diff --git "a/ciment.ipynb" "b/ciment.ipynb"
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+++ "b/ciment.ipynb"
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n",
+    "import matplotlib.pyplot as plt \n",
+    "import numpy as np \n",
+    "import pycaret as py\n",
+    "import seaborn as sns "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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>cement</th>\n",
+       "      <th>slag</th>\n",
+       "      <th>ash</th>\n",
+       "      <th>water</th>\n",
+       "      <th>superplastic</th>\n",
+       "      <th>coarseagg</th>\n",
+       "      <th>fineagg</th>\n",
+       "      <th>age</th>\n",
+       "      <th>strength</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>540.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>162.0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>1040.0</td>\n",
+       "      <td>676.0</td>\n",
+       "      <td>28</td>\n",
+       "      <td>79.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>540.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>162.0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>1055.0</td>\n",
+       "      <td>676.0</td>\n",
+       "      <td>28</td>\n",
+       "      <td>61.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>332.5</td>\n",
+       "      <td>142.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>228.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>932.0</td>\n",
+       "      <td>594.0</td>\n",
+       "      <td>270</td>\n",
+       "      <td>40.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>332.5</td>\n",
+       "      <td>142.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>228.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>932.0</td>\n",
+       "      <td>594.0</td>\n",
+       "      <td>365</td>\n",
+       "      <td>41.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>198.6</td>\n",
+       "      <td>132.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>192.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>978.4</td>\n",
+       "      <td>825.5</td>\n",
+       "      <td>360</td>\n",
+       "      <td>44.30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   cement   slag  ash  water  superplastic  coarseagg  fineagg  age  strength\n",
+       "0   540.0    0.0  0.0  162.0           2.5     1040.0    676.0   28     79.99\n",
+       "1   540.0    0.0  0.0  162.0           2.5     1055.0    676.0   28     61.89\n",
+       "2   332.5  142.5  0.0  228.0           0.0      932.0    594.0  270     40.27\n",
+       "3   332.5  142.5  0.0  228.0           0.0      932.0    594.0  365     41.05\n",
+       "4   198.6  132.4  0.0  192.0           0.0      978.4    825.5  360     44.30"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"concrete.csv\")\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>cement</th>\n",
+       "      <th>slag</th>\n",
+       "      <th>ash</th>\n",
+       "      <th>water</th>\n",
+       "      <th>superplastic</th>\n",
+       "      <th>coarseagg</th>\n",
+       "      <th>fineagg</th>\n",
+       "      <th>age</th>\n",
+       "      <th>strength</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1025</th>\n",
+       "      <td>276.4</td>\n",
+       "      <td>116.0</td>\n",
+       "      <td>90.3</td>\n",
+       "      <td>179.6</td>\n",
+       "      <td>8.9</td>\n",
+       "      <td>870.1</td>\n",
+       "      <td>768.3</td>\n",
+       "      <td>28</td>\n",
+       "      <td>44.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1026</th>\n",
+       "      <td>322.2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>115.6</td>\n",
+       "      <td>196.0</td>\n",
+       "      <td>10.4</td>\n",
+       "      <td>817.9</td>\n",
+       "      <td>813.4</td>\n",
+       "      <td>28</td>\n",
+       "      <td>31.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1027</th>\n",
+       "      <td>148.5</td>\n",
+       "      <td>139.4</td>\n",
+       "      <td>108.6</td>\n",
+       "      <td>192.7</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>892.4</td>\n",
+       "      <td>780.0</td>\n",
+       "      <td>28</td>\n",
+       "      <td>23.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1028</th>\n",
+       "      <td>159.1</td>\n",
+       "      <td>186.7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>175.6</td>\n",
+       "      <td>11.3</td>\n",
+       "      <td>989.6</td>\n",
+       "      <td>788.9</td>\n",
+       "      <td>28</td>\n",
+       "      <td>32.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1029</th>\n",
+       "      <td>260.9</td>\n",
+       "      <td>100.5</td>\n",
+       "      <td>78.3</td>\n",
+       "      <td>200.6</td>\n",
+       "      <td>8.6</td>\n",
+       "      <td>864.5</td>\n",
+       "      <td>761.5</td>\n",
+       "      <td>28</td>\n",
+       "      <td>32.40</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      cement   slag    ash  water  superplastic  coarseagg  fineagg  age  \\\n",
+       "1025   276.4  116.0   90.3  179.6           8.9      870.1    768.3   28   \n",
+       "1026   322.2    0.0  115.6  196.0          10.4      817.9    813.4   28   \n",
+       "1027   148.5  139.4  108.6  192.7           6.1      892.4    780.0   28   \n",
+       "1028   159.1  186.7    0.0  175.6          11.3      989.6    788.9   28   \n",
+       "1029   260.9  100.5   78.3  200.6           8.6      864.5    761.5   28   \n",
