance' between observations, all numerical features should be standardized or normalized. Most features in the dataset follow the Gaussian distribution, so it's better to use normalization. Moreover, this way we minimize the influence of outliers, which are present in case of some features (for instance loading, measurement_17) <br>\n",
    "<br> \n",
    "List of columns to be normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['loading', 'measurement_0', 'measurement_1', 'measurement_2',\n",
       "       'measurement_3', 'measurement_4', 'measurement_5', 'measurement_6',\n",
       "       'measurement_7', 'measurement_8', 'measurement_9', 'measurement_10',\n",
       "       'measurement_11', 'measurement_12', 'measurement_13', 'measurement_14',\n",
       "       'measurement_15', 'measurement_16', 'measurement_17'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_dummies.columns[[*range(0, 19)]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>loading</th>\n",
       "      <th>measurement_0</th>\n",
       "      <th>measurement_1</th>\n",
       "      <th>measurement_2</th>\n",
       "      <th>measurement_3</th>\n",
       "      <th>measurement_4</th>\n",
       "      <th>measurement_5</th>\n",
       "      <th>measurement_6</th>\n",
       "      <th>measurement_7</th>\n",
       "      <th>measurement_8</th>\n",
       "      <th>...</th>\n",
       "      <th>measurement_2_grouped_(-0.001, 2.0]</th>\n",
       "      <th>measurement_2_grouped_(2.0, 3.0]</th>\n",
       "      <th>measurement_2_grouped_(3.0, 4.0]</th>\n",
       "      <th>measurement_2_grouped_(4.0, 5.0]</th>\n",
       "      <th>measurement_2_grouped_(5.0, 6.0]</th>\n",
       "      <th>measurement_2_grouped_(6.0, 7.0]</th>\n",
       "      <th>measurement_2_grouped_(7.0, 8.0]</th>\n",
       "      <th>measurement_2_grouped_(8.0, 9.0]</th>\n",
       "      <th>measurement_2_grouped_(9.0, 11.0]</th>\n",
       "      <th>measurement_2_grouped_(11.0, 24.0]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.513603</td>\n",
       "      <td>2.078416</td>\n",
       "      <td>-1.485328</td>\n",
       "      <td>-0.681222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.015593</td>\n",
       "      <td>-0.319033</td>\n",
       "      <td>-0.014153</td>\n",
       "      <td>-0.067520</td>\n",
       "      <td>0.327396</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.036400</td>\n",
       "      <td>-1.070214</td>\n",
       "      <td>0.179974</td>\n",
       "      <td>-0.379065</td>\n",
       "      <td>-0.549359</td>\n",
       "      <td>-0.731627</td>\n",
       "      <td>1.703556</td>\n",
       "      <td>0.598172</td>\n",
       "      <td>-0.121901</td>\n",
       "      <td>-0.066527</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.019484</td>\n",
       "      <td>-0.828012</td>\n",
       "      <td>1.845276</td>\n",
       "      <td>-0.379065</td>\n",
       "      <td>-1.697273</td>\n",
       "      <td>0.577577</td>\n",
       "      <td>-1.657378</td>\n",
       "      <td>-0.286633</td>\n",
       "      <td>1.452140</td>\n",
       "      <td>0.304632</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.060479</td>\n",
       "      <td>0.867404</td>\n",
       "      <td>0.179974</td>\n",
       "      <td>-0.076908</td>\n",
       "      <td>-1.114317</td>\n",
       "      <td>0.677278</td>\n",
       "      <td>0.361192</td>\n",
       "      <td>-0.752161</td>\n",
       "      <td>-0.738225</td>\n",
       "      <td>0.907394</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.549740</td>\n",
       "      <td>-0.585809</td>\n",
       "      <td>-0.533727</td>\n",
       "      <td>-0.076908</td>\n",
       "      <td>-0.828338</td>\n",
       "      <td>0.043825</td>\n",
       "      <td>-1.348916</td>\n",
       "      <td>0.015005</td>\n",
       "      <td>0.087568</td>\n",
       "      <td>-1.010756</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    loading  measurement_0  measurement_1  measurement_2  measurement_3  \\\n",
       "0 -0.513603       2.078416      -1.485328      -0.681222            NaN   \n",
       "1  0.036400      -1.070214       0.179974      -0.379065      -0.549359   \n",
       "2  0.019484      -0.828012       1.845276      -0.379065      -1.697273   \n",
       "3 -0.060479       0.867404       0.179974      -0.076908      -1.114317   \n",
       "4 -0.549740      -0.585809      -0.533727      -0.076908      -0.828338   \n",
