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install pycaret\n"]},{"cell_type":"code","source":["#Import Librairies\n","import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import pycaret as py\n","import seaborn as sns"],"metadata":{"id":"EVFRS8x1y49o","executionInfo":{"status":"ok","timestamp":1718906251400,"user_tz":-60,"elapsed":2334,"user":{"displayName":"Oceane Hadama","userId":"07293307270246501044"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["data = pd.read_csv(\"concrete.csv\")\n","data.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":226},"id":"Bgz7Dt2Fy_Lp","executionInfo":{"status":"ok","timestamp":1718906533290,"user_tz":-60,"elapsed":593,"user":{"displayName":"Oceane Hadama","userId":"07293307270246501044"}},"outputId":"910958f6-6b27-477e-e384-d085ea278e1c"},"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"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"],"text/html":["\n","  <div id=\"df-a5dfd491-819d-4ad4-9b57-044b296bb40e\" class=\"colab-df-container\">\n","    <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","      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0.000000   185.000000      6.400000   \n","75%     350.000000   142.950000   118.300000   192.000000     10.200000   \n","max     540.000000   359.400000   200.100000   247.000000     32.200000   \n","\n","         coarseagg      fineagg          age     strength  \n","count  1030.000000  1030.000000  1030.000000  1030.000000  \n","mean    972.918932   773.580485    45.662136    35.817961  \n","std      77.753954    80.175980    63.169912    16.705742  \n","min     801.000000   594.000000     1.000000     2.330000  \n","25%     932.000000   730.950000     7.000000    23.710000  \n","50%     968.000000   779.500000    28.000000    34.445000  \n","75%    1029.400000   824.000000    56.000000    46.135000  \n","max    1145.000000   992.600000   365.000000    82.600000  "],"text/html":["\n","  <div id=\"df-b84f6e02-5135-4ac9-82bb-1a7d0d34844d\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: 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<td>164.900000</td>\n","      <td>0.000000</td>\n","      <td>932.000000</td>\n","      <td>730.950000</td>\n","      <td>7.000000</td>\n","      <td>23.710000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>272.900000</td>\n","      <td>22.000000</td>\n","      <td>0.000000</td>\n","      <td>185.000000</td>\n","      <td>6.400000</td>\n","      <td>968.000000</td>\n","      <td>779.500000</td>\n","      <td>28.000000</td>\n","      <td>34.445000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>350.000000</td>\n","      <td>142.950000</td>\n","      <td>118.300000</td>\n","      <td>192.000000</td>\n","      <td>10.200000</td>\n","      <td>1029.400000</td>\n","      <td>824.000000</td>\n","      <td>56.000000</td>\n","      <td>46.135000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>540.000000</td>\n","      <td>359.400000</td>\n","      <td>200.100000</td>\n","      <td>247.000000</td>\n","      <td>32.200000</td>\n","  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\"min\": 0.0,\n        \"max\": 1030.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1030.0,\n          6.204660194174758,\n          10.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"coarseagg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 334.4689898607596,\n        \"min\": 77.75395396672077,\n        \"max\": 1145.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          972.9189320388349,\n          968.0,\n          1030.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fineagg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 295.86618714901516,\n        \"min\": 80.17598014240437,\n        \"max\": 1030.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          773.5804854368932,\n          779.5,\n          1030.0\n   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data.duplicated()\n","duplicates.value_counts()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"q1nNC40O0gYS","executionInfo":{"status":"ok","timestamp":1718906669661,"user_tz":-60,"elapsed":577,"user":{"displayName":"Oceane Hadama","userId":"07293307270246501044"}},"outputId":"5e8e376f-a66d-474b-e7dd-cc3f310a5e00"},"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["False    1005\n","True       25\n","Name: count, dtype: int64"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","source":["dropped_duplicates = data.drop_duplicates(subset = [\"cement\"],keep=\"first\")\n","dropped_duplicates"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":443},"id":"IVS7hepW0lY0","executionInfo":{"status":"ok","timestamp":1718906732621,"user_tz":-60,"elapsed":744,"user":{"displayName":"Oceane 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     <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>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>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","    <tr>\n","      <th>5</th>\n","      <td>266.0</td>\n","      <td>114.0</td>\n","      <td>0.0</td>\n","      <td>228.0</td>\n","      <td>0.0</td>\n","      <td>932.0</td>\n","      <td>670.0</td>\n","      <td>90</td>\n","      <td>47.03</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>380.0</td>\n","      <td>95.0</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>43.70</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\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","<p>278 rows × 9 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2894f13d-a5d2-4d5f-b1c0-63dc25806dec')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      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]\n}"}},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["data.isna().sum()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KyD-GNRk02tp","executionInfo":{"status":"ok","timestamp":1718906752748,"user_tz":-60,"elapsed":570,"user":{"displayName":"Oceane Hadama","userId":"07293307270246501044"}},"outputId":"cc4fb750-00db-4c0f-c50e-071002283cbe"},"execution_count":11,"outputs":[{"output_type":"execute_result","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"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["#Utilisation des boxplot pour visualiser les valeurs abérrantes\n","\n","# Création des boxplots pour chaque colonne\n","plt.figure(figsize=(12, 8))\n","for column in data.columns:\n","    plt.figure()\n","    sns.boxplot(data[column])\n","    plt.title(f\"Boxplot de la colonne {column}\")\n","    plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"N35LKMnm05o9","executionInfo":{"status":"ok","timestamp":1718906789741,"user_tz":-60,"elapsed":6647,"user":{"displayName":"Oceane Hadama","userId":"07293307270246501044"}},"outputId":"1e1f8ed5-04a5-46ad-c62d-2d8a6c656ba3"},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x800 with 0 Axes>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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4fA/h1KlT9f777yslJUVjx45VcnKySktL9e9//1urV69WaWnpufswcGHz34NgQN1W3aPn4eHhpnPnzmbu3Lk+jy4bY8zBgwfNhAkTTHx8vAkNDTWtW7c2Tz/9tNVuw4YNJiQkxOdxcmOMOXbsmOnatauJj4+33suSnp5u6tevb3bs2GH69u1r6tWrZ2JiYsyjjz5qKioqfLbXCY//GmPMv//9b5OWlmYaNGhg6tWrZ3r27Gk+/PDDk87xz3/+s2nRooUJDg4+o8fQ33zzTdOuXTvjdDpNcnKyWbJkiUlPTz/pPTvGGDN//nzTpUsXExERYSIjI03Hjh3Ngw8+aAoLC3/2GNU9ev73v//ddOrUyYSHh5tmzZqZp556yrz00ktGktm1a9fP7s+Yn95H8+tf/9o0bNjQhIeHmzZt2pz0vqIz+cyqe8+OMcb87W9/M5deeqlxOp0mKirKjBgxwnz77bc+bap+p6c736pHz59++umT2lb3u96xY4e59dZbTWxsrAkNDTX/9V//ZQYOHGjeeOON034uL774omndurVxOp2mbdu2ZsGCBdV+/mVlZSYjI8NERUWZBg0amMGDB5v8/HwjyUydOtWnbXFxscnIyDAJCQkmNDTUxMbGmt69e5v58+efth6gphzGnMHrVgEElFGjRumNN97w6TEBAkleXp4uvfRSvfbaaxoxYoS/y8EFjjE7AICzcvzXm1SZOXOmgoKC1L17dz9UBPhizA4A4KxMmzZNGzZsUM+ePRUSEqLly5dr+fLlGjdu3CmfVAPOJ8IOAOCsXHnllVq1apWeeOIJHTp0SImJiXrsscdO+jJVwF8YswMAAGyNMTsAAMDWCDsAAMDWGLOjn167X1hYqMjIyF/0inwAAOA/xhgdPHhQ8fHxCgo6df8NYUdSYWEhTwwAAFBH7dmzR02bNj3lesKOZH0x4549e075HTcAACCweL1eJSQk+HzBcnUIO/r/393icrkIOwAA1DGnG4LCAGUAAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AtrV9+3b17NlTPXr0UM+ePbV9+3Z/lwTAD3iDMgBb6tGjh8+8MUa33367JCknJ+f8FwTAb+jZAWA7xwed4OBgjRw5UsHBwdWuB2B/9OwAsJXjb1W99tpr1jchjxkzRt9++61uueUWq12rVq38UiOA84ueHQC2MnbsWEk/9ehUBZ0qTZs2tXp4qtoBsD/CDgBbMcZIkoYPH67S0lKNGjVKgwYN0qhRo1RaWqrf/OY3Pu0A2J/D8DdeXq9XbrdbHo9HLpfL3+UAOAs9e/Y8oyDjcDj0/vvvn4eKAJwrZ3r9pmcHgK38+c9/9plPTk7Wn/70JyUnJ/9sOwD2RdgBYCtRUVE+8/n5+fr000+Vn5//s+0A2BdhB4CtTJw40We+oqJCixYtUkVFxc+2A2BfhB0AtrJ//35J0p/+9Cf95S9/kcPhkPTTGJ2//OUvmjZtmk87APbHe3YA2EqjRo108OBBvfjii5ozZ85Jg5Dvuusuqx2ACwM9OwBs5ZlnnpEkffnllzp06JDPukOHDumrr77yaQfA/ujZAWArUVFRioqKUmlpqQYOHKh27drptttu04IFC6ygU9UGwIWB9+yI9+wAdnTDDTeotLT0pOVRUVFasmSJHyoCUNvqxHt25s6dq06dOsnlcsnlcik1NVXLly+31vfo0UMOh8NnuvPOO332UVBQoAEDBqhevXqKjo7WAw88oGPHjp3vUwEQYJYsWaIlS5aoWbNmioyMVLNmzaxlAC4sfr2N1bRpU02dOlWtW7eWMUYvv/yyrr/+en3++edq3769pJ++v2bKlCnWNvXq1bN+rqio0IABAxQbG6sPP/xQe/fu1a