+       "\n",
+       "      strength  \n",
+       "1025     44.28  \n",
+       "1026     31.18  \n",
+       "1027     23.70  \n",
+       "1028     32.77  \n",
+       "1029     32.40  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  0. ,  96.7,  94.6, 100.5, 125.4, 121.6, 118.3, 125.2, 163.3,\n",
+       "       163.8,  24.5, 118.2,  94.1,  95.7, 100.4, 124.8, 167. , 122. ,\n",
+       "       124.3,  96.2,  97.4,  95.6,  99.9,  98.8, 125.1, 121.4, 117.5,\n",
+       "       174.7, 174.2, 123.8, 121.9, 124.1,  59. ,  60. , 136. , 158. ,\n",
+       "       163. , 119. ,  95. , 120. , 143.6, 132.1, 118.6, 128.6, 172.4,\n",
+       "       173.5, 159.9,  79. ,  94. ,  97. , 123. , 132. , 141. ,  82. ,\n",
+       "       148. , 112. ,  89. , 139. , 111. , 175. , 116. , 100. , 184. ,\n",
+       "       130. , 107. ,  76. , 150. , 146. , 126. , 142. , 161. , 200. ,\n",
+       "       113. , 128. ,  86. ,  90. , 109. ,  78. ,  71. , 103. , 137. ,\n",
+       "       138. , 195. , 185. ,  92. , 143. , 134. , 133. , 166. ,  87. ,\n",
+       "       106. , 182. ,  91. , 179. ,  77. , 172. ,  98. , 164. , 152. ,\n",
+       "       194. , 193. , 190. , 187. ,  71.5, 106.7, 111.9, 103.3, 119.8,\n",
+       "       136.6, 137.9,  86.1, 194.9, 185.3,  89.6,  91.7, 142.7,  78.4,\n",
+       "       133.6, 132.6, 142.8, 165.7,  87.5, 113.2, 106.2, 182.1, 107.5,\n",
+       "       143.2, 178.9, 125.8,  81.8, 148.1, 112.3,  89.3, 138.7, 111.2,\n",
+       "       174.9,  99.6, 183.9, 129.7, 106.9,  75.6, 150.4, 146.4, 126.5,\n",
+       "        93.9, 141.6, 160.9, 200.1, 112.6, 127.9, 127.7,  86.5,  90.3,\n",
+       "       115.6, 108.6,  78.3])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.ash.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2.5,  0. , 10.1,  8.6, 16.5, 18.6, 23.4,  8.9, 32.2, 12.1, 28.2,\n",
+       "       14.3, 11.4, 11.6, 10.3, 15.9, 11.2, 11.1, 22. ,  9.5,  4.5,  4.6,\n",
+       "        7.5,  7.8,  5.7,  9.9,  6.9,  6.7,  6.1,  7. ,  5.5,  8.7, 10.4,\n",
+       "        5.8,  6.4,  7.6,  8.2, 10.8,  9.4, 11.8, 12.4, 12.8, 14.2, 12. ,\n",
+       "       10.2, 11.7, 11.9,  9.6, 11.3,  4. ,  1.9,  5.9,  5.2, 20. ,  3. ,\n",
+       "       20.8, 17.9, 15.3,  8.3,  9. ,  8.1,  3.6,  4.1,  6.5,  7.9,  9.7,\n",
+       "        5.3, 13.9, 13.1, 12.3,  3.9,  8.5,  1.7, 10.9, 19. , 10. , 18. ,\n",
+       "        6. , 15. , 11. ,  5. ,  8. , 13. , 16. ,  2. ,  9.1,  3.4, 10.5,\n",
+       "        3.1,  7.4, 12.6, 16.1, 15.6, 10.6,  7.1,  8.4,  7.2,  9.2,  2.2,\n",
+       "       18.8,  8.8, 18.3, 22.1,  6.2,  6.3,  9.8,  6.6, 10.7, 11.5, 12.2,\n",
+       "       12.7])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.superplastic.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['cement', 'slag', 'ash', 'water', 'superplastic', 'coarseagg',\n",
+       "       'fineagg', 'age', 'strength'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lineplot(data.strength.unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "cement          0\n",
+       "slag            0\n",
+       "ash             0\n",
+       "water           0\n",
+       "superplastic    0\n",
+       "coarseagg       0\n",
+       "fineagg         0\n",
+       "age             0\n",
+       "strength        0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pycaret.regression import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_848e9_row8_col1 {\n",
+       "  background-color: lightgreen;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_848e9\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_848e9_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
+       "      <th id=\"T_848e9_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_848e9_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_848e9_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
+       "      <td id=\"T_848e9_row0_col1\" class=\"data row0 col1\" >123</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_848e9_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "      <td id=\"T_848e9_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
+       "      <td id=\"T_848e9_row1_col1\" class=\"data row1 col1\" >strength</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_848e9_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+       "      <td id=\"T_848e9_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
+       "      <td id=\"T_848e9_row2_col1\" class=\"data row2 col1\" >Regression</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_848e9_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+       "      <td id=\"T_848e9_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
+       "      <td id=\"T_848e9_row3_col1\" class=\"data row3 col1\" >(1030, 9)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_848e9_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "      <td id=\"T_848e9_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
+       "      <td id=\"T_848e9_row4_col1\" class=\"data row4 col1\" >(1030, 9)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_848e9_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+       "      <td id=\"T_848e9_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
+       "      <td id=\"T_848e9_row5_col1\" class=\"data row5 col1\" >(824, 9)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_848e9_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+       "      <td id=\"T_848e9_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