       "\n",
       "   measurement_4  measurement_5  measurement_6  measurement_7  measurement_8  \\\n",
       "0      -0.015593      -0.319033      -0.014153      -0.067520       0.327396   \n",
       "1      -0.731627       1.703556       0.598172      -0.121901      -0.066527   \n",
       "2       0.577577      -1.657378      -0.286633       1.452140       0.304632   \n",
       "3       0.677278       0.361192      -0.752161      -0.738225       0.907394   \n",
       "4       0.043825      -1.348916       0.015005       0.087568      -1.010756   \n",
       "\n",
       "   ...  measurement_2_grouped_(-0.001, 2.0]  measurement_2_grouped_(2.0, 3.0]  \\\n",
       "0  ...                                  0.0                               0.0   \n",
       "1  ...                                  0.0                               0.0   \n",
       "2  ...                                  0.0                               0.0   \n",
       "3  ...                                  0.0                               0.0   \n",
       "4  ...                                  0.0                               0.0   \n",
       "\n",
       "   measurement_2_grouped_(3.0, 4.0]  measurement_2_grouped_(4.0, 5.0]  \\\n",
       "0                               1.0                               0.0   \n",
       "1                               0.0                               1.0   \n",
       "2                               0.0                               1.0   \n",
       "3                               0.0                               0.0   \n",
       "4                               0.0                               0.0   \n",
       "\n",
       "   measurement_2_grouped_(5.0, 6.0]  measurement_2_grouped_(6.0, 7.0]  \\\n",
       "0                               0.0                               0.0   \n",
       "1                               0.0                               0.0   \n",
       "2                               0.0                               0.0   \n",
       "3                               1.0                               0.0   \n",
       "4                               1.0                               0.0   \n",
       "\n",
       "   measurement_2_grouped_(7.0, 8.0]  measurement_2_grouped_(8.0, 9.0]  \\\n",
       "0                               0.0                               0.0   \n",
       "1                               0.0                               0.0   \n",
       "2                               0.0                               0.0   \n",
       "3                               0.0                               0.0   \n",
       "4                               0.0                               0.0   \n",
       "\n",
       "   measurement_2_grouped_(9.0, 11.0]  measurement_2_grouped_(11.0, 24.0]  \n",
       "0                                0.0                                 0.0  \n",
       "1                                0.0                                 0.0  \n",
       "2                                0.0                                 0.0  \n",
       "3                                0.0                                 0.0  \n",
       "4                                0.0                                 0.0  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scale features\n",
    "transformer = ColumnTransformer(\n",
    "    [('scaler', StandardScaler(), [*range(0, 19)])],\n",
    "     remainder='passthrough'\n",
    ")\n",
    "X_train_dummies_scaled = transformer.fit_transform(X_train_dummies)\n",
    "X_test_dummies_scaled = transformer.transform(X_test_dummies)\n",
    "\n",
    "\n",
    "# ColumnTransformer() returns an array\n",
    "# In order to make further analysis more interpretable we need to name the features as before\n",
    "X_train_dummies_scaled = pd.DataFrame(X_train_dummies_scaled, columns=X_train_dummies.columns)\n",
    "X_test_dummies_scaled = pd.DataFrame(X_test_dummies_scaled, columns=X_test_dummies.columns)\n",
    "\n",
    "# check if features names were restored\n",
    "X_train_dummies_scaled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing values <br>\n",
    "Calculate the percentage of missing values for each variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product_code             0.000000\n",
       "loading                  0.007628\n",
       "attribute_0              0.000000\n",
       "attribute_1              0.000000\n",