233qrQ0FD98Y9/PO/nAyCwREVFaeHChf4uA4CfBdxtrKioKD399NMaM2aMevTooc6dO2vmzJnVtl2+fLkGDhyowsJCxcTESJLmzZunSZMmad++fQoLCzujY3IbCwCAuqdO3MY6XtWLv8rKypSammotf/3119W4cWN16NBBmZmZ+uGHH6x1ubm56tixoxV0JCktLU1er1dbtmw55bHKy8vl9Xp9JgAAYE9+fxpr06ZNSk1N1eHDh9WgQQO99dZb1nfYDB8+XElJSYqPj9fGjRs1adIk5efnW/fci4qKfIKOJGu+qKjolMfMysrS448/fo7OCAAABBK/h502bdooLy9PHo9Hb7zxhtLT07Vu3TolJydr3LhxVruOHTsqLi5OvXv31o4dO9SyZcsaHzMzM9PnVfFer1cJCQlndR4AACAw+f02VlhYmFq1aqUuXbooKytLl1xyiZ599tlq26akpEiStm/fLkmKjY1VcXGxT5uq+djY2FMe0+l0Wk+AVU0AAMCe/B52TlRZWany8vJq1+Xl5UmS4uLiJEmpqanatGmTSkpKrDarVq2Sy+WyboUBAIALm19vY2VmZqpfv35KTEzUwYMHlZ2drZycHK1cuVI7duxQdna2+vfvr0aNGmnjxo2aMGGCunfvrk6dOkmS+vbtq+TkZI0cOVLTpk1TUVGRJk+erIyMDDmdTn+eGgAACBB+DTslJSW69dZbtXfvXrndbnXq1EkrV67UNddcoz179mj16tWaOXOmysrKlJCQoCFDhmjy5MnW9sHBwVq2bJnuuusupaamqn79+kpPT/d5Lw8AALiwBdx7dvyB9+wAAFD31Ln37AAAAJwLhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrfg07c+fOVadOneRyueRyuZSamqrly5db6w8fPqyMjAw1atRIDRo00JAhQ1RcXOyzj4KCAg0YMED16tVTdHS0HnjgAR07dux8nwoAAAhQfg07TZs21dSpU7VhwwZ99tln6tWrl66//npt2bJFkjRhwgS98847Wrx4sdatW6fCwkLdcMMN1vYVFRUaMGCAjhw5og8//FAvv/yyFi5cqEceecRfpwQAAAKMwxhj/F3E8aKiovT0009r6NChatKkibKzszV06FBJ0tatW9WuXTvl5ubqiiuu0PLlyzVw4EAVFhYqJiZGkjRv3jxNmjRJ+/btU1hYWLXHKC8vV3l5uTXv9XqVkJAgj8cjl8t17k8SAACcNa/XK7fbfdrrd8CM2amoqNCiRYtUVlam1NRUbdiwQUePHlWfPn2sNm3btlViYqJyc3MlSbm5uerYsaMVdCQpLS1NXq/X6h2qTlZWltxutzUlJCScuxMDAAB+5fews2nTJjVo0EBOp1N33nmn3nrrLSUnJ6uoqEhhYWFq2LChT/uYmBgVFRVJkoqKinyCTtX6qnWnkpmZKY/HY0179uyp3ZMCAAABI8TfBbRp00Z5eXnyeDx64403lJ6ernXr1p3TYzqdTjmdznN6DAAAEBj8HnbCwsLUqlUrSVKXLl306aef6tlnn9VNN92kI0eO6MCBAz69O8XFxYqNjZUkxcbG6pNPPvHZX9XTWlVtAADAhc3vt7FOVFlZqfLycnXp0kWhoaFas2aNtS4/P18FBQVKTU2VJKWmpmrTpk0qKSmx2qxatUoul0vJycnnvXYAABB4/Nqzk5mZqX79+ikxMVEHDx5Udna2cnJytHLlSrndbo0ZM0YTJ05UVFSUXC6X7rnnHqWmpuqKK66QJPXt21fJyckaOXKkpk2bpqKiIk2ePFkZGRncpgIAAJL8HHZKSkp06623au/evXK73erUqZNWrlypa665RpI0Y8YMBQUFaciQISovL1daWprmzJljbR8cHKxly5bprrvuUmpqqurXr6/09HRNmTLFX6cEAAACTMC9Z8cfzvQ5fQAAEDjq3Ht2AAAAzgXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgDb2rlzp3r16qUePXqoV69e2rlzp79LAuAHIf4uAADOhR49evjMV1ZWavTo0ZKknJyc818QAL+hZweA7RwfdEJDQzV69GiFhoZWux6A/dGzA8BWjr9VlZ2drfj4eEnSrbfeqsLCQg0fPtxq16JFC7/UCOD8omcHgK3cfvvtkn7q0akKOlXi4+OtHp6qdgDsj7ADwFYqKyslSSNHjqx2/bBhw3zaAbA/wg4AWwkK+ul/a6+++mq16xctWuTTDoD98bcdgK385S9/kSQdPXpUhYWFPusKCwt19OhRn3YA7I8BygBs5fhBx8OHD1doaKiGDRumRYsWWUHnxHYA7I2eHQC2c/x7dI4ePapXX33VJ+jwnh3gwkLYAWBLOTk5eumll6yxOUFBQXrppZcIOsAFiNtYAGyrRYsWWrt2rb/LAOBn9OwAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABb82vYycrKUteuXRUZGano6GgNHjxY+fn5Pm169Oghh8PhM915550+bQoKCjRgwADVq1dP0dHReuCBB3Ts2LHzeSoAACBA+fUNyuvWrVNGRoa6du2qY8eO6fe//7369u2rL7/8UvXr17fajR07VlOmTLHm69WrZ/1cUVGhAQMGKDY2Vh9++KH27t2rW2+9VaGhofrjH/94Xs8HAAAEHocxxvi7iCr79u1TdHS01q1bp+7du0v6qWenc+fOmjlzZrXbLF++XAMHDlRhYaFiYmIkSfPmzdOkSZO0b98+hYWFnbRNeXm5ysvLrXmv16uEhAR5PB65XK7aPzEAAFDrvF6v3G73aa/fATVmx+PxSJKioqJ8lr/++utq3LixOnTooMzMTP3www/WutzcXHXs2NEKOpKUlpYmr9erLVu2VHucrKwsud1ua0pISDgHZwMAAAJBwHwRaGVlpe677z796le/UocOHazlw4cPV1JSkuLj47Vx40ZNmjRJ+fn5WrJkiSSpqKjIJ+hIsuaLioqqPVZmZqYmTpxozVf17AAAAPsJmLCTkZGhzZs364MPPvBZPm7cOOvnjh07Ki4uTr1799aOHTvUsmXLGh3L6XTK6XSeVb0AAKBuCIjbWOPHj9eyZcv0/vvvq2nTpj/bNiUlRZK0fft2SVJsbKyKi4t92lTNx8bGnoNqAQBAXeLXsGOM0fjx4/XWW29p7dq1at68+Wm3ycvLkyTFxcVJklJTU7Vp0yaVlJRYbVatWiWXy6Xk5ORzUjcAAKg7/HobKyMjQ9nZ2Xr77bcVGRlpjbFxu92KiIjQjh07lJ2drf79+6tRo0bauHGjJkyYoO7du6tTp06SpL59+yo5OVkjR47UtGnTVFRUpMmTJysjI4NbVQAAwL+PnjscjmqXL1iwQKNGjdKePXt0yy23aPPmzSorK1NCQoJ+/etfa/LkyT6PmO3evVt33XWXcnJyVL9+faWnp2vq1KkKCTmzLHemj64BAIDAcabX74B6z46/EHYAAKh76uR7dgAAAGobYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANiaX8NOVlaWunbtqsjISEVHR2vw4MHKz8/3aXP48GFlZGSoUaNGatCggYYMGaLi4mKfNgUFBRowYIDq1aun6OhoPfDAAzp27Nj5PBUAABCg/Bp21q1bp4yMDH300UdatWqVjh49qr59+6qsrMxqM2HCBL3zzjtavHix1q1bp8LCQt1www3W+oqKCg0YMEBHjhzRhx9+qJdfflkLFy7UI4884o9TAgAAAcZhjDH+LqLKvn37FB0drXXr1ql79+7yeDxq0qSJsrOzNXToUEnS1q1b1a5dO+Xm5uqKK67Q8uXLNXDgQBUWFiomJkaSNG/ePE2aNEn79u1TWFjYaY/r9Xrldrvl8XjkcrnO6TkCAIDacabX74Aas+PxeCRJUVFRkqQNGzbo6NGj6tOnj9Wmbdu2SkxMVG5uriQpNzdXHTt2tIKOJKWlpcnr9WrLli3VHqe8vFxer9dnAgAA9hQwYaeyslL33XeffvWrX6lDhw6SpKKiIoWFhalhw4Y+bWNiYlRUVGS1OT7oVK2vWledrKwsud1ua0pISKjlswEAAIEiYMJORkaGNm/erEWLFp3zY2VmZsrj8VjTnj17zvkxAQCAf4T4uwBJGj9+vJYtW6b169eradOm1vLY2FgdOXJEBw4c8OndKS4uVmxsrNXmk08+8dlf1dNaVW1O5HQ65XQ6a/ksAABAIPJrz44xRuPHj9dbb72ltWvXqnnz5j7ru3TpotDQUK1Zs8Zalp+fr4KCAqWmpkqSUlNTtWnTJpWUlFhtVq1aJZfLpeTk5PNzIgAAIGD5tWcnIyND2dnZevvttxUZGWmNsXG73YqIiJDb7daYMWM0ceJERUVFyeVy6Z577lFqaqquuOIKSVLfvn2VnJyskSNHatq0aSoqKtLkyZOVkZFB7w0AADi7R8+3b9+uHTt2qHv37oqIiJAxRg6H48wPfoq2CxYs0KhRoyT99FLB+++/X3/9619VXl6utLQ0zZkzx+cW1e7du3XXXXcpJydH9evXV3p6uqZOnaqQkDPLcjx6DgBA3XOm1+8ahZ39+/frpptu0tq1a+VwOLRt2za1aNFCo0eP1kUXXaQ//elPZ1X8+UbYAQCg7jmn79mZMGGCQkJCVFBQoHr16lnLb7rpJq1YsaImuwQAADgnajRm57333tPKlSt9npySpNatW2v37t21UhgAAEBtqFHPTllZmU+PTpXS0lIGBQMAgIBSo7Bz1VVX6ZVXXrHmHQ6HKisrNW3aNPXs2bPWigMAADhbNbqNNW3aNPXu3VufffaZjhw5ogcffFBbtmxRaWmp/vWvf9V2jQAAADVWo56dDh066Ouvv1a3bt10/fXXq6ysTDfccIM+//xztWzZsrZrBAAAqLGzes+OXfDoOWBPH3zwgSZPnmzNP/nkk+rWrZsfKwJQm87pe3Y2btxY/c4cDoWHhysxMbFODVQm7AD206NHj1Ouy8nJOW91ADh3zvT6XaMxO507d7beflyVlY5/G3JoaKhuuukmvfDCCwoPD6/JIQCgxk4MOp07d1ZeXp7PegIPcOGo0Zidt956S61bt9b8+fP1xRdf6IsvvtD8+fPVpk0bZWdn68UXX9TatWt9uo8B4Hz44IMPrJ+nT5+unJwczZw5Uzk5OZo+fXq17QDYW41uY11++eV64oknlJaW5rN85cqVevjhh/XJJ59o6dKluv/++7Vjx45aK/Zc4TYWYB/H9+pU13tzuvUA6o5z+nURmzZtUlJS0knLk5KStGnTJkk/dRvv3bu3JrsHgLPWuXPnape3b9/+/BYCwO9qFHbatm2rqVOn6siRI9ayo0ePaurUqWrbtq0k6bvvvlNMTEztVAkAv9DxY3SOt2XLlvNbCAC/q1HYmT17tpYtW6amTZuqT58+6tOnj5o2baply5Zp7ty5kqSdO3fq7rvvrtViAeB0nnzySevnzz77zGfd8fPHtwNgbzV+z87Bgwf1+uuv6+uvv5YktWnTRsOHD1dkZGStFng+MGYHsJcTn8Zq3779ST06jNcB6r5z+p6dKl9++aUKCgp8bmdJ0nXXXVfTXfoFYQewH96zA9jfOX3Pzs6dO/XrX/9amzZtksPhkDHG5z07FRUVNdktANSanJwc3qAMQFINw87//M//qHnz5lqzZo2aN2+ujz/+WKWlpbr//vt93mMBAP7UrVs3enEA1Czs5Obmau3atWrcuLGCgoIUHBysbt26KSsrS/fee68+//zz2q4TAACgRmr0NFZFRYU1ELlx48YqLCyU9NN7dvLz82uvOgAAgLNUo56dDh066IsvvlDz5s2VkpKiadOmKSwsTPPnz1eLFi1qu0YAAIAaq1HYmTx5ssrKyiRJU6ZM0cCBA3XVVVepUaNG+tvf/larBQIAAJyNs3r0/HilpaW66KKLfJ7Kqit49BwAgLrnnD56Xp2oqKja2hUAAECtqdEAZQAAgLqCsAMAAGyNsAMAAGyNsAMAAGyNsAMAAGyNsAMAAGyNsAMAAGyNsAMAAGyNsAMAAGyNsAMAAGyNsAMAAGzNr2Fn/fr1GjRokOLj4+VwOLR06VKf9aNGjZLD4fCZrr32Wp82paWlGjFihFwulxo2bKgxY8bo0KFD5/EsAABAIPNr2CkrK9Mll1yi2bNnn7LNtddeq71791rTX//6V5/1I0aM0JYtW7Rq1SotW7ZM69ev17hx48516QAAoI6otW89r4l+/fqpX79+P9vG6XQqNja22nVfffWVVqxYoU8//VSXXXaZJOn5559X//79NX36dMXHx9d6zQAAoG4J+DE7OTk5io6OVps2bXTXXXdp//791rrc3Fw1bNjQCjqS1KdPHwUFBenjjz8+5T7Ly8vl9Xp9JgAAYE8BHXauvfZavfLKK1qzZo2eeuoprVu3Tv369VNFRYUkqaioSNHR0T7bhISEKCoqSkVFRafcb1ZWltxutzUlJCSc0/MAAAD+49fbWKczbNgw6+eOHTuqU6dOatmypXJyctS7d+8a7zczM1MTJ0605r1eL4EHAACbCuienRO1aNFCjRs31vbt2yVJsbGxKikp8Wlz7NgxlZaWnnKcj/TTOCCXy+UzAQAAe6pTYefbb7/V/v37FRcXJ0lKTU3VgQMHtGHDBqvN2rVrVVlZqZSUFH+VCQAAAohfb2MdOnTI6qWRpF27dikvL09RUVGKiorS448/riFDhig2NlY7duzQgw8+qFatWiktLU2S1K5dO1177bUaO3as5s2bp6NHj2r8+PEaNmwYT2IBAABJksMYY/x18JycHPXs2fOk5enp6Zo7d64GDx6szz//XAcOHFB8fLz69u2rJ554QjExMVbb0tJSjR8/Xu+8846CgoI0ZMgQPffcc2rQoMEZ1+H1euV2u+XxeLilBQBAHXGm12+/hp1AQdgBAKDuOdPrd50aswMAAPBLEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICthfi7AMBuDh8+rIKCAn+XAQSsxMREhYeH+7sMXEAIO0AtKygo0Lhx4/xdBhCw5s+fr4svvtjfZeACQtgBalliYqLmz5/v7zLwf3bv3q0//OEPeuihh5SUlOTvcqCf/o4A5xNhB6hl4eHh/Ks1ACUlJfF7AS5QDFAGAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC25tews379eg0aNEjx8fFyOBxaunSpz3pjjB555BHFxcUpIiJCffr00bZt23zalJaWasSIEXK5XGrYsKHGjBmjQ4cOncezAAAAgcyvYaesrEyXXHKJZs+eXe36adOm6bnnntO8efP08ccfq379+kpLS9Phw4etNiNGjNCWLVu0atUqLVu2TOvXr9e4cePO1ykAAIAAF+LPg/fr10/9+vWrdp0xRjNnztTkyZN1/fXXS5JeeeUVxcTEaOnSpRo2bJi++uorrVixQp9++qkuu+wySdLzzz+v/v37a/r06YqPjz9v5wIAAAJTwI7Z2bVrl4qKitSnTx9rmdvtVkpKinJzcyVJubm5atiwoRV0JKlPnz4KCgrSxx9/fMp9l5eXy+v1+kwAAMCeAjbsFBUVSZJiYmJ8lsfExFjrioqKFB0d7bM+JCREUVFRVpvqZGVlye12W1NCQkItVw8AAAJFwIadcykzM1Mej8ea9uzZ4++SAADAORKwYSc2NlaSVFxc7LO8uLjYWhcbG6uSkhKf9ceOHVNpaanVpjpOp1Mul8tnAgAA9hSwYad58+aKjY3VmjVrrGVer1cff/yxUlNTJUmpqak6cOCANmzYYLVZu3atKisrlZKSct5rBgAAgcevT2MdOnRI27dvt+Z37dqlvLw8RUVFKTExUffdd5+efPJJtW7dWs2bN9fDDz+s+Ph4DR48WJLUrl07XXvttRo7dqzmzZuno0ePavz48Ro2bBhPYgEAAEl+DjufffaZevbsac1PnDhRkpSenq6FCxfqwQcfVFlZmcaNG6cDBw6oW7duWrFihcLDw61tXn/9dY0fP169e/dWUFCQhgwZoueee+68nwsAAAhMDmOM8XcR/ub1euV2u+XxeBi/A9jM119/rXHjxmn+/Pm6+OKL/V0OgFp0ptfvgB2zAwAAUBsIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNZC/F0Aak9xcbE8Ho+/ywACyu7du33+C+AnbrdbMTEx/i7jvHAYY4y/i/A3r9crt9stj8cjl8vl73JqpLi4WLeMvFVHj5T7uxQAQB0QGubUa6++UqcDz5levwO6Z+exxx7T448/7rOsTZs22rp1qyTp8OHDuv/++7Vo0SKVl5crLS1Nc+bMqdO/uJryeDw6eqRcP7a4WpXhbn+XAwAIYEGHPdLOdfJ4PBfENTOgw44ktW/fXqtXr7bmQ0L+f8kTJkzQu+++q8WLF8vtdmv8+PG64YYb9K9//csfpQaEynC3Kus39ncZAAAEjIAPOyEhIYqNjT1pucfj0Ysvvqjs7Gz16tVLkrRgwQK1a9dOH330ka644orzXSoAAAhAAf801rZt2xQfH68WLVpoxIgRKigokCRt2LBBR48eVZ8+fay2bdu2VWJionJzc392n+Xl5fJ6vT4TAACwp4AOOykpKVq4cKFWrFihuXPnateuXbrqqqt08OBBFRUVKSwsTA0bNvTZJiYmRkVFRT+736ysLLndbmtKSEg4h2cBAAD8KaBvY/Xr18/6uVOnTkpJSVFSUpL+93//VxERETXeb2ZmpiZOnGjNe71eAg8AADYV0D07J2rYsKEuvvhibd++XbGxsTpy5IgOHDjg06a4uLjaMT7HczqdcrlcPhMAALCnOhV2Dh06pB07diguLk5dunRRaGio1qxZY63Pz89XQUGBUlNT/VglAAAIJAF9G+u3v/2tBg0apKSkJBUWFurRRx9VcHCwbr75Zrndbo0ZM0YTJ05UVFSUXC6X7rnnHqWmpvIkFgAAsAR02Pn222918803a//+/WrSpIm6deumjz76SE2aNJEkzZgxQ0FBQRoyZIjPSwUBAACqBHTYWbRo0c+uDw8P1+zZszV79uzzVBEAAKhr6tSYHQAAgF+KsAMAAGyNsAMAAGyNsAMAAGyNsAMAAGyNsAMAAGyNsAMAAGyNsAMAAGyNsAMAAGyNsAMAAGyNsAMAAGyNsAMAAGwtoL8IFL9c0I8H/F0CACDAXWjXCsKOzUTsWu/vEgAACCiEHZv5sXl3VUY09HcZAIAAFvTjgQvqH8eEHZupjGioyvqN/V0GAAABgwHKAADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1kL8XQBqV9Bhj79LAAAEuAvtWkHYsQm3263QMKe0c52/SwEA1AGhYU653W5/l3FeEHZsIiYmRq+9+oo8ngsrrQOns3v3bv3hD3/QQw89pKSkJH+XAwQMt9utmJgYf5dxXhB2bCQmJuaC+YML/FJJSUm6+OKL/V0GAD+wzQDl2bNnq1mzZgoPD1dKSoo++eQTf5cEAAACgC16dv72t79p4sSJmjdvnlJSUjRz5kylpaUpPz9f0dHR/i4PF5jDhw+roKDA32Xg/+zevdvnv/C/xMREhYeH+7sMXEAcxhjj7yLOVkpKirp27apZs2ZJkiorK5WQkKB77rlHv/vd705qX15ervLycmve6/UqISFBHo9HLpfrvNUNe/r66681btw4f5cBBKz58+dzSxG1wuv1yu12n/b6Xed7do4cOaINGzYoMzPTWhYUFKQ+ffooNze32m2ysrL0+OOPn68ScYFJTEzU/Pnz/V0GELASExP9XQIuMHU+7Hz//feqqKg4aWBuTEyMtm7dWu02mZmZmjhxojVf1bMD1Ibw8HD+1QoAAaTOh52acDqdcjqd/i4DAACcB3X+aazGjRsrODhYxcXFPsuLi4sVGxvrp6oAAECgqPNhJywsTF26dNGaNWusZZWVlVqzZo1SU1P9WBkAAAgEtriNNXHiRKWnp+uyyy7T5ZdfrpkzZ6qsrEy33Xabv0sDAAB+Zouwc9NNN2nfvn165JFHVFRUpM6dO2vFihW8TRgAANjjPTtn60yf0wcAAIHjTK/fdX7MDgAAwM8h7AAAAFsj7AAAAFsj7AAAAFsj7AAAAFsj7AAAAFsj7AAAAFuzxUsFz1bVq4a8Xq+fKwEAAGeq6rp9ulcGEnYkHTx4UJKUkJDg50oAAMAvdfDgQbnd7lOu5w3K+umLQwsLCxUZGSmHw+HvcgDUIq/Xq4SEBO3Zs4c3pAM2Y4zRwYMHFR8fr6CgU4/MIewAsDW+DgYAA5QBAICtEXYAAICtEXYA2JrT6dSjjz4qp9Pp71IA+AljdgAAgK3RswMAAGyNsAMAAGyNsAMAAGyNsAMAAGyNsAMAAGyNsAMAAGyNsAMAAGyNsAMAAGzt/wFj1MiL5q07swAAAABJRU5ErkJggg==\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}]},{"cell_type":"code","source":["#gerer les outliers sur totals rooms\n","def replace_outliers(data, n_std=3):\n","    for column in data.columns:\n","        if data[column].dtype != 'object':  # Ignorer les colonnes catégorielles\n","            mean, std = data[column].mean(), data[column].std()\n","            cutoff = std * n_std\n","            lower, upper = mean - cutoff, mean + cutoff\n","            data[column] = np.where(data[column] < lower, lower, data[column])  # Remplacer par lower si en dessous du seuil\n","            data[column] = np.where(data[column] > upper, upper, data[column])  # Remplacer par upper si au-dessus du seuil\n","    return data\n","\n","# Appliquer la fonction pour remplacer les outliers\n","data = replace_outliers(data)"],"metadata":{"id":"S0NOW03g1ERz","executionInfo":{"status":"ok","timestamp":1718906803257,"user_tz":-60,"elapsed":588,"user":{"displayName":"Oceane Hadama","userId":"07293307270246501044"}}},"execution_count":13,"outputs":[]},{"cell_type":"code","source":["# Création d'un grand canvas\n","plt.figure(figsize=(12, 10))\n","\n","# Tracer un boxplot pour chaque colonne\n","for i, col in enumerate(data.columns):\n","    plt.subplot(5, 6, i + 1)  # Adjust the grid size according to your number of columns\n","    sns.boxplot(y=data[col])\n","    plt.title(col)\n","\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":229},"id":"_XkNP7zA1Ij5","executionInfo":{"status":"ok","timestamp":1718906821964,"user_tz":-60,"elapsed":2054,"user":{"displayName":"Oceane Hadama","userId":"07293307270246501044"}},"outputId":"b8cf091a-101e-47aa-9a3e-2a10d3952d97"},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x1000 with 9 Axes>"],"image/png":"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init setup with normalize = True\n","from pycaret.regression import *\n","s = setup(data, target = 'strength', session_id = 123,\n","          normalize = True, normalize_method = 'minmax')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":707},"id":"FMiDEFjV1OF3","executionInfo":{"status":"ok","timestamp":1718906883086,"user_tz":-60,"elapsed":5662,"user":{"displayName":"Oceane Hadama","userId":"07293307270246501044"}},"outputId":"0a714b66-e92c-458f-a7b2-831732410e69"},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":["<pandas.io.formats.style.Styler at 0x7d6c391a43d0>"],"text/html":["<style type=\"text/css\">\n","#T_d8daa_row8_col1, #T_d8daa_row12_col1 {\n","  background-color: lightgreen;\n","}\n","</style>\n","<table id=\"T_d8daa\" class=\"dataframe\">\n","  <thead>\n","    <tr>\n","      <th class=\"blank level0\" >&nbsp;</th>\n","      <th id=\"T_d8daa_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n","      <th id=\"T_d8daa_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th id=\"T_d8daa_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n","      <td id=\"T_d8daa_row0_col0\" class=\"data row0 col0\" >Session id</td>\n","      <td id=\"T_d8daa_row0_col1\" class=\"data row0 col1\" >123</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_d8daa_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n","      <td id=\"T_d8daa_row1_col0\" class=\"data row1 col0\" >Target</td>\n","      <td id=\"T_d8daa_row1_col1\" class=\"data row1 col1\" >strength</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_d8daa_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n","      <td id=\"T_d8daa_row2_col0\" class=\"data row2 col0\" >Target type</td>\n","      <td id=\"T_d8daa_row2_col1\" class=\"data row2 col1\" >Regression</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_d8daa_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n","      <td id=\"T_d8daa_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n","      <td id=\"T_d8daa_row3_col1\" class=\"data row3 col1\" >(1030, 9)</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_d8daa_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n","      <td id=\"T_d8daa_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n","      <td id=\"T_d8daa_row4_col1\" class=\"data row4 col1\" >(1030, 9)</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_d8daa_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n","      <td id=\"T_d8daa_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n","      <td id=\"T_d8daa_row5_col1\" class=\"data row5 col1\" >(721, 9)</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_d8daa_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n","      <td id=\"T_d8daa_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n","      <td id=\"T_d8daa_row6_col1\" class=\"data row6 col1\" >(309, 9)</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_d8daa_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n","      <td id=\"T_d8daa_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n","      <td id=\"T_d8daa_row7_col1\" class=\"data row7 col1\" >8</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_d8daa_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n","      <td id=\"T_d8daa_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n","      <td id=\"T_d8daa_row8_col1\" class=\"data row8 col1\" >True</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_d8daa_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n","      <td id=\"T_d8daa_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n","      <td id=\"T_d8daa_row9_col1\" class=\"data row9 col1\" >simple</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_d8daa_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n","      <td id=\"T_d8daa_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n","      <td id=\"T_d8daa_row10_col1\" class=\"data row10 col1\" >mean</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_d8daa_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n","      <td id=\"T_d8daa_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n","      <td id=\"T_d8daa_row11_col1\" class=\"data row11 col1\" >mode</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_d8daa_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n","      <td id=\"T_d8daa_row12_col0\" class=\"data row12 col0\" >Normalize</td>\n","      <td id=\"T_d8daa_row12_col1\" class=\"data row12 col1\" >True</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_d8daa_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n","      <td id=\"T_d8daa_row13_col0\" class=\"data row13 col0\" >Normalize method</td>\n","      <td id=\"T_d8daa_row13_col1\" class=\"data row13 col1\" >minmax</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_d8daa_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n","      <td id=\"T_d8daa_row14_col0\" class=\"data row14 col0\" >Fold Generator</td>\n","      <td id=\"T_d8daa_row14_col1\" class=\"data row14 col1\" >KFold</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_d8daa_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n","      <td id=\"T_d8daa_row15_col0\" class=\"data row15 col0\" >Fold Number</td>\n","      <td id=\"T_d8daa_row15_col1\" class=\"data row15 col1\" >10</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_d8daa_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n","      <td id=\"T_d8daa_row16_col0\" class=\"data row16 col0\" >CPU Jobs</td>\n","      <td id=\"T_d8daa_row16_col1\" class=\"data row16 col1\" >-1</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_d8daa_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n","      <td id=\"T_d8daa_row17_col0\" class=\"data row17 col0\" >Use GPU</td>\n","      <td id=\"T_d8daa_row17_col1\" class=\"data row17 col1\" >False</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_d8daa_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n","      <td id=\"T_d8daa_row18_col0\" class=\"data row18 col0\" >Log Experiment</td>\n","      <td id=\"T_d8daa_row18_col1\" class=\"data row18 col1\" >False</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_d8daa_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n","      <td id=\"T_d8daa_row19_col0\" class=\"data row19 col0\" >Experiment Name</td>\n","      <td id=\"T_d8daa_row19_col1\" class=\"data row19 col1\" >reg-default-name</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_d8daa_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n","      <td id=\"T_d8daa_row20_col0\" class=\"data row20 col0\" >USI</td>\n","      <td id=\"T_d8daa_row20_col1\" class=\"data row20 col1\" >42a7</td>\n","    </tr>\n","  </tbody>\n","</table>\n"]},"metadata":{}}]},{"cell_type":"code","source":["compare_models()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["ca56e63e6d474e30b681e1aeb00b9db5","63ef6bab3f8b492ead4bba8882c93ab2","81f1a753d2714d5bb68a3dc4e98c11b5","0787724270d94d01ad2582da1aca75e2","d54a4dfe19d34008a857ee692f86848e","584ba20bc073454eaeb1d0d14e4c23c4","cd3a2b5f49374b70828c1728dc6ea275","0ec0c98c64504e678eeec8e09507d44e","e10d9f8aada8412097da9d4c0b667482","e3845a19d2e542078f56751c99b050f7","f60f7cebfad44af4ab6c2b53e130c33d"]},"id":"pQ18xOWy1aP_","executionInfo":{"status":"ok","timestamp":1718906932378,"user_tz":-60,"elapsed":32999,"user":{"displayName":"Oceane Hadama","userId":"07293307270246501044"}},"outputId":"1a81fb50-1f57-42a2-8b9f-afb2c9752999"},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML 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class=\"col_heading level0 col2\" >MSE</th>\n","      <th id=\"T_030be_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n","      <th id=\"T_030be_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n","      <th id=\"T_030be_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n","      <th id=\"T_030be_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n","      <th id=\"T_030be_level0_col7\" class=\"col_heading level0 col7\" >TT (Sec)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th id=\"T_030be_level0_row0\" class=\"row_heading level0 row0\" >lightgbm</th>\n","      <td id=\"T_030be_row0_col0\" class=\"data row0 col0\" >Light Gradient Boosting Machine</td>\n","      <td id=\"T_030be_row0_col1\" class=\"data row0 col1\" >3.1994</td>\n","      <td id=\"T_030be_row0_col2\" class=\"data row0 col2\" >21.9369</td>\n","      <td id=\"T_030be_row0_col3\" class=\"data row0 col3\" >4.6339</td>\n","      <td id=\"T_030be_row0_col4\" class=\"data row0 col4\" >0.9216</td>\n","      <td id=\"T_030be_row0_col5\" class=\"data row0 col5\" >0.1511</td>\n","      <td id=\"T_030be_row0_col6\" class=\"data row0 col6\" >0.1106</td>\n","      <td id=\"T_030be_row0_col7\" class=\"data row0 col7\" >0.1490</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_030be_level0_row1\" class=\"row_heading level0 row1\" >et</th>\n","      <td id=\"T_030be_row1_col0\" class=\"data row1 col0\" >Extra Trees Regressor</td>\n","      <td id=\"T_030be_row1_col1\" class=\"data row1 col1\" >3.2194</td>\n","      <td id=\"T_030be_row1_col2\" class=\"data row1 col2\" >23.3481</td>\n","      <td id=\"T_030be_row1_col3\" class=\"data row1 col3\" >4.7890</td>\n","      <td id=\"T_030be_row1_col4\" class=\"data row1 col4\" >0.9170</td>\n","      <td id=\"T_030be_row1_col5\" class=\"data row1 col5\" >0.1511</td>\n","      <td id=\"T_030be_row1_col6\" class=\"data row1 col6\" >0.1111</td>\n","      <td id=\"T_030be_row1_col7\" class=\"data row1 col7\" >0.2370</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_030be_level0_row2\" class=\"row_heading level0 row2\" >xgboost</th>\n","      <td id=\"T_030be_row2_col0\" class=\"data row2 col0\" >Extreme Gradient Boosting</td>\n","      <td id=\"T_030be_row2_col1\" class=\"data row2 col1\" >3.2475</td>\n","      <td id=\"T_030be_row2_col2\" class=\"data row2 col2\" >23.8631</td>\n","      <td id=\"T_030be_row2_col3\" class=\"data row2 col3\" >4.8068</td>\n","      <td id=\"T_030be_row2_col4\" class=\"data row2 col4\" >0.9148</td>\n","      <td id=\"T_030be_row2_col5\" class=\"data row2 col5\" >0.1540</td>\n","      <td id=\"T_030be_row2_col6\" class=\"data row2 col6\" >0.1111</td>\n","      <td id=\"T_030be_row2_col7\" class=\"data row2 col7\" >0.2340</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_030be_level0_row3\" class=\"row_heading level0 row3\" >gbr</th>\n","      <td id=\"T_030be_row3_col0\" class=\"data row3 col0\" >Gradient Boosting Regressor</td>\n","      <td id=\"T_030be_row3_col1\" class=\"data row3 col1\" >3.7623</td>\n","      <td id=\"T_030be_row3_col2\" class=\"data row3 col2\" >27.6371</td>\n","      <td id=\"T_030be_row3_col3\" class=\"data row3 col3\" >5.2351</td>\n","      <td id=\"T_030be_row3_col4\" class=\"data row3 col4\" >0.9008</td>\n","      <td id=\"T_030be_row3_col5\" class=\"data row3 col5\" >0.1633</td>\n","      <td id=\"T_030be_row3_col6\" class=\"data row3 col6\" >0.1293</td>\n","      <td id=\"T_030be_row3_col7\" class=\"data row3 col7\" >0.2560</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_030be_level0_row4\" class=\"row_heading level0 row4\" >rf</th>\n","      <td id=\"T_030be_row4_col0\" class=\"data row4 col0\" >Random Forest Regressor</td>\n","      <td id=\"T_030be_row4_col1\" class=\"data row4 col1\" >3.6433</td>\n","      <td id=\"T_030be_row4_col2\" class=\"data row4 col2\" >28.0138</td>\n","      <td id=\"T_030be_row4_col3\" class=\"data row4 col3\" >5.2188</td>\n","      <td id=\"T_030be_row4_col4\" class=\"data row4 col4\" >0.9000</td>\n","      <td id=\"T_030be_row4_col5\" class=\"data row4 col5\" >0.1677</td>\n","      <td id=\"T_030be_row4_col6\" class=\"data row4 col6\" >0.1285</td>\n","      <td id=\"T_030be_row4_col7\" class=\"data row4 col7\" >0.3680</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_030be_level0_row5\" class=\"row_heading level0 row5\" >dt</th>\n","      <td id=\"T_030be_row5_col0\" class=\"data row5 col0\" >Decision Tree Regressor</td>\n","      <td id=\"T_030be_row5_col1\" class=\"data row5 col1\" >4.5767</td>\n","      <td id=\"T_030be_row5_col2\" class=\"data row5 col2\" >45.3693</td>\n","      <td id=\"T_030be_row5_col3\" class=\"data row5 col3\" >6.6261</td>\n","      <td id=\"T_030be_row5_col4\" class=\"data row5 col4\" >0.8389</td>\n","      <td id=\"T_030be_row5_col5\" class=\"data row5 col5\" >0.2180</td>\n","      <td id=\"T_030be_row5_col6\" class=\"data row5 col6\" >0.1586</td>\n","      <td id=\"T_030be_row5_col7\" class=\"data row5 col7\" >0.0300</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_030be_level0_row6\" class=\"row_heading level0 row6\" >ada</th>\n","      <td id=\"T_030be_row6_col0\" class=\"data row6 col0\" >AdaBoost Regressor</td>\n","      <td id=\"T_030be_row6_col1\" class=\"data row6 col1\" >6.2763</td>\n","      <td id=\"T_030be_row6_col2\" class=\"data row6 col2\" >58.0741</td>\n","      <td id=\"T_030be_row6_col3\" class=\"data row6 col3\" >7.5860</td>\n","      <td id=\"T_030be_row6_col4\" class=\"data row6 col4\" >0.7917</td>\n","      <td id=\"T_030be_row6_col5\" class=\"data row6 col5\" >0.2809</td>\n","      <td id=\"T_030be_row6_col6\" class=\"data row6 col6\" >0.2645</td>\n","      <td id=\"T_030be_row6_col7\" class=\"data row6 col7\" >0.1870</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_030be_level0_row7\" class=\"row_heading level0 row7\" >knn</th>\n","      <td id=\"T_030be_row7_col0\" class=\"data row7 col0\" >K Neighbors Regressor</td>\n","      <td id=\"T_030be_row7_col1\" class=\"data row7 col1\" >7.0020</td>\n","      <td id=\"T_030be_row7_col2\" class=\"data row7 col2\" >84.1723</td>\n","      <td id=\"T_030be_row7_col3\" class=\"data row7 col3\" >9.1279</td>\n","      <td id=\"T_030be_row7_col4\" class=\"data row7 col4\" >0.7002</td>\n","      <td id=\"T_030be_row7_col5\" class=\"data row7 col5\" >0.3021</td>\n","      <td id=\"T_030be_row7_col6\" class=\"data row7 col6\" >0.2691</td>\n","      <td id=\"T_030be_row7_col7\" class=\"data row7 col7\" >0.0390</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_030be_level0_row8\" class=\"row_heading level0 row8\" >br</th>\n","      <td id=\"T_030be_row8_col0\" class=\"data row8 col0\" >Bayesian Ridge</td>\n","      <td id=\"T_030be_row8_col1\" class=\"data