+       "      <td id=\"T_848e9_row6_col1\" class=\"data row6 col1\" >(206, 9)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_848e9_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+       "      <td id=\"T_848e9_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
+       "      <td id=\"T_848e9_row7_col1\" class=\"data row7 col1\" >8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_848e9_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+       "      <td id=\"T_848e9_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
+       "      <td id=\"T_848e9_row8_col1\" class=\"data row8 col1\" >True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_848e9_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+       "      <td id=\"T_848e9_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
+       "      <td id=\"T_848e9_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_848e9_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
+       "      <td id=\"T_848e9_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
+       "      <td id=\"T_848e9_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_848e9_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
+       "      <td id=\"T_848e9_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
+       "      <td id=\"T_848e9_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_848e9_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
+       "      <td id=\"T_848e9_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
+       "      <td id=\"T_848e9_row12_col1\" class=\"data row12 col1\" >KFold</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_848e9_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
+       "      <td id=\"T_848e9_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
+       "      <td id=\"T_848e9_row13_col1\" class=\"data row13 col1\" >10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_848e9_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
+       "      <td id=\"T_848e9_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
+       "      <td id=\"T_848e9_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_848e9_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
+       "      <td id=\"T_848e9_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
+       "      <td id=\"T_848e9_row15_col1\" class=\"data row15 col1\" >False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_848e9_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
+       "      <td id=\"T_848e9_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
+       "      <td id=\"T_848e9_row16_col1\" class=\"data row16 col1\" >False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_848e9_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
+       "      <td id=\"T_848e9_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
+       "      <td id=\"T_848e9_row17_col1\" class=\"data row17 col1\" >reg-default-name</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_848e9_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
+       "      <td id=\"T_848e9_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
+       "      <td id=\"T_848e9_row18_col1\" class=\"data row18 col1\" >9ad7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x2bbe3915a50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "s= setup(data=data, train_size=0.8, session_id=123, target='strength')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_532a9 th {\n",
+       "  text-align: left;\n",
+       "}\n",
+       "#T_532a9_row0_col0, #T_532a9_row1_col0, #T_532a9_row1_col1, #T_532a9_row1_col2, #T_532a9_row1_col3, #T_532a9_row1_col4, #T_532a9_row1_col5, #T_532a9_row1_col6, #T_532a9_row2_col0, #T_532a9_row2_col1, #T_532a9_row2_col2, #T_532a9_row2_col3, #T_532a9_row2_col4, #T_532a9_row2_col5, #T_532a9_row2_col6, #T_532a9_row3_col0, #T_532a9_row3_col1, #T_532a9_row3_col2, #T_532a9_row3_col3, #T_532a9_row3_col4, #T_532a9_row3_col5, #T_532a9_row3_col6, #T_532a9_row4_col0, #T_532a9_row4_col1, #T_532a9_row4_col2, #T_532a9_row4_col3, #T_532a9_row4_col4, #T_532a9_row4_col5, #T_532a9_row4_col6, #T_532a9_row5_col0, #T_532a9_row5_col1, #T_532a9_row5_col2, #T_532a9_row5_col3, #T_532a9_row5_col4, #T_532a9_row5_col5, #T_532a9_row5_col6, #T_532a9_row6_col0, #T_532a9_row6_col1, #T_532a9_row6_col2, #T_532a9_row6_col3, #T_532a9_row6_col4, #T_532a9_row6_col5, #T_532a9_row6_col6, #T_532a9_row7_col0, #T_532a9_row7_col1, #T_532a9_row7_col2, #T_532a9_row7_col3, #T_532a9_row7_col4, #T_532a9_row7_col5, #T_532a9_row7_col6, #T_532a9_row8_col0, #T_532a9_row8_col1, #T_532a9_row8_col2, #T_532a9_row8_col3, #T_532a9_row8_col4, #T_532a9_row8_col5, #T_532a9_row8_col6, #T_532a9_row9_col0, #T_532a9_row9_col1, #T_532a9_row9_col2, #T_532a9_row9_col3, #T_532a9_row9_col4, #T_532a9_row9_col5, #T_532a9_row9_col6, #T_532a9_row10_col0, #T_532a9_row10_col1, #T_532a9_row10_col2, #T_532a9_row10_col3, #T_532a9_row10_col4, #T_532a9_row10_col5, #T_532a9_row10_col6, #T_532a9_row11_col0, #T_532a9_row11_col1, #T_532a9_row11_col2, #T_532a9_row11_col3, #T_532a9_row11_col4, #T_532a9_row11_col5, #T_532a9_row11_col6, #T_532a9_row12_col0, #T_532a9_row12_col1, #T_532a9_row12_col2, #T_532a9_row12_col3, #T_532a9_row12_col4, #T_532a9_row12_col5, #T_532a9_row12_col6, #T_532a9_row13_col0, #T_532a9_row13_col1, #T_532a9_row13_col2, #T_532a9_row13_col3, #T_532a9_row13_col4, #T_532a9_row13_col5, #T_532a9_row13_col6, #T_532a9_row14_col0, #T_532a9_row14_col1, #T_532a9_row14_col2, #T_532a9_row14_col3, #T_532a9_row14_col4, #T_532a9_row14_col5, #T_532a9_row14_col6, #T_532a9_row15_col0, #T_532a9_row15_col1, #T_532a9_row15_col2, #T_532a9_row15_col3, #T_532a9_row15_col4, #T_532a9_row15_col5, #T_532a9_row15_col6, #T_532a9_row16_col0, #T_532a9_row16_col1, #T_532a9_row16_col2, #T_532a9_row16_col3, #T_532a9_row16_col4, #T_532a9_row16_col5, #T_532a9_row16_col6, #T_532a9_row17_col0, #T_532a9_row17_col1, #T_532a9_row17_col2, #T_532a9_row17_col3, #T_532a9_row17_col4, #T_532a9_row17_col5, #T_532a9_row17_col6, #T_532a9_row18_col0, #T_532a9_row18_col1, #T_532a9_row18_col2, #T_532a9_row18_col3, #T_532a9_row18_col4, #T_532a9_row18_col5, #T_532a9_row18_col6 {\n",