       "attribute_2              0.000000\n",
       "attribute_3              0.000000\n",
       "measurement_0            0.000000\n",
       "measurement_1            0.000000\n",
       "measurement_2            0.000000\n",
       "measurement_3            0.014101\n",
       "measurement_4            0.020525\n",
       "measurement_5            0.025092\n",
       "measurement_6            0.028604\n",
       "measurement_7            0.035028\n",
       "measurement_8            0.039293\n",
       "measurement_9            0.045516\n",
       "measurement_10           0.049681\n",
       "measurement_11           0.053897\n",
       "measurement_12           0.059166\n",
       "measurement_13           0.066392\n",
       "measurement_14           0.070658\n",
       "measurement_15           0.075676\n",
       "measurement_16           0.080745\n",
       "measurement_17           0.085512\n",
       "measurement_0_grouped    0.000000\n",
       "measurement_1_grouped    0.000000\n",
       "measurement_2_grouped    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.isna().sum(axis=0) / X_train.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The percentage of missing values was calculated on a dataset with categorical features (before one-hot encoding). As we can see, all features of float dtype have missing values. Missing values do not occur in case of categorical features. It means, that filling missing values after encoding categorical variables is safe. <br>\n",
    "To fill missing values KNN will be used. Since some algorithms can handle missing values by themselves, separate datasets will be created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loading</th>\n",
       "      <th>measurement_0</th>\n",
       "      <th>measurement_1</th>\n",
       "      <th>measurement_2</th>\n",
       "      <th>measurement_3</th>\n",
       "      <th>measurement_4</th>\n",
       "      <th>measurement_5</th>\n",
       "      <th>measurement_6</th>\n",
       "      <th>measurement_7</th>\n",
       "      <th>measurement_8</th>\n",
       "      <th>...</th>\n",
       "      <th>measurement_2_grouped_(-0.001, 2.0]</th>\n",
       "      <th>measurement_2_grouped_(2.0, 3.0]</th>\n",
       "      <th>measurement_2_grouped_(3.0, 4.0]</th>\n",
       "      <th>measurement_2_grouped_(4.0, 5.0]</th>\n",
       "      <th>measurement_2_grouped_(5.0, 6.0]</th>\n",
       "      <th>measurement_2_grouped_(6.0, 7.0]</th>\n",
       "      <th>measurement_2_grouped_(7.0, 8.0]</th>\n",
       "      <th>measurement_2_grouped_(8.0, 9.0]</th>\n",
       "      <th>measurement_2_grouped_(9.0, 11.0]</th>\n",
       "      <th>measurement_2_grouped_(11.0, 24.0]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.513603</td>\n",
       "      <td>2.078416</td>\n",
       "      <td>-1.485328</td>\n",
       "      <td>-0.681222</td>\n",
       "      <td>-0.653551</td>\n",
       "      <td>-0.015593</td>\n",
       "      <td>-0.319033</td>\n",
       "      <td>-0.014153</td>\n",
       "      <td>-0.067520</td>\n",
       "      <td>0.327396</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.036400</td>\n",
       "      <td>-1.070214</td>\n",
       "      <td>0.179974</td>\n",
       "      <td>-0.379065</td>\n",
       "      <td>-0.549359</td>\n",
       "      <td>-0.731627</td>\n",
       "      <td>1.703556</td>\n",
       "      <td>0.598172</td>\n",
       "      <td>-0.121901</td>\n",
       "      <td>-0.066527</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.019484</td>\n",
       "      <td>-0.828012</td>\n",
       "      <td>1.845276</td>\n",
       "      <td>-0.379065</td>\n",
       "      <td>-1.697273</td>\n",
       "      <td>0.577577</td>\n",
       "      <td>-1.657378</td>\n",
       "      <td>-0.286633</td>\n",
       "      <td>1.452140</td>\n",
       "      <td>0.304632</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.060479</td>\n",
       "      <td>0.867404</td>\n",
       "      <td>0.179974</td>\n",
       "      <td>-0.076908</td>\n",
       "      <td>-1.114317</td>\n",
       "      <td>0.677278</td>\n",
       "      <td>0.361192</td>\n",
       "      <td>-0.752161</td>\n",
       "      <td>-0.738225</td>\n",
       "      <td>0.907394</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.549740</td>\n",
       "      <td>-0.585809</td>\n",
       "      <td>-0.533727</td>\n",
       "      <td>-0.076908</td>\n",
       "      <td>-0.828338</td>\n",
       "      <td>0.043825</td>\n",
       "      <td>-1.348916</td>\n",
       "      <td>0.015005</td>\n",
       "      <td>0.087568</td>\n",
       "      <td>-1.010756</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    loading  measurement_0  measurement_1  measurement_2  measurement_3  \\\n",
       "0 -0.513603       2.078416      -1.485328      -0.681222      -0.653551   \n",
       "1  0.036400      -1.070214       0.179974      -0.379065      -0.549359   \n",
       "2  0.019484      -0.828012       1.845276      -0.379065      -1.697273   \n",
       "3 -0.060479       0.867404       0.179974      -0.076908      -1.114317   \n",
       "4 -0.549740      -0.585809      -0.533727      -0.076908      -0.828338   \n",
       "\n",
       "   measurement_4  measurement_5  measurement_6  measurement_7  measurement_8  \\\n",
       "0      -0.015593      -0.319033      -0.014153      -0.067520       0.327396   \n",
       "1      -0.731627       1.703556       0.598172      -0.121901      -0.066527   \n",
       "2       0.577577      -1.657378      -0.286633       1.452140       0.304632   \n",
       "3       0.677278       0.361192      -0.752161      -0.738225       0.907394   \n",
       "4       0.043825      -1.348916       0.015005       0.087568      -1.010756   \n",
       "\n",
       "   ...  measurement_2_grouped_(-0.001, 2.0]  measurement_2_grouped_(2.0, 3.0]  \\\n",
       "0  ...                                  0.0                               0.0   \n",
       "1  ...                                  0.0                               0.0   \n",
       "2  ...                                  0.0                               0.0   \n",
       "3  ...                                  0.0                               0.0   \n",
       "4  ...                                  0.0                               0.0   \n",
       "\n",
       "   measurement_2_grouped_(3.0, 4.0]  measurement_2_grouped_(4.0, 5.0]  \\\n",
       "0                               1.0                               0.0   \n",
       "1                               0.0                               1.0   \n",
       "2                               0.0                               1.0   \n",
       "3                               0.0                               0.0   \n",
       "4                               0.0                               0.0   \n",
       "\n",
       "   measurement_2_grouped_(5.0, 6.0]  measurement_2_grouped_(6.0, 7.0]  \\\n",
       "0                               0.0                               0.0   \n",
       "1                               0.0                               0.0   \n",
       "2                               0.0                               0.0   \n",
       "3                               1.0                               0.0   \n",
       "4                               1.0                               0.0   \n",
       "\n",
       "   measurement_2_grouped_(7.0, 8.0]  measurement_2_grouped_(8.0, 9.0]  \\\n",
       "0                               0.0                               0.0   \n",
       "1                               0.0                               0.0   \n",
       "2                               0.0                               0.0   \n",
       "3                               0.0                               0.0   \n",
       "4                               0.0                               0.0   \n",
       "\n",
       "   measurement_2_grouped_(9.0, 11.0]  measurement_2_grouped_(11.0, 24.0]  \n",
       "0                                0.0                                 0.0  \n",
       "1                                0.0                                 0.0  \n",
       "2                                0.0                                 0.0  \n",
       "3                                0.0                                 0.0  \n",
       "4                                0.0                                 0.0  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fill missing values\n",
    "imputer = KNNImputer()\n",
    "X_train_dummies_scaled_knn = imputer.fit_transform(X_train_dummies_scaled)\n",
    "X_test_dummies_scaled_knn = imputer.transform(X_test_dummies_scaled)\n",
    "\n",
    "# restore column names\n",
    "X_train_dummies_scaled_knn = pd.DataFrame(X_train_dummies_scaled_knn, columns=X_train_dummies_scaled.columns)\n",