row8 col1\" >7.8270</td>\n","      <td id=\"T_030be_row8_col2\" class=\"data row8 col2\" >99.7922</td>\n","      <td id=\"T_030be_row8_col3\" class=\"data row8 col3\" >9.9661</td>\n","      <td id=\"T_030be_row8_col4\" class=\"data row8 col4\" >0.6383</td>\n","      <td id=\"T_030be_row8_col5\" class=\"data row8 col5\" >0.3194</td>\n","      <td id=\"T_030be_row8_col6\" class=\"data row8 col6\" >0.2960</td>\n","      <td id=\"T_030be_row8_col7\" class=\"data row8 col7\" >0.0340</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_030be_level0_row9\" class=\"row_heading level0 row9\" >lar</th>\n","      <td id=\"T_030be_row9_col0\" class=\"data row9 col0\" >Least Angle Regression</td>\n","      <td id=\"T_030be_row9_col1\" class=\"data row9 col1\" >7.8124</td>\n","      <td id=\"T_030be_row9_col2\" class=\"data row9 col2\" >99.8391</td>\n","      <td id=\"T_030be_row9_col3\" class=\"data row9 col3\" >9.9679</td>\n","      <td id=\"T_030be_row9_col4\" class=\"data row9 col4\" >0.6381</td>\n","      <td id=\"T_030be_row9_col5\" class=\"data row9 col5\" >0.3189</td>\n","      <td id=\"T_030be_row9_col6\" class=\"data row9 col6\" >0.2947</td>\n","      <td id=\"T_030be_row9_col7\" class=\"data row9 col7\" >0.0460</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_030be_level0_row10\" class=\"row_heading level0 row10\" >lr</th>\n","      <td id=\"T_030be_row10_col0\" class=\"data row10 col0\" >Linear Regression</td>\n","      <td id=\"T_030be_row10_col1\" class=\"data row10 col1\" >7.8124</td>\n","      <td id=\"T_030be_row10_col2\" class=\"data row10 col2\" >99.8391</td>\n","      <td id=\"T_030be_row10_col3\" class=\"data row10 col3\" >9.9679</td>\n","      <td id=\"T_030be_row10_col4\" class=\"data row10 col4\" >0.6381</td>\n","      <td id=\"T_030be_row10_col5\" class=\"data row10 col5\" >0.3189</td>\n","      <td id=\"T_030be_row10_col6\" class=\"data row10 col6\" >0.2947</td>\n","      <td id=\"T_030be_row10_col7\" class=\"data row10 col7\" >0.6890</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_030be_level0_row11\" class=\"row_heading level0 row11\" >ridge</th>\n","      <td id=\"T_030be_row11_col0\" class=\"data row11 col0\" >Ridge Regression</td>\n","      <td id=\"T_030be_row11_col1\" class=\"data row11 col1\" >7.8985</td>\n","      <td id=\"T_030be_row11_col2\" class=\"data row11 col2\" >100.2706</td>\n","      <td id=\"T_030be_row11_col3\" class=\"data row11 col3\" >9.9911</td>\n","      <td id=\"T_030be_row11_col4\" class=\"data row11 col4\" >0.6369</td>\n","      <td id=\"T_030be_row11_col5\" class=\"data row11 col5\" >0.3230</td>\n","      <td id=\"T_030be_row11_col6\" class=\"data row11 col6\" >0.3030</td>\n","      <td id=\"T_030be_row11_col7\" class=\"data row11 col7\" >0.0560</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_030be_level0_row12\" class=\"row_heading level0 row12\" >huber</th>\n","      <td id=\"T_030be_row12_col0\" class=\"data row12 col0\" >Huber Regressor</td>\n","      <td id=\"T_030be_row12_col1\" class=\"data row12 col1\" >7.7205</td>\n","      <td id=\"T_030be_row12_col2\" class=\"data row12 col2\" >100.9673</td>\n","      <td id=\"T_030be_row12_col3\" class=\"data row12 col3\" >10.0255</td>\n","      <td id=\"T_030be_row12_col4\" class=\"data row12 col4\" >0.6335</td>\n","      <td id=\"T_030be_row12_col5\" class=\"data row12 col5\" >0.3153</td>\n","      <td id=\"T_030be_row12_col6\" class=\"data row12 col6\" >0.2886</td>\n","      <td id=\"T_030be_row12_col7\" class=\"data row12 col7\" >0.0430</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_030be_level0_row13\" class=\"row_heading level0 row13\" >par</th>\n","      <td id=\"T_030be_row13_col0\" class=\"data row13 col0\" >Passive Aggressive Regressor</td>\n","      <td id=\"T_030be_row13_col1\" class=\"data row13 col1\" >8.8430</td>\n","      <td id=\"T_030be_row13_col2\" class=\"data row13 col2\" >125.0497</td>\n","      <td id=\"T_030be_row13_col3\" class=\"data row13 col3\" >11.0754</td>\n","      <td id=\"T_030be_row13_col4\" class=\"data row13 col4\" >0.5437</td>\n","      <td id=\"T_030be_row13_col5\" class=\"data row13 col5\" >0.3426</td>\n","      <td id=\"T_030be_row13_col6\" class=\"data row13 col6\" >0.3262</td>\n","      <td id=\"T_030be_row13_col7\" class=\"data row13 col7\" >0.0290</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_030be_level0_row14\" class=\"row_heading level0 row14\" >lasso</th>\n","      <td id=\"T_030be_row14_col0\" class=\"data row14 col0\" >Lasso Regression</td>\n","      <td id=\"T_030be_row14_col1\" class=\"data row14 col1\" >11.1533</td>\n","      <td id=\"T_030be_row14_col2\" class=\"data row14 col2\" >192.0434</td>\n","      <td id=\"T_030be_row14_col3\" class=\"data row14 col3\" >13.8082</td>\n","      <td id=\"T_030be_row14_col4\" class=\"data row14 col4\" >0.3168</td>\n","      <td id=\"T_030be_row14_col5\" class=\"data row14 col5\" >0.4595</td>\n","      <td id=\"T_030be_row14_col6\" class=\"data row14 col6\" >0.4897</td>\n","      <td id=\"T_030be_row14_col7\" class=\"data row14 col7\" >0.0430</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_030be_level0_row15\" class=\"row_heading level0 row15\" >llar</th>\n","      <td id=\"T_030be_row15_col0\" class=\"data row15 col0\" >Lasso Least Angle Regression</td>\n","      <td id=\"T_030be_row15_col1\" class=\"data row15 col1\" >11.1533</td>\n","      <td id=\"T_030be_row15_col2\" class=\"data row15 col2\" >192.0434</td>\n","      <td id=\"T_030be_row15_col3\" class=\"data row15 col3\" >13.8082</td>\n","      <td id=\"T_030be_row15_col4\" class=\"data row15 col4\" >0.3168</td>\n","      <td id=\"T_030be_row15_col5\" class=\"data row15 col5\" >0.4595</td>\n","      <td id=\"T_030be_row15_col6\" class=\"data row15 col6\" >0.4897</td>\n","      <td id=\"T_030be_row15_col7\" class=\"data row15 col7\" >0.0540</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_030be_level0_row16\" class=\"row_heading level0 row16\" >omp</th>\n","      <td id=\"T_030be_row16_col0\" class=\"data row16 col0\" >Orthogonal Matching Pursuit</td>\n","      <td id=\"T_030be_row16_col1\" class=\"data row16 col1\" >11.9815</td>\n","      <td id=\"T_030be_row16_col2\" class=\"data row16 col2\" >215.4168</td>\n","      <td id=\"T_030be_row16_col3\" class=\"data row16 col3\" >14.6284</td>\n","      <td id=\"T_030be_row16_col4\" class=\"data row16 col4\" >0.2293</td>\n","      <td id=\"T_030be_row16_col5\" class=\"data row16 col5\" >0.4756</td>\n","      <td id=\"T_030be_row16_col6\" class=\"data row16 col6\" >0.5073</td>\n","      <td id=\"T_030be_row16_col7\" class=\"data row16 col7\" >0.0500</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_030be_level0_row17\" class=\"row_heading level0 row17\" >en</th>\n","      <td id=\"T_030be_row17_col0\" class=\"data row17 col0\" >Elastic Net</td>\n","      <td id=\"T_030be_row17_col1\" class=\"data row17 col1\" >13.0977</td>\n","      <td id=\"T_030be_row17_col2\" class=\"data row17 col2\" >264.5286</td>\n","      <td id=\"T_030be_row17_col3\" class=\"data row17 col3\" >16.2152</td>\n","      <td id=\"T_030be_row17_col4\" class=\"data row17 col4\" >0.0583</td>\n","      <td id=\"T_030be_row17_col5\" class=\"data row17 col5\" >0.5243</td>\n","      <td id=\"T_030be_row17_col6\" class=\"data row17 col6\" >0.5861</td>\n","      <td id=\"T_030be_row17_col7\" class=\"data row17 col7\" >0.0460</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_030be_level0_row18\" class=\"row_heading level0 row18\" >dummy</th>\n","      <td id=\"T_030be_row18_col0\" class=\"data row18 col0\" >Dummy Regressor</td>\n","      <td id=\"T_030be_row18_col1\" class=\"data row18 col1\" >13.6313</td>\n","      <td id=\"T_030be_row18_col2\" class=\"data row18 col2\" >286.2956</td>\n","      <td id=\"T_030be_row18_col3\" class=\"data row18 col3\" >16.8710</td>\n","      <td id=\"T_030be_row18_col4\" class=\"data row18 col4\" >-0.0195</td>\n","      <td id=\"T_030be_row18_col5\" class=\"data row18 col5\" >0.5409</td>\n","      <td id=\"T_030be_row18_col6\" class=\"data row18 col6\" >0.6102</td>\n","      <td id=\"T_030be_row18_col7\" class=\"data row18 col7\" >0.0270</td>\n","    </tr>\n","  </tbody>\n","</table>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Processing:   0%|          | 0/81 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ca56e63e6d474e30b681e1aeb00b9db5"}},"metadata":{"application/vnd.jupyter.widget-view+json":{"colab":{"custom_widget_manager":{"url":"https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"}}}}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["LGBMRegressor(n_jobs=-1, random_state=123)"],"text/html":["<style>#sk-container-id-1 {\n","  /* Definition of color scheme common for light and dark mode */\n","  --sklearn-color-text: black;\n","  --sklearn-color-line: gray;\n","  /* Definition of color scheme for unfitted estimators */\n","  --sklearn-color-unfitted-level-0: #fff5e6;\n","  --sklearn-color-unfitted-level-1: #f6e4d2;\n","  --sklearn-color-unfitted-level-2: #ffe0b3;\n","  --sklearn-color-unfitted-level-3: chocolate;\n","  /* Definition of color scheme for fitted estimators */\n","  --sklearn-color-fitted-level-0: #f0f8ff;\n","  --sklearn-color-fitted-level-1: #d4ebff;\n","  --sklearn-color-fitted-level-2: #b3dbfd;\n","  --sklearn-color-fitted-level-3: cornflowerblue;\n","\n","  /* Specific color for light theme */\n","  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n","  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-icon: #696969;\n","\n","  @media (prefers-color-scheme: dark) {\n","    /* Redefinition of color scheme for dark theme */\n","    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n","    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-icon: #878787;\n","  }\n","}\n","\n","#sk-container-id-1 {\n","  color: var(--sklearn-color-text);\n","}\n","\n","#sk-container-id-1 pre {\n","  padding: 0;\n","}\n","\n","#sk-container-id-1 input.sk-hidden--visually {\n","  border: 0;\n","  clip: rect(1px 1px 1px 1px);\n","  clip: rect(1px, 1px, 1px, 1px);\n","  height: 1px;\n","  margin: -1px;\n","  overflow: hidden;\n","  padding: 0;\n","  position: absolute;\n","  width: 1px;\n","}\n","\n","#sk-container-id-1 div.sk-dashed-wrapped {\n","  border: 1px dashed var(--sklearn-color-line);\n","  margin: 0 0.4em 0.5em 0.4em;\n","  box-sizing: border-box;\n","  padding-bottom: 0.4em;\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","#sk-container-id-1 div.sk-container {\n","  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n","     but bootstrap.min.css set `[hidden] { display: none !important; }`\n","     so we also need the `!important` here to be able to override the\n","     default hidden behavior on the sphinx rendered scikit-learn.org.\n","     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n","  display: inline-block !important;\n","  position: relative;\n","}\n","\n","#sk-container-id-1 div.sk-text-repr-fallback {\n","  display: none;\n","}\n","\n","div.sk-parallel-item,\n","div.sk-serial,\n","div.sk-item {\n","  /* draw centered vertical line to link estimators */\n","  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n","  background-size: 2px 100%;\n","  background-repeat: no-repeat;\n","  background-position: center center;\n","}\n","\n","/* Parallel-specific style estimator block */\n","\n","#sk-container-id-1 div.sk-parallel-item::after {\n","  content: \"\";\n","  width: 100%;\n","  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n","  flex-grow: 1;\n","}\n","\n","#sk-container-id-1 div.sk-parallel {\n","  display: flex;\n","  align-items: stretch;\n","  justify-content: center;\n","  background-color: var(--sklearn-color-background);\n","  position: relative;\n","}\n","\n","#sk-container-id-1 div.sk-parallel-item {\n","  display: flex;\n","  flex-direction: column;\n","}\n","\n","#sk-container-id-1 div.sk-parallel-item:first-child::after {\n","  align-self: flex-end;\n","  width: 50%;\n","}\n","\n","#sk-container-id-1 div.sk-parallel-item:last-child::after {\n","  align-self: flex-start;\n","  width: 50%;\n","}\n","\n","#sk-container-id-1 div.sk-parallel-item:only-child::after {\n","  width: 0;\n","}\n","\n","/* Serial-specific style estimator block */\n","\n","#sk-container-id-1 div.sk-serial {\n","  display: flex;\n","  flex-direction: column;\n","  align-items: center;\n","  background-color: var(--sklearn-color-background);\n","  padding-right: 1em;\n","  padding-left: 1em;\n","}\n","\n","\n","/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n","clickable and can be expanded/collapsed.\n","- Pipeline and ColumnTransformer use this feature and define the default style\n","- Estimators will overwrite some part of the style using the `sk-estimator` class\n","*/\n","\n","/* Pipeline and ColumnTransformer style (default) */\n","\n","#sk-container-id-1 div.sk-toggleable {\n","  /* Default theme specific background. It is overwritten whether we have a\n","  specific estimator or a Pipeline/ColumnTransformer */\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","/* Toggleable label */\n","#sk-container-id-1 label.sk-toggleable__label {\n","  cursor: pointer;\n","  display: block;\n","  width: 100%;\n","  margin-bottom: 0;\n","  padding: 0.5em;\n","  box-sizing: border-box;\n","  text-align: center;\n","}\n","\n","#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n","  /* Arrow on the left of the label */\n","  content: \"▸\";\n","  float: left;\n","  margin-right: 0.25em;\n","  color: var(--sklearn-color-icon);\n","}\n","\n","#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n","  color: var(--sklearn-color-text);\n","}\n","\n","/* Toggleable content - dropdown */\n","\n","#sk-container-id-1 div.sk-toggleable__content {\n","  max-height: 0;\n","  max-width: 0;\n","  overflow: hidden;\n","  text-align: left;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-1 div.sk-toggleable__content.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-1 div.sk-toggleable__content pre {\n","  margin: 0.2em;\n","  border-radius: 0.25em;\n","  color: var(--sklearn-color-text);\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n","  /* Expand drop-down */\n","  max-height: 200px;\n","  max-width: 100%;\n","  overflow: auto;\n","}\n","\n","#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n","  content: \"▾\";\n","}\n","\n","/* Pipeline/ColumnTransformer-specific style */\n","\n","#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator-specific style */\n","\n","/* Colorize estimator box */\n","#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n","#sk-container-id-1 div.sk-label label {\n","  /* The background is the default theme color */\n","  color: var(--sklearn-color-text-on-default-background);\n","}\n","\n","/* On hover, darken the color of the background */\n","#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","/* Label box, darken color on hover, fitted */\n","#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator label */\n","\n","#sk-container-id-1 div.sk-label label {\n","  font-family: monospace;\n","  font-weight: bold;\n","  display: inline-block;\n","  line-height: 1.2em;\n","}\n","\n","#sk-container-id-1 div.sk-label-container {\n","  text-align: center;\n","}\n","\n","/* Estimator-specific */\n","#sk-container-id-1 div.sk-estimator {\n","  font-family: monospace;\n","  border: 1px dotted var(--sklearn-color-border-box);\n","  border-radius: 0.25em;\n","  box-sizing: border-box;\n","  margin-bottom: 0.5em;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-1 div.sk-estimator.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","/* on hover */\n","#sk-container-id-1 div.sk-estimator:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-1 div.sk-estimator.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Specification for estimator info (e.g. \"i\" and \"?\") */\n","\n","/* Common style for \"i\" and \"?\" */\n","\n",".sk-estimator-doc-link,\n","a:link.sk-estimator-doc-link,\n","a:visited.sk-estimator-doc-link {\n","  float: right;\n","  font-size: smaller;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1em;\n","  height: 1em;\n","  width: 1em;\n","  text-decoration: none !important;\n","  margin-left: 1ex;\n","  /* unfitted */\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-unfitted-level-1);\n","}\n","\n",".sk-estimator-doc-link.fitted,\n","a:link.sk-estimator-doc-link.fitted,\n","a:visited.sk-estimator-doc-link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","/* Span, style for the box shown on hovering the info icon */\n",".sk-estimator-doc-link span {\n","  display: none;\n","  z-index: 9999;\n","  position: relative;\n","  font-weight: normal;\n","  right: .2ex;\n","  padding: .5ex;\n","  margin: .5ex;\n","  width: min-content;\n","  min-width: 20ex;\n","  max-width: 50ex;\n","  color: var(--sklearn-color-text);\n","  box-shadow: 2pt 2pt 4pt #999;\n","  /* unfitted */\n","  background: var(--sklearn-color-unfitted-level-0);\n","  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n","}\n","\n",".sk-estimator-doc-link.fitted span {\n","  /* fitted */\n","  background: var(--sklearn-color-fitted-level-0);\n","  border: var(--sklearn-color-fitted-level-3);\n","}\n","\n",".sk-estimator-doc-link:hover span {\n","  display: block;\n","}\n","\n","/* \"?