+       "  text-align: left;\n",
+       "}\n",
+       "#T_532a9_row0_col1, #T_532a9_row0_col2, #T_532a9_row0_col3, #T_532a9_row0_col4, #T_532a9_row0_col5, #T_532a9_row0_col6 {\n",
+       "  text-align: left;\n",
+       "  background-color: yellow;\n",
+       "}\n",
+       "#T_532a9_row0_col7, #T_532a9_row1_col7, #T_532a9_row2_col7, #T_532a9_row3_col7, #T_532a9_row4_col7, #T_532a9_row5_col7, #T_532a9_row6_col7, #T_532a9_row7_col7, #T_532a9_row8_col7, #T_532a9_row9_col7, #T_532a9_row10_col7, #T_532a9_row11_col7, #T_532a9_row12_col7, #T_532a9_row13_col7, #T_532a9_row14_col7, #T_532a9_row15_col7, #T_532a9_row16_col7 {\n",
+       "  text-align: left;\n",
+       "  background-color: lightgrey;\n",
+       "}\n",
+       "#T_532a9_row17_col7, #T_532a9_row18_col7 {\n",
+       "  text-align: left;\n",
+       "  background-color: yellow;\n",
+       "  background-color: lightgrey;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_532a9\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_532a9_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
+       "      <th id=\"T_532a9_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
+       "      <th id=\"T_532a9_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
+       "      <th id=\"T_532a9_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
+       "      <th id=\"T_532a9_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
+       "      <th id=\"T_532a9_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
+       "      <th id=\"T_532a9_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
+       "      <th id=\"T_532a9_level0_col7\" class=\"col_heading level0 col7\" >TT (Sec)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_532a9_level0_row0\" class=\"row_heading level0 row0\" >catboost</th>\n",
+       "      <td id=\"T_532a9_row0_col0\" class=\"data row0 col0\" >CatBoost Regressor</td>\n",
+       "      <td id=\"T_532a9_row0_col1\" class=\"data row0 col1\" >2.6597</td>\n",
+       "      <td id=\"T_532a9_row0_col2\" class=\"data row0 col2\" >16.6411</td>\n",
+       "      <td id=\"T_532a9_row0_col3\" class=\"data row0 col3\" >4.0547</td>\n",
+       "      <td id=\"T_532a9_row0_col4\" class=\"data row0 col4\" >0.9387</td>\n",
+       "      <td id=\"T_532a9_row0_col5\" class=\"data row0 col5\" >0.1240</td>\n",
+       "      <td id=\"T_532a9_row0_col6\" class=\"data row0 col6\" >0.0896</td>\n",
+       "      <td id=\"T_532a9_row0_col7\" class=\"data row0 col7\" >0.7350</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_532a9_level0_row1\" class=\"row_heading level0 row1\" >lightgbm</th>\n",
+       "      <td id=\"T_532a9_row1_col0\" class=\"data row1 col0\" >Light Gradient Boosting Machine</td>\n",
+       "      <td id=\"T_532a9_row1_col1\" class=\"data row1 col1\" >3.0376</td>\n",
+       "      <td id=\"T_532a9_row1_col2\" class=\"data row1 col2\" >19.6723</td>\n",
+       "      <td id=\"T_532a9_row1_col3\" class=\"data row1 col3\" >4.4110</td>\n",
+       "      <td id=\"T_532a9_row1_col4\" class=\"data row1 col4\" >0.9274</td>\n",
+       "      <td id=\"T_532a9_row1_col5\" class=\"data row1 col5\" >0.1462</td>\n",
+       "      <td id=\"T_532a9_row1_col6\" class=\"data row1 col6\" >0.1077</td>\n",
+       "      <td id=\"T_532a9_row1_col7\" class=\"data row1 col7\" >0.0970</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_532a9_level0_row2\" class=\"row_heading level0 row2\" >et</th>\n",
+       "      <td id=\"T_532a9_row2_col0\" class=\"data row2 col0\" >Extra Trees Regressor</td>\n",
+       "      <td id=\"T_532a9_row2_col1\" class=\"data row2 col1\" >2.9868</td>\n",
+       "      <td id=\"T_532a9_row2_col2\" class=\"data row2 col2\" >20.9991</td>\n",
+       "      <td id=\"T_532a9_row2_col3\" class=\"data row2 col3\" >4.5721</td>\n",
+       "      <td id=\"T_532a9_row2_col4\" class=\"data row2 col4\" >0.9222</td>\n",
+       "      <td id=\"T_532a9_row2_col5\" class=\"data row2 col5\" >0.1436</td>\n",
+       "      <td id=\"T_532a9_row2_col6\" class=\"data row2 col6\" >0.1032</td>\n",
+       "      <td id=\"T_532a9_row2_col7\" class=\"data row2 col7\" >0.0430</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_532a9_level0_row3\" class=\"row_heading level0 row3\" >rf</th>\n",
+       "      <td id=\"T_532a9_row3_col0\" class=\"data row3 col0\" >Random Forest Regressor</td>\n",
+       "      <td id=\"T_532a9_row3_col1\" class=\"data row3 col1\" >3.5078</td>\n",
+       "      <td id=\"T_532a9_row3_col2\" class=\"data row3 col2\" >25.3881</td>\n",
+       "      <td id=\"T_532a9_row3_col3\" class=\"data row3 col3\" >5.0110</td>\n",
+       "      <td id=\"T_532a9_row3_col4\" class=\"data row3 col4\" >0.9059</td>\n",
+       "      <td id=\"T_532a9_row3_col5\" class=\"data row3 col5\" >0.1624</td>\n",