    "X_test_dummies_scaled_knn = pd.DataFrame(X_test_dummies_scaled_knn, columns=X_test_dummies_scaled.columns)\n",
    "\n",
    "X_train_dummies_scaled_knn.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count the number of missing values to make sure, that they were filled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set missing values count:  0\n",
      "Test set missing values count:  0\n"
     ]
    }
   ],
   "source": [
    "print(\"Train set missing values count: \", X_train_dummies_scaled_knn.isna().sum().sum())\n",
    "print(\"Test set missing values count: \", X_test_dummies_scaled_knn.isna().sum().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploration summary <br>\n",
    "Most features in the dataset are numerical. Before normalization, measurements 0, 1, 2 followed gamma distribution, while measurements 3 - 17 followed Gaussian distribution. Standard deviation of measurements 0-2 was about 4, std of measurements 3-9 was about 1, std of measurements 10-16 was about 1.5. <br>\n",
    "Most features can be considered weak in terms of predicting failure probability. <br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loading</th>\n",
       "      <th>measurement_0</th>\n",
       "      <th>measurement_1</th>\n",
       "      <th>measurement_2</th>\n",
       "      <th>measurement_3</th>\n",
       "      <th>measurement_4</th>\n",
       "      <th>measurement_5</th>\n",
       "      <th>measurement_6</th>\n",
       "      <th>measurement_7</th>\n",
       "      <th>measurement_8</th>\n",
       "      <th>...</th>\n",
       "      <th>measurement_2_grouped_(-0.001, 2.0]</th>\n",
       "      <th>measurement_2_grouped_(2.0, 3.0]</th>\n",
       "      <th>measurement_2_grouped_(3.0, 4.0]</th>\n",
       "      <th>measurement_2_grouped_(4.0, 5.0]</th>\n",
       "      <th>measurement_2_grouped_(5.0, 6.0]</th>\n",
       "      <th>measurement_2_grouped_(6.0, 7.0]</th>\n",
       "      <th>measurement_2_grouped_(7.0, 8.0]</th>\n",
       "      <th>measurement_2_grouped_(8.0, 9.0]</th>\n",
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       "      <th>measurement_2_grouped_(11.0, 24.0]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>19775.000000</td>\n",
       "      <td>19927.000000</td>\n",
       "      <td>19927.000000</td>\n",
       "      <td>19927.000000</td>\n",
       "      <td>19646.000000</td>\n",
       "      <td>19518.000000</td>\n",
       "      <td>19427.000000</td>\n",
       "      <td>19357.000000</td>\n",
       "      <td>19229.000000</td>\n",
       "      <td>19144.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>19927.000000</td>\n",
       "      <td>19927.000000</td>\n",
       "      <td>19927.000000</td>\n",
       "      <td>19927.000000</td>\n",
       "      <td>19927.000000</td>\n",
       "      <td>19927.000000</td>\n",
       "      <td>19927.000000</td>\n",
       "      <td>19927.000000</td>\n",
       "      <td>19927.000000</td>\n",
       "      <td>19927.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>127.569762</td>\n",
       "      <td>7.418678</td>\n",
       "      <td>8.243489</td>\n",
       "      <td>6.254529</td>\n",
       "      <td>17.791400</td>\n",
       "      <td>11.729484</td>\n",
       "      <td>17.131521</td>\n",
       "      <td>17.504076</td>\n",
       "      <td>11.721046</td>\n",
       "      <td>19.030216</td>\n",
       "      <td>...</td>\n",
       "      <td>0.122597</td>\n",
       "      <td>0.088021</td>\n",
       "      <td>0.113565</td>\n",
       "      <td>0.121644</td>\n",
       "      <td>0.117981</td>\n",
       "      <td>0.107894</td>\n",
       "      <td>0.097707</td>\n",
       "      <td>0.070608</td>\n",
       "      <td>0.091534</td>\n",
       "      <td>0.068450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>39.019003</td>\n",
       "      <td>4.128883</td>\n",
       "      <td>4.203547</td>\n",
       "      <td>3.309619</td>\n",
       "      <td>1.000101</td>\n",
       "      <td>0.992995</td>\n",
       "      <td>0.995285</td>\n",
       "      <td>0.994595</td>\n",
       "      <td>0.993011</td>\n",
       "      <td>1.010375</td>\n",
       "      <td>...</td>\n",
       "      <td>0.327983</td>\n",
       "      <td>0.283333</td>\n",
       "      <td>0.317290</td>\n",
       "      <td>0.326882</td>\n",
       "      <td>0.322593</td>\n",
       "      <td>0.310254</td>\n",
       "      <td>0.296925</td>\n",