\"-specific style due to the `<a>` HTML tag */\n","\n","#sk-container-id-1 a.estimator_doc_link {\n","  float: right;\n","  font-size: 1rem;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1rem;\n","  height: 1rem;\n","  width: 1rem;\n","  text-decoration: none;\n","  /* unfitted */\n","  color: var(--sklearn-color-unfitted-level-1);\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","}\n","\n","#sk-container-id-1 a.estimator_doc_link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","#sk-container-id-1 a.estimator_doc_link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","}\n","</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMRegressor(n_jobs=-1, random_state=123)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LGBMRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LGBMRegressor(n_jobs=-1, random_state=123)</pre></div> </div></div></div></div>"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","source":["best_model=create_model(\"lightgbm\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":457,"referenced_widgets":["8a8391dd045d4444a769e1d9cff86612","9702e5c732e4461ab0d1cdfe7eb7003c","47efd5e7d1134f48ba8ac9324026bc5b","3fa243958ae64accafe5ecd04a1de711","06e4310f3406458e9cd1841b168532b4","7de7ad41b19549449a3ebc16cc716bb8","0643fbbc0c7149f8a49007edbf53e05f","6767b8870fc44c8090ec468e98380313","5699472a204241c1884673086b34f3e2","419a8ef58ca0452ea0442d0250fa1c2e","d7970322150f490ca2b8ce37f5c6ff57"]},"id":"nbGr3NJS1m0v","executionInfo":{"status":"ok","timestamp":1718906971698,"user_tz":-60,"elapsed":5895,"user":{"displayName":"Oceane Hadama","userId":"07293307270246501044"}},"outputId":"74af2622-9209-435b-a590-ed0158fa8672"},"execution_count":17,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<pandas.io.formats.style.Styler at 0x7d6c38ca1390>"],"text/html":["<style type=\"text/css\">\n","#T_1500e_row10_col0, #T_1500e_row10_col1, #T_1500e_row10_col2, #T_1500e_row10_col3, #T_1500e_row10_col4, #T_1500e_row10_col5 {\n","  background: yellow;\n","}\n","</style>\n","<table id=\"T_1500e\" class=\"dataframe\">\n","  <thead>\n","    <tr>\n","      <th class=\"blank level0\" >&nbsp;</th>\n","      <th id=\"T_1500e_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n","      <th id=\"T_1500e_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n","      <th id=\"T_1500e_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n","      <th id=\"T_1500e_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n","      <th id=\"T_1500e_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n","      <th id=\"T_1500e_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_1500e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n","      <td id=\"T_1500e_row0_col0\" class=\"data row0 col0\" >3.0762</td>\n","      <td id=\"T_1500e_row0_col1\" class=\"data row0 col1\" >24.1785</td>\n","      <td id=\"T_1500e_row0_col2\" class=\"data row0 col2\" >4.9172</td>\n","      <td id=\"T_1500e_row0_col3\" class=\"data row0 col3\" >0.9047</td>\n","      <td id=\"T_1500e_row0_col4\" class=\"data row0 col4\" >0.1341</td>\n","      <td id=\"T_1500e_row0_col5\" class=\"data row0 col5\" >0.0923</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_1500e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n","      <td id=\"T_1500e_row1_col0\" class=\"data row1 col0\" >3.0985</td>\n","      <td id=\"T_1500e_row1_col1\" class=\"data row1 col1\" >19.4929</td>\n","      <td id=\"T_1500e_row1_col2\" class=\"data row1 col2\" >4.4151</td>\n","      <td id=\"T_1500e_row1_col3\" class=\"data row1 col3\" >0.9426</td>\n","      <td id=\"T_1500e_row1_col4\" class=\"data row1 col4\" >0.1394</td>\n","      <td id=\"T_1500e_row1_col5\" class=\"data row1 col5\" >0.0980</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_1500e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n","      <td id=\"T_1500e_row2_col0\" class=\"data row2 col0\" >3.0367</td>\n","      <td id=\"T_1500e_row2_col1\" class=\"data row2 col1\" >20.5595</td>\n","      <td id=\"T_1500e_row2_col2\" class=\"data row2 col2\" >4.5343</td>\n","      <td id=\"T_1500e_row2_col3\" class=\"data row2 col3\" >0.9177</td>\n","      <td id=\"T_1500e_row2_col4\" class=\"data row2 col4\" >0.1384</td>\n","      <td id=\"T_1500e_row2_col5\" class=\"data row2 col5\" >0.1010</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_1500e_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n","      <td id=\"T_1500e_row3_col0\" class=\"data row3 col0\" >3.0059</td>\n","      <td id=\"T_1500e_row3_col1\" class=\"data row3 col1\" >19.2648</td>\n","      <td id=\"T_1500e_row3_col2\" class=\"data row3 col2\" >4.3892</td>\n","      <td id=\"T_1500e_row3_col3\" class=\"data row3 col3\" >0.9451</td>\n","      <td id=\"T_1500e_row3_col4\" class=\"data row3 col4\" >0.1821</td>\n","      <td id=\"T_1500e_row3_col5\" class=\"data row3 col5\" >0.1325</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_1500e_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n","      <td id=\"T_1500e_row4_col0\" class=\"data row4 col0\" >3.0278</td>\n","      <td id=\"T_1500e_row4_col1\" class=\"data row4 col1\" >22.1693</td>\n","      <td id=\"T_1500e_row4_col2\" class=\"data row4 col2\" >4.7084</td>\n","      <td id=\"T_1500e_row4_col3\" class=\"data row4 col3\" >0.9266</td>\n","      <td id=\"T_1500e_row4_col4\" class=\"data row4 col4\" >0.1465</td>\n","      <td id=\"T_1500e_row4_col5\" class=\"data row4 col5\" >0.1095</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_1500e_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n","      <td id=\"T_1500e_row5_col0\" class=\"data row5 col0\" >4.2383</td>\n","      <td id=\"T_1500e_row5_col1\" class=\"data row5 col1\" >40.3020</td>\n","      <td id=\"T_1500e_row5_col2\" class=\"data row5 col2\" >6.3484</td>\n","      <td id=\"T_1500e_row5_col3\" class=\"data row5 col3\" >0.8688</td>\n","      <td id=\"T_1500e_row5_col4\" class=\"data row5 col4\" >0.1872</td>\n","      <td id=\"T_1500e_row5_col5\" class=\"data row5 col5\" >0.1406</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_1500e_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n","      <td id=\"T_1500e_row6_col0\" class=\"data row6 col0\" >3.0922</td>\n","      <td id=\"T_1500e_row6_col1\" class=\"data row6 col1\" >19.2712</td>\n","      <td id=\"T_1500e_row6_col2\" class=\"data row6 col2\" >4.3899</td>\n","      <td id=\"T_1500e_row6_col3\" class=\"data row6 col3\" >0.9273</td>\n","      <td id=\"T_1500e_row6_col4\" class=\"data row6 col4\" >0.1448</td>\n","      <td id=\"T_1500e_row6_col5\" class=\"data row6 col5\" >0.1034</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_1500e_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n","      <td id=\"T_1500e_row7_col0\" class=\"data row7 col0\" >3.0078</td>\n","      <td id=\"T_1500e_row7_col1\" class=\"data row7 col1\" >17.8337</td>\n","      <td id=\"T_1500e_row7_col2\" class=\"data row7 col2\" >4.2230</td>\n","      <td id=\"T_1500e_row7_col3\" class=\"data row7 col3\" >0.9312</td>\n","      <td id=\"T_1500e_row7_col4\" class=\"data row7 col4\" >0.1660</td>\n","      <td id=\"T_1500e_row7_col5\" class=\"data row7 col5\" >0.1197</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_1500e_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n","      <td id=\"T_1500e_row8_col0\" class=\"data row8 col0\" >2.7311</td>\n","      <td id=\"T_1500e_row8_col1\" class=\"data row8 col1\" >12.4866</td>\n","      <td id=\"T_1500e_row8_col2\" class=\"data row8 col2\" >3.5336</td>\n","      <td id=\"T_1500e_row8_col3\" class=\"data row8 col3\" >0.9403</td>\n","      <td id=\"T_1500e_row8_col4\" class=\"data row8 col4\" >0.1139</td>\n","      <td id=\"T_1500e_row8_col5\" class=\"data row8 col5\" >0.0901</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_1500e_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n","      <td id=\"T_1500e_row9_col0\" class=\"data row9 col0\" >3.6796</td>\n","      <td id=\"T_1500e_row9_col1\" class=\"data row9 col1\" >23.8101</td>\n","      <td id=\"T_1500e_row9_col2\" class=\"data row9 col2\" >4.8796</td>\n","      <td id=\"T_1500e_row9_col3\" class=\"data row9 col3\" >0.9118</td>\n","      <td id=\"T_1500e_row9_col4\" class=\"data row9 col4\" >0.1585</td>\n","      <td id=\"T_1500e_row9_col5\" class=\"data row9 col5\" >0.1187</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_1500e_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n","      <td id=\"T_1500e_row10_col0\" class=\"data row10 col0\" >3.1994</td>\n","      <td id=\"T_1500e_row10_col1\" class=\"data row10 col1\" >21.9369</td>\n","      <td id=\"T_1500e_row10_col2\" class=\"data row10 col2\" >4.6339</td>\n","      <td id=\"T_1500e_row10_col3\" class=\"data row10 col3\" >0.9216</td>\n","      <td id=\"T_1500e_row10_col4\" class=\"data row10 col4\" >0.1511</td>\n","      <td id=\"T_1500e_row10_col5\" class=\"data row10 col5\" >0.1106</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_1500e_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n","      <td id=\"T_1500e_row11_col0\" class=\"data row11 col0\" >0.4119</td>\n","      <td id=\"T_1500e_row11_col1\" class=\"data row11 col1\" >6.8857</td>\n","      <td id=\"T_1500e_row11_col2\" class=\"data row11 col2\" >0.6813</td>\n","      <td id=\"T_1500e_row11_col3\" class=\"data row11 col3\" >0.0216</td>\n","      <td