+       "      <td id=\"T_532a9_row3_col6\" class=\"data row3 col6\" >0.1245</td>\n",
+       "      <td id=\"T_532a9_row3_col7\" class=\"data row3 col7\" >0.0610</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_532a9_level0_row4\" class=\"row_heading level0 row4\" >gbr</th>\n",
+       "      <td id=\"T_532a9_row4_col0\" class=\"data row4 col0\" >Gradient Boosting Regressor</td>\n",
+       "      <td id=\"T_532a9_row4_col1\" class=\"data row4 col1\" >3.7207</td>\n",
+       "      <td id=\"T_532a9_row4_col2\" class=\"data row4 col2\" >26.2396</td>\n",
+       "      <td id=\"T_532a9_row4_col3\" class=\"data row4 col3\" >5.1038</td>\n",
+       "      <td id=\"T_532a9_row4_col4\" class=\"data row4 col4\" >0.9026</td>\n",
+       "      <td id=\"T_532a9_row4_col5\" class=\"data row4 col5\" >0.1618</td>\n",
+       "      <td id=\"T_532a9_row4_col6\" class=\"data row4 col6\" >0.1286</td>\n",
+       "      <td id=\"T_532a9_row4_col7\" class=\"data row4 col7\" >0.0290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_532a9_level0_row5\" class=\"row_heading level0 row5\" >dt</th>\n",
+       "      <td id=\"T_532a9_row5_col0\" class=\"data row5 col0\" >Decision Tree Regressor</td>\n",
+       "      <td id=\"T_532a9_row5_col1\" class=\"data row5 col1\" >4.2519</td>\n",
+       "      <td id=\"T_532a9_row5_col2\" class=\"data row5 col2\" >40.6646</td>\n",
+       "      <td id=\"T_532a9_row5_col3\" class=\"data row5 col3\" >6.3491</td>\n",
+       "      <td id=\"T_532a9_row5_col4\" class=\"data row5 col4\" >0.8487</td>\n",
+       "      <td id=\"T_532a9_row5_col5\" class=\"data row5 col5\" >0.2172</td>\n",
+       "      <td id=\"T_532a9_row5_col6\" class=\"data row5 col6\" >0.1526</td>\n",
+       "      <td id=\"T_532a9_row5_col7\" class=\"data row5 col7\" >0.0080</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_532a9_level0_row6\" class=\"row_heading level0 row6\" >ada</th>\n",
+       "      <td id=\"T_532a9_row6_col0\" class=\"data row6 col0\" >AdaBoost Regressor</td>\n",
+       "      <td id=\"T_532a9_row6_col1\" class=\"data row6 col1\" >6.2031</td>\n",
+       "      <td id=\"T_532a9_row6_col2\" class=\"data row6 col2\" >57.3758</td>\n",
+       "      <td id=\"T_532a9_row6_col3\" class=\"data row6 col3\" >7.5468</td>\n",
+       "      <td id=\"T_532a9_row6_col4\" class=\"data row6 col4\" >0.7867</td>\n",
+       "      <td id=\"T_532a9_row6_col5\" class=\"data row6 col5\" >0.2829</td>\n",
+       "      <td id=\"T_532a9_row6_col6\" class=\"data row6 col6\" >0.2659</td>\n",
+       "      <td id=\"T_532a9_row6_col7\" class=\"data row6 col7\" >0.0220</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_532a9_level0_row7\" class=\"row_heading level0 row7\" >knn</th>\n",
+       "      <td id=\"T_532a9_row7_col0\" class=\"data row7 col0\" >K Neighbors Regressor</td>\n",
+       "      <td id=\"T_532a9_row7_col1\" class=\"data row7 col1\" >7.1263</td>\n",
+       "      <td id=\"T_532a9_row7_col2\" class=\"data row7 col2\" >88.3398</td>\n",
+       "      <td id=\"T_532a9_row7_col3\" class=\"data row7 col3\" >9.3822</td>\n",
+       "      <td id=\"T_532a9_row7_col4\" class=\"data row7 col4\" >0.6730</td>\n",
+       "      <td id=\"T_532a9_row7_col5\" class=\"data row7 col5\" >0.3145</td>\n",
+       "      <td id=\"T_532a9_row7_col6\" class=\"data row7 col6\" >0.2823</td>\n",
+       "      <td id=\"T_532a9_row7_col7\" class=\"data row7 col7\" >0.0100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_532a9_level0_row8\" class=\"row_heading level0 row8\" >en</th>\n",
+       "      <td id=\"T_532a9_row8_col0\" class=\"data row8 col0\" >Elastic Net</td>\n",
+       "      <td id=\"T_532a9_row8_col1\" class=\"data row8 col1\" >8.3257</td>\n",
+       "      <td id=\"T_532a9_row8_col2\" class=\"data row8 col2\" >111.5700</td>\n",
+       "      <td id=\"T_532a9_row8_col3\" class=\"data row8 col3\" >10.5469</td>\n",
+       "      <td id=\"T_532a9_row8_col4\" class=\"data row8 col4\" >0.5836</td>\n",
+       "      <td id=\"T_532a9_row8_col5\" class=\"data row8 col5\" >0.3396</td>\n",
+       "      <td id=\"T_532a9_row8_col6\" class=\"data row8 col6\" >0.3241</td>\n",
+       "      <td id=\"T_532a9_row8_col7\" class=\"data row8 col7\" >0.0110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_532a9_level0_row9\" class=\"row_heading level0 row9\" >lar</th>\n",
+       "      <td id=\"T_532a9_row9_col0\" class=\"data row9 col0\" >Least Angle Regression</td>\n",
+       "      <td id=\"T_532a9_row9_col1\" class=\"data row9 col1\" >8.3228</td>\n",
+       "      <td id=\"T_532a9_row9_col2\" class=\"data row9 col2\" >111.5532</td>\n",
+       "      <td id=\"T_532a9_row9_col3\" class=\"data row9 col3\" >10.5453</td>\n",
+       "      <td id=\"T_532a9_row9_col4\" class=\"data row9 col4\" >0.5836</td>\n",
+       "      <td id=\"T_532a9_row9_col5\" class=\"data row9 col5\" >0.3391</td>\n",
+       "      <td id=\"T_532a9_row9_col6\" class=\"data row9 col6\" >0.3235</td>\n",
+       "      <td id=\"T_532a9_row9_col7\" class=\"data row9 col7\" >0.0510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_532a9_level0_row10\" class=\"row_heading level0 row10\" >ridge</th>\n",
+       "      <td id=\"T_532a9_row10_col0\" class=\"data row10 col0\" >Ridge Regression</td>\n",
+       "      <td id=\"T_532a9_row10_col1\" class=\"data row10 col1\" >8.3228</td>\n",
+       "      <td id=\"T_532a9_row10_col2\" class=\"data row10 col2\" >111.5532</td>\n",