       "      <td>0.256175</td>\n",
       "      <td>0.288374</td>\n",
       "      <td>0.252523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>33.160000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.166000</td>\n",
       "      <td>8.008000</td>\n",
       "      <td>12.813000</td>\n",
       "      <td>13.968000</td>\n",
       "      <td>7.973000</td>\n",
       "      <td>15.217000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>99.830000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>17.118000</td>\n",
       "      <td>11.055250</td>\n",
       "      <td>16.454500</td>\n",
       "      <td>16.831000</td>\n",
       "      <td>11.053000</td>\n",
       "      <td>18.344000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>122.120000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>17.787000</td>\n",
       "      <td>11.727000</td>\n",
       "      <td>17.138000</td>\n",
       "      <td>17.508000</td>\n",
       "      <td>11.717000</td>\n",
       "      <td>19.028000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>148.990000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>18.468000</td>\n",
       "      <td>12.407750</td>\n",
       "      <td>17.804000</td>\n",
       "      <td>18.180000</td>\n",
       "      <td>12.388000</td>\n",
       "      <td>19.711000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>385.860000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>21.499000</td>\n",
       "      <td>16.484000</td>\n",
       "      <td>21.425000</td>\n",
       "      <td>21.543000</td>\n",
       "      <td>15.359000</td>\n",
       "      <td>23.807000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            loading  measurement_0  measurement_1  measurement_2  \\\n",
       "count  19775.000000   19927.000000   19927.000000   19927.000000   \n",
       "mean     127.569762       7.418678       8.243489       6.254529   \n",
       "std       39.019003       4.128883       4.203547       3.309619   \n",
       "min       33.160000       0.000000       0.000000       0.000000   \n",
       "25%       99.830000       4.000000       5.000000       4.000000   \n",
       "50%      122.120000       7.000000       8.000000       6.000000   \n",
       "75%      148.990000      10.000000      11.000000       8.000000   \n",
       "max      385.860000      29.000000      28.000000      24.000000   \n",
       "\n",
       "       measurement_3  measurement_4  measurement_5  measurement_6  \\\n",
       "count   19646.000000   19518.000000   19427.000000   19357.000000   \n",
       "mean       17.791400      11.729484      17.131521      17.504076   \n",
       "std         1.000101       0.992995       0.995285       0.994595   \n",
       "min        14.166000       8.008000      12.813000      13.968000   \n",
       "25%        17.118000      11.055250      16.454500      16.831000   \n",
       "50%        17.787000      11.727000      17.138000      17.508000   \n",
       "75%        18.468000      12.407750      17.804000      18.180000   \n",
       "max        21.499000      16.484000      21.425000      21.543000   \n",
       "\n",
       "       measurement_7  measurement_8  ...  measurement_2_grouped_(-0.001, 2.0]  \\\n",
       "count   19229.000000   19144.000000  ...                         19927.000000   \n",
       "mean       11.721046      19.030216  ...                             0.122597   \n",
       "std         0.993011       1.010375  ...                             0.327983   \n",
       "min         7.973000      15.217000  ...                             0.000000   \n",
       "25%        11.053000      18.344000  ...                             0.000000   \n",
       "50%        11.717000      19.028000  ...                             0.000000   \n",
       "75%        12.388000      19.711000  ...                             0.000000   \n",
       "max        15.359000      23.807000  ...                             1.000000   \n",
       "\n",
       "       measurement_2_grouped_(2.0, 3.0]  measurement_2_grouped_(3.0, 4.0]  \\\n",
       "count                      19927.000000                      19927.000000   \n",
       "mean                           0.088021                          0.113565   \n",
       "std                            0.283333                          0.317290   \n",
       "min                            0.000000                          0.000000   \n",