+       "      <td id=\"T_532a9_row10_col3\" class=\"data row10 col3\" >10.5453</td>\n",
+       "      <td id=\"T_532a9_row10_col4\" class=\"data row10 col4\" >0.5836</td>\n",
+       "      <td id=\"T_532a9_row10_col5\" class=\"data row10 col5\" >0.3391</td>\n",
+       "      <td id=\"T_532a9_row10_col6\" class=\"data row10 col6\" >0.3235</td>\n",
+       "      <td id=\"T_532a9_row10_col7\" class=\"data row10 col7\" >0.0100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_532a9_level0_row11\" class=\"row_heading level0 row11\" >lr</th>\n",
+       "      <td id=\"T_532a9_row11_col0\" class=\"data row11 col0\" >Linear Regression</td>\n",
+       "      <td id=\"T_532a9_row11_col1\" class=\"data row11 col1\" >8.3228</td>\n",
+       "      <td id=\"T_532a9_row11_col2\" class=\"data row11 col2\" >111.5532</td>\n",
+       "      <td id=\"T_532a9_row11_col3\" class=\"data row11 col3\" >10.5453</td>\n",
+       "      <td id=\"T_532a9_row11_col4\" class=\"data row11 col4\" >0.5836</td>\n",
+       "      <td id=\"T_532a9_row11_col5\" class=\"data row11 col5\" >0.3391</td>\n",
+       "      <td id=\"T_532a9_row11_col6\" class=\"data row11 col6\" >0.3235</td>\n",
+       "      <td id=\"T_532a9_row11_col7\" class=\"data row11 col7\" >1.0730</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_532a9_level0_row12\" class=\"row_heading level0 row12\" >br</th>\n",
+       "      <td id=\"T_532a9_row12_col0\" class=\"data row12 col0\" >Bayesian Ridge</td>\n",
+       "      <td id=\"T_532a9_row12_col1\" class=\"data row12 col1\" >8.3202</td>\n",
+       "      <td id=\"T_532a9_row12_col2\" class=\"data row12 col2\" >111.6163</td>\n",
+       "      <td id=\"T_532a9_row12_col3\" class=\"data row12 col3\" >10.5503</td>\n",
+       "      <td id=\"T_532a9_row12_col4\" class=\"data row12 col4\" >0.5835</td>\n",
+       "      <td id=\"T_532a9_row12_col5\" class=\"data row12 col5\" >0.3404</td>\n",
+       "      <td id=\"T_532a9_row12_col6\" class=\"data row12 col6\" >0.3247</td>\n",
+       "      <td id=\"T_532a9_row12_col7\" class=\"data row12 col7\" >0.0080</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_532a9_level0_row13\" class=\"row_heading level0 row13\" >lasso</th>\n",
+       "      <td id=\"T_532a9_row13_col0\" class=\"data row13 col0\" >Lasso Regression</td>\n",
+       "      <td id=\"T_532a9_row13_col1\" class=\"data row13 col1\" >8.3295</td>\n",
+       "      <td id=\"T_532a9_row13_col2\" class=\"data row13 col2\" >111.6391</td>\n",
+       "      <td id=\"T_532a9_row13_col3\" class=\"data row13 col3\" >10.5506</td>\n",
+       "      <td id=\"T_532a9_row13_col4\" class=\"data row13 col4\" >0.5833</td>\n",
+       "      <td id=\"T_532a9_row13_col5\" class=\"data row13 col5\" >0.3400</td>\n",
+       "      <td id=\"T_532a9_row13_col6\" class=\"data row13 col6\" >0.3246</td>\n",
+       "      <td id=\"T_532a9_row13_col7\" class=\"data row13 col7\" >0.2510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_532a9_level0_row14\" class=\"row_heading level0 row14\" >llar</th>\n",
+       "      <td id=\"T_532a9_row14_col0\" class=\"data row14 col0\" >Lasso Least Angle Regression</td>\n",
+       "      <td id=\"T_532a9_row14_col1\" class=\"data row14 col1\" >8.3294</td>\n",
+       "      <td id=\"T_532a9_row14_col2\" class=\"data row14 col2\" >111.6389</td>\n",
+       "      <td id=\"T_532a9_row14_col3\" class=\"data row14 col3\" >10.5506</td>\n",
+       "      <td id=\"T_532a9_row14_col4\" class=\"data row14 col4\" >0.5833</td>\n",
+       "      <td id=\"T_532a9_row14_col5\" class=\"data row14 col5\" >0.3400</td>\n",
+       "      <td id=\"T_532a9_row14_col6\" class=\"data row14 col6\" >0.3246</td>\n",
+       "      <td id=\"T_532a9_row14_col7\" class=\"data row14 col7\" >0.0080</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_532a9_level0_row15\" class=\"row_heading level0 row15\" >huber</th>\n",
+       "      <td id=\"T_532a9_row15_col0\" class=\"data row15 col0\" >Huber Regressor</td>\n",
+       "      <td id=\"T_532a9_row15_col1\" class=\"data row15 col1\" >8.2477</td>\n",
+       "      <td id=\"T_532a9_row15_col2\" class=\"data row15 col2\" >115.4764</td>\n",
+       "      <td id=\"T_532a9_row15_col3\" class=\"data row15 col3\" >10.7216</td>\n",
+       "      <td id=\"T_532a9_row15_col4\" class=\"data row15 col4\" >0.5675</td>\n",
+       "      <td id=\"T_532a9_row15_col5\" class=\"data row15 col5\" >0.3320</td>\n",
+       "      <td id=\"T_532a9_row15_col6\" class=\"data row15 col6\" >0.3153</td>\n",
+       "      <td id=\"T_532a9_row15_col7\" class=\"data row15 col7\" >0.0120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_532a9_level0_row16\" class=\"row_heading level0 row16\" >par</th>\n",
+       "      <td id=\"T_532a9_row16_col0\" class=\"data row16 col0\" >Passive Aggressive Regressor</td>\n",
+       "      <td id=\"T_532a9_row16_col1\" class=\"data row16 col1\" >9.5432</td>\n",
+       "      <td id=\"T_532a9_row16_col2\" class=\"data row16 col2\" >147.8627</td>\n",
+       "      <td id=\"T_532a9_row16_col3\" class=\"data row16 col3\" >12.0127</td>\n",
+       "      <td id=\"T_532a9_row16_col4\" class=\"data row16 col4\" >0.4577</td>\n",
+       "      <td id=\"T_532a9_row16_col5\" class=\"data row16 col5\" >0.3793</td>\n",
+       "      <td id=\"T_532a9_row16_col6\" class=\"data row16 col6\" >0.3504</td>\n",
+       "      <td id=\"T_532a9_row16_col7\" class=\"data row16 col7\" >0.0090</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_532a9_level0_row17\" class=\"row_heading level0 row17\" >omp</th>\n",
+       "      <td id=\"T_532a9_row17_col0\" class=\"data row17 col0\" >Orthogonal Matching Pursuit</td>\n",
+       "      <td id=\"T_532a9_row17_col1\" class=\"data row17 col1\" >11.9797</td>\n",
+       "      <td id=\"T_532a9_row17_col2\" class=\"data row17 col2\" >213.4601</td>\n",
+       "      <td id=\"T_532a9_row17_col3\" class=\"data row17 col3\" >14.5622</td>\n",
+       "      <td id=\"T_532a9_row17_col4\" class=\"data row17 col4\" >0.2157</td>\n",
+       "      <td id=\"T_532a9_row17_col5\" class=\"data row17 col5\" >0.4788</td>\n",
+       "      <td id=\"T_532a9_row17_col6\" class=\"data row17 col6\" >0.5142</td>\n",
+       "      <td id=\"T_532a9_row17_col7\" class=\"data row17 col7\" >0.0070</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_532a9_level0_row18\" class=\"row_heading level0 row18\" >dummy</th>\n",
+       "      <td id=\"T_532a9_row18_col0\" class=\"data row18 col0\" >Dummy Regressor</td>\n",
+       "      <td id=\"T_532a9_row18_col1\" class=\"data row18 col1\" >13.4902</td>\n",
+       "      <td id=\"T_532a9_row18_col2\" class=\"data row18 col2\" >281.5103</td>\n",
+       "      <td id=\"T_532a9_row18_col3\" class=\"data row18 col3\" >16.7328</td>\n",
+       "      <td id=\"T_532a9_row18_col4\" class=\"data row18 col4\" >-0.0317</td>\n",
+       "      <td id=\"T_532a9_row18_col5\" class=\"data row18 col5\" >0.5436</td>\n",
+       "      <td id=\"T_532a9_row18_col6\" class=\"data row18 col6\" >0.6176</td>\n",
+       "      <td id=\"T_532a9_row18_col7\" class=\"data row18 col7\" >0.0070</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x2bbe3906e50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<catboost.core.CatBoostRegressor at 0x2bbe4008b10>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compare_models()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_88a14_row10_col0, #T_88a14_row10_col1, #T_88a14_row10_col2, #T_88a14_row10_col3, #T_88a14_row10_col4, #T_88a14_row10_col5 {\n",
+       "  background: yellow;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_88a14\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_88a14_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
+       "      <th id=\"T_88a14_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
+       "      <th id=\"T_88a14_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
+       "      <th id=\"T_88a14_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
+       "      <th id=\"T_88a14_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
+       "      <th id=\"T_88a14_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >Fold</th>\n",
+       "      <th class=\"blank col0\" >&nbsp;</th>\n",
+       "      <th class=\"blank col1\" >&nbsp;</th>\n",
+       "      <th class=\"blank col2\" >&nbsp;</th>\n",
+       "      <th class=\"blank col3\" >&nbsp;</th>\n",
+       "      <th class=\"blank col4\" >&nbsp;</th>\n",
+       "      <th class=\"blank col5\" >&nbsp;</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_88a14_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_88a14_row0_col0\" class=\"data row0 col0\" >2.9173</td>\n",
+       "      <td id=\"T_88a14_row0_col1\" class=\"data row0 col1\" >18.7030</td>\n",
+       "      <td id=\"T_88a14_row0_col2\" class=\"data row0 col2\" >4.3247</td>\n",
+       "      <td id=\"T_88a14_row0_col3\" class=\"data row0 col3\" >0.9181</td>\n",
+       "      <td id=\"T_88a14_row0_col4\" class=\"data row0 col4\" >0.1655</td>\n",
+       "      <td id=\"T_88a14_row0_col5\" class=\"data row0 col5\" >0.1202</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_88a14_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "      <td id=\"T_88a14_row1_col0\" class=\"data row1 col0\" >2.9024</td>\n",
+       "      <td id=\"T_88a14_row1_col1\" class=\"data row1 col1\" >19.3824</td>\n",
+       "      <td id=\"T_88a14_row1_col2\" class=\"data row1 col2\" >4.4025</td>\n",
+       "      <td id=\"T_88a14_row1_col3\" class=\"data row1 col3\" >0.9262</td>\n",
+       "      <td id=\"T_88a14_row1_col4\" class=\"data row1 col4\" >0.1294</td>\n",
+       "      <td id=\"T_88a14_row1_col5\" class=\"data row1 col5\" >0.0954</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_88a14_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+       "      <td id=\"T_88a14_row2_col0\" class=\"data row2 col0\" >2.8908</td>\n",
+       "      <td id=\"T_88a14_row2_col1\" class=\"data row2 col1\" >18.4849</td>\n",
+       "      <td id=\"T_88a14_row2_col2\" class=\"data row2 col2\" >4.2994</td>\n",
+       "      <td id=\"T_88a14_row2_col3\" class=\"data row2 col3\" >0.9436</td>\n",
+       "      <td id=\"T_88a14_row2_col4\" class=\"data row2 col4\" >0.1229</td>\n",
+       "      <td id=\"T_88a14_row2_col5\" class=\"data row2 col5\" >0.0830</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_88a14_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+       "      <td id=\"T_88a14_row3_col0\" class=\"data row3 col0\" >2.8456</td>\n",
+       "      <td id=\"T_88a14_row3_col1\" class=\"data row3 col1\" >20.3588</td>\n",
+       "      <td id=\"T_88a14_row3_col2\" class=\"data row3 col2\" >4.5121</td>\n",
+       "      <td id=\"T_88a14_row3_col3\" class=\"data row3 col3\" >0.9252</td>\n",
+       "      <td id=\"T_88a14_row3_col4\" class=\"data row3 col4\" >0.1409</td>\n",