       "25%                            0.000000                          0.000000   \n",
       "50%                            0.000000                          0.000000   \n",
       "75%                            0.000000                          0.000000   \n",
       "max                            1.000000                          1.000000   \n",
       "\n",
       "       measurement_2_grouped_(4.0, 5.0]  measurement_2_grouped_(5.0, 6.0]  \\\n",
       "count                      19927.000000                      19927.000000   \n",
       "mean                           0.121644                          0.117981   \n",
       "std                            0.326882                          0.322593   \n",
       "min                            0.000000                          0.000000   \n",
       "25%                            0.000000                          0.000000   \n",
       "50%                            0.000000                          0.000000   \n",
       "75%                            0.000000                          0.000000   \n",
       "max                            1.000000                          1.000000   \n",
       "\n",
       "       measurement_2_grouped_(6.0, 7.0]  measurement_2_grouped_(7.0, 8.0]  \\\n",
       "count                      19927.000000                      19927.000000   \n",
       "mean                           0.107894                          0.097707   \n",
       "std                            0.310254                          0.296925   \n",
       "min                            0.000000                          0.000000   \n",
       "25%                            0.000000                          0.000000   \n",
       "50%                            0.000000                          0.000000   \n",
       "75%                            0.000000                          0.000000   \n",
       "max                            1.000000                          1.000000   \n",
       "\n",
       "       measurement_2_grouped_(8.0, 9.0]  measurement_2_grouped_(9.0, 11.0]  \\\n",
       "count                      19927.000000                       19927.000000   \n",
       "mean                           0.070608                           0.091534   \n",
       "std                            0.256175                           0.288374   \n",
       "min                            0.000000                           0.000000   \n",
       "25%                            0.000000                           0.000000   \n",
       "50%                            0.000000                           0.000000   \n",
       "75%                            0.000000                           0.000000   \n",
       "max                            1.000000                           1.000000   \n",
       "\n",
       "       measurement_2_grouped_(11.0, 24.0]  \n",
       "count                        19927.000000  \n",
       "mean                             0.068450  \n",
       "std                              0.252523  \n",
       "min                              0.000000  \n",
       "25%                              0.000000  \n",
       "50%                              0.000000  \n",
       "75%                              0.000000  \n",
       "max                              1.000000  \n",
       "\n",
       "[8 rows x 64 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_dummies.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pipeline <br>\n",
    "To make models building easier, preprocessing steps may be merged into a pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# does not contain one-hot encoding step\n",
    "preprocessing = Pipeline([\n",
    "    ('transformer', ColumnTransformer([\n",
    "        ('scaler', StandardScaler(), [*range(0, 19)])],\n",
    "         remainder='passthrough')),\n",
    "    ('imputer', KNNImputer())\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.to_csv('data/X_train.csv')\n",
    "X_test.to_csv('data/X_test.csv')\n",
    "\n",
    "X_train_dummies.to_csv('data/X_train_dummies.csv')\n",
    "X_test_dummies.to_csv('data/X_test_dummies.csv')\n",
    "\n",
    "X_train_dummies_scaled_knn.to_csv('data/X_train_dummies_scaled_knn.csv')\n",
    "X_test_dummies_scaled_knn.to_csv('data/X_test_dummies_scaled_knn.csv')\n",
    "\n",
    "y_train.to_csv('data/y_train.csv')\n",
    "y_test.to_csv('data/y_test.csv')"
   ]
  }
 ],
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