+       "      <td id=\"T_88a14_row3_col5\" class=\"data row3 col5\" >0.0959</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_88a14_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "      <td id=\"T_88a14_row4_col0\" class=\"data row4 col0\" >2.8268</td>\n",
+       "      <td id=\"T_88a14_row4_col1\" class=\"data row4 col1\" >15.9504</td>\n",
+       "      <td id=\"T_88a14_row4_col2\" class=\"data row4 col2\" >3.9938</td>\n",
+       "      <td id=\"T_88a14_row4_col3\" class=\"data row4 col3\" >0.9479</td>\n",
+       "      <td id=\"T_88a14_row4_col4\" class=\"data row4 col4\" >0.1726</td>\n",
+       "      <td id=\"T_88a14_row4_col5\" class=\"data row4 col5\" >0.1379</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_88a14_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+       "      <td id=\"T_88a14_row5_col0\" class=\"data row5 col0\" >3.7981</td>\n",
+       "      <td id=\"T_88a14_row5_col1\" class=\"data row5 col1\" >30.2989</td>\n",
+       "      <td id=\"T_88a14_row5_col2\" class=\"data row5 col2\" >5.5044</td>\n",
+       "      <td id=\"T_88a14_row5_col3\" class=\"data row5 col3\" >0.8957</td>\n",
+       "      <td id=\"T_88a14_row5_col4\" class=\"data row5 col4\" >0.1873</td>\n",
+       "      <td id=\"T_88a14_row5_col5\" class=\"data row5 col5\" >0.1403</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_88a14_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+       "      <td id=\"T_88a14_row6_col0\" class=\"data row6 col0\" >3.2850</td>\n",
+       "      <td id=\"T_88a14_row6_col1\" class=\"data row6 col1\" >23.0887</td>\n",
+       "      <td id=\"T_88a14_row6_col2\" class=\"data row6 col2\" >4.8051</td>\n",
+       "      <td id=\"T_88a14_row6_col3\" class=\"data row6 col3\" >0.9225</td>\n",
+       "      <td id=\"T_88a14_row6_col4\" class=\"data row6 col4\" >0.1422</td>\n",
+       "      <td id=\"T_88a14_row6_col5\" class=\"data row6 col5\" >0.1012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_88a14_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+       "      <td id=\"T_88a14_row7_col0\" class=\"data row7 col0\" >3.1015</td>\n",
+       "      <td id=\"T_88a14_row7_col1\" class=\"data row7 col1\" >19.8103</td>\n",
+       "      <td id=\"T_88a14_row7_col2\" class=\"data row7 col2\" >4.4509</td>\n",
+       "      <td id=\"T_88a14_row7_col3\" class=\"data row7 col3\" >0.9254</td>\n",
+       "      <td id=\"T_88a14_row7_col4\" class=\"data row7 col4\" >0.1636</td>\n",
+       "      <td id=\"T_88a14_row7_col5\" class=\"data row7 col5\" >0.1190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_88a14_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+       "      <td id=\"T_88a14_row8_col0\" class=\"data row8 col0\" >2.7379</td>\n",
+       "      <td id=\"T_88a14_row8_col1\" class=\"data row8 col1\" >13.6518</td>\n",
+       "      <td id=\"T_88a14_row8_col2\" class=\"data row8 col2\" >3.6948</td>\n",
+       "      <td id=\"T_88a14_row8_col3\" class=\"data row8 col3\" >0.9321</td>\n",
+       "      <td id=\"T_88a14_row8_col4\" class=\"data row8 col4\" >0.1104</td>\n",
+       "      <td id=\"T_88a14_row8_col5\" class=\"data row8 col5\" >0.0848</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_88a14_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+       "      <td id=\"T_88a14_row9_col0\" class=\"data row9 col0\" >3.0709</td>\n",
+       "      <td id=\"T_88a14_row9_col1\" class=\"data row9 col1\" >16.9938</td>\n",
+       "      <td id=\"T_88a14_row9_col2\" class=\"data row9 col2\" >4.1223</td>\n",
+       "      <td id=\"T_88a14_row9_col3\" class=\"data row9 col3\" >0.9374</td>\n",
+       "      <td id=\"T_88a14_row9_col4\" class=\"data row9 col4\" >0.1267</td>\n",
+       "      <td id=\"T_88a14_row9_col5\" class=\"data row9 col5\" >0.0988</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_88a14_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
+       "      <td id=\"T_88a14_row10_col0\" class=\"data row10 col0\" >3.0376</td>\n",
+       "      <td id=\"T_88a14_row10_col1\" class=\"data row10 col1\" >19.6723</td>\n",
+       "      <td id=\"T_88a14_row10_col2\" class=\"data row10 col2\" >4.4110</td>\n",
+       "      <td id=\"T_88a14_row10_col3\" class=\"data row10 col3\" >0.9274</td>\n",
+       "      <td id=\"T_88a14_row10_col4\" class=\"data row10 col4\" >0.1462</td>\n",
+       "      <td id=\"T_88a14_row10_col5\" class=\"data row10 col5\" >0.1077</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_88a14_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
+       "      <td id=\"T_88a14_row11_col0\" class=\"data row11 col0\" >0.2950</td>\n",
+       "      <td id=\"T_88a14_row11_col1\" class=\"data row11 col1\" >4.2988</td>\n",
+       "      <td id=\"T_88a14_row11_col2\" class=\"data row11 col2\" >0.4640</td>\n",
+       "      <td id=\"T_88a14_row11_col3\" class=\"data row11 col3\" >0.0139</td>\n",
+       "      <td id=\"T_88a14_row11_col4\" class=\"data row11 col4\" >0.0236</td>\n",
+       "      <td id=\"T_88a14_row11_col5\" class=\"data row11 col5\" >0.0195</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x2bbe383bad0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model = create_model(\"lightgbm\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x550 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_model(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "pickle.dump(model, open('modelCiment2.pkl', 'wb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "PycaretVenv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}