id=\"T_1500e_row11_col4\" class=\"data row11 col4\" >0.0214</td>\n","      <td id=\"T_1500e_row11_col5\" class=\"data row11 col5\" >0.0161</td>\n","    </tr>\n","  </tbody>\n","</table>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Processing:   0%|          | 0/4 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8a8391dd045d4444a769e1d9cff86612"}},"metadata":{"application/vnd.jupyter.widget-view+json":{"colab":{"custom_widget_manager":{"url":"https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"}}}}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}}]},{"cell_type":"code","source":["evaluate_model(best_model)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":207,"referenced_widgets":["4c25fc7a8a1544109f21f99376b10bff","9226abbad2354818be531342d271b67f","d6f76238bf254dc38bc214dc3019665e","08da994ae8214c35ba30f0fa70692f40","5346cea83d694d52bcfbfde4f5a85fe2","82369f966e9a4754bc395c7cd34e4dc1","1910713892204881b111a3b1f3310bda"]},"id":"5DTS1Xl81vQR","executionInfo":{"status":"ok","timestamp":1718906987086,"user_tz":-60,"elapsed":705,"user":{"displayName":"Oceane Hadama","userId":"07293307270246501044"}},"outputId":"3bbd2dce-485a-4270-d09a-325729b09d49"},"execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/plain":["interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Pipeline Plot', 'pipelin…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4c25fc7a8a1544109f21f99376b10bff"}},"metadata":{"application/vnd.jupyter.widget-view+json":{"colab":{"custom_widget_manager":{"url":"https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"}}}}}]},{"cell_type":"code","source":["tune_model(best_model)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":574,"referenced_widgets":["bf13bcc874074e3687673ec991ae241b","be23c5e89d7c4bef80ce4b8dbd9be03f","5cdd3648355345c1b3e267c623536479","ab5ed4e281c4492cb134e3a890ce3eee","24aba235767b41dcb126b6d5a2d38803","7685ad4695d549bdb0f5ddf2f72e6a8b","40ebdc25431c4eb99ac61b37e91f2703","3f6409fb29864067b08f13613e0cc518","19a784d69fe9407a9dee7c1f043729d6","73cfd908ef4e4d638bc28a7f0b7de309","55fdedd27f304a92b5830a90254c3bc9"]},"id":"mi_WXS7b10cR","executionInfo":{"status":"ok","timestamp":1718907038953,"user_tz":-60,"elapsed":38620,"user":{"displayName":"Oceane Hadama","userId":"07293307270246501044"}},"outputId":"e75464d1-d7f2-4884-a992-df0c770661ec"},"execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<pandas.io.formats.style.Styler at 0x7d6c60b9bdf0>"],"text/html":["<style type=\"text/css\">\n","#T_8a765_row10_col0, #T_8a765_row10_col1, #T_8a765_row10_col2, #T_8a765_row10_col3, #T_8a765_row10_col4, #T_8a765_row10_col5 {\n","  background: yellow;\n","}\n","</style>\n","<table id=\"T_8a765\" class=\"dataframe\">\n","  <thead>\n","    <tr>\n","      <th class=\"blank level0\" >&nbsp;</th>\n","      <th id=\"T_8a765_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n","      <th id=\"T_8a765_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n","      <th id=\"T_8a765_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n","      <th id=\"T_8a765_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n","      <th id=\"T_8a765_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n","      <th id=\"T_8a765_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_8a765_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n","      <td id=\"T_8a765_row0_col0\" class=\"data row0 col0\" >3.4198</td>\n","      <td id=\"T_8a765_row0_col1\" class=\"data row0 col1\" >22.4270</td>\n","      <td id=\"T_8a765_row0_col2\" class=\"data row0 col2\" >4.7357</td>\n","      <td id=\"T_8a765_row0_col3\" class=\"data row0 col3\" >0.9116</td>\n","      <td id=\"T_8a765_row0_col4\" class=\"data row0 col4\" >0.1274</td>\n","      <td id=\"T_8a765_row0_col5\" class=\"data row0 col5\" >0.1046</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_8a765_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n","      <td id=\"T_8a765_row1_col0\" class=\"data row1 col0\" >4.0398</td>\n","      <td id=\"T_8a765_row1_col1\" class=\"data row1 col1\" >29.2443</td>\n","      <td id=\"T_8a765_row1_col2\" class=\"data row1 col2\" >5.4078</td>\n","      <td id=\"T_8a765_row1_col3\" class=\"data row1 col3\" >0.9139</td>\n","      <td id=\"T_8a765_row1_col4\" class=\"data row1 col4\" >0.1688</td>\n","      <td id=\"T_8a765_row1_col5\" class=\"data row1 col5\" >0.1339</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_8a765_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n","      <td id=\"T_8a765_row2_col0\" class=\"data row2 col0\" >3.9307</td>\n","      <td id=\"T_8a765_row2_col1\" class=\"data row2 col1\" >26.7268</td>\n","      <td id=\"T_8a765_row2_col2\" class=\"data row2 col2\" >5.1698</td>\n","      <td id=\"T_8a765_row2_col3\" class=\"data row2 col3\" >0.8930</td>\n","      <td id=\"T_8a765_row2_col4\" class=\"data row2 col4\" >0.2445</td>\n","      <td id=\"T_8a765_row2_col5\" class=\"data row2 col5\" >0.1340</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_8a765_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n","      <td id=\"T_8a765_row3_col0\" class=\"data row3 col0\" >4.0112</td>\n","      <td id=\"T_8a765_row3_col1\" class=\"data row3 col1\" >29.6160</td>\n","      <td id=\"T_8a765_row3_col2\" class=\"data row3 col2\" >5.4421</td>\n","      <td id=\"T_8a765_row3_col3\" class=\"data row3 col3\" >0.9156</td>\n","      <td id=\"T_8a765_row3_col4\" class=\"data row3 col4\" >0.1702</td>\n","      <td id=\"T_8a765_row3_col5\" class=\"data row3 col5\" >0.1352</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_8a765_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n","      <td id=\"T_8a765_row4_col0\" class=\"data row4 col0\" >3.8475</td>\n","      <td id=\"T_8a765_row4_col1\" class=\"data row4 col1\" >28.6295</td>\n","      <td id=\"T_8a765_row4_col2\" class=\"data row4 col2\" >5.3507</td>\n","      <td id=\"T_8a765_row4_col3\" class=\"data row4 col3\" >0.9053</td>\n","      <td id=\"T_8a765_row4_col4\" class=\"data row4 col4\" >0.2250</td>\n","      <td id=\"T_8a765_row4_col5\" class=\"data row4 col5\" >0.1541</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_8a765_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n","      <td id=\"T_8a765_row5_col0\" class=\"data row5 col0\" >3.8243</td>\n","      <td id=\"T_8a765_row5_col1\" class=\"data row5 col1\" >26.3901</td>\n","      <td id=\"T_8a765_row5_col2\" class=\"data row5 col2\" >5.1371</td>\n","      <td id=\"T_8a765_row5_col3\" class=\"data row5 col3\" >0.9141</td>\n","      <td id=\"T_8a765_row5_col4\" class=\"data row5 col4\" >0.1976</td>\n","      <td id=\"T_8a765_row5_col5\" class=\"data row5 col5\" >0.1397</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_8a765_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n","      <td id=\"T_8a765_row6_col0\" class=\"data row6 col0\" >3.2980</td>\n","      <td id=\"T_8a765_row6_col1\" class=\"data row6 col1\" >21.6917</td>\n","      <td id=\"T_8a765_row6_col2\" class=\"data row6 col2\" >4.6574</td>\n","      <td id=\"T_8a765_row6_col3\" class=\"data row6 col3\" >0.9182</td>\n","      <td id=\"T_8a765_row6_col4\" class=\"data row6 col4\" >0.1526</td>\n","      <td id=\"T_8a765_row6_col5\" class=\"data row6 col5\" >0.1092</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_8a765_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n","      <td id=\"T_8a765_row7_col0\" class=\"data row7 col0\" >3.5940</td>\n","      <td id=\"T_8a765_row7_col1\" class=\"data row7 col1\" >24.6624</td>\n","      <td id=\"T_8a765_row7_col2\" class=\"data row7 col2\" >4.9661</td>\n","      <td id=\"T_8a765_row7_col3\" class=\"data row7 col3\" >0.9048</td>\n","      <td id=\"T_8a765_row7_col4\" class=\"data row7 col4\" >0.1705</td>\n","      <td id=\"T_8a765_row7_col5\" class=\"data row7 col5\" >0.1370</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_8a765_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n","      <td id=\"T_8a765_row8_col0\" class=\"data row8 col0\" >3.1270</td>\n","      <td id=\"T_8a765_row8_col1\" class=\"data row8 col1\" >16.3962</td>\n","      <td id=\"T_8a765_row8_col2\" class=\"data row8 col2\" >4.0492</td>\n","      <td id=\"T_8a765_row8_col3\" class=\"data row8 col3\" >0.9216</td>\n","      <td id=\"T_8a765_row8_col4\" class=\"data row8 col4\" >0.1211</td>\n","      <td id=\"T_8a765_row8_col5\" class=\"data row8 col5\" >0.0977</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_8a765_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n","      <td id=\"T_8a765_row9_col0\" class=\"data row9 col0\" >4.3710</td>\n","      <td id=\"T_8a765_row9_col1\" class=\"data row9 col1\" >33.5371</td>\n","      <td id=\"T_8a765_row9_col2\" class=\"data row9 col2\" >5.7911</td>\n","      <td id=\"T_8a765_row9_col3\" class=\"data row9 col3\" >0.8758</td>\n","      <td id=\"T_8a765_row9_col4\" class=\"data row9 col4\" >0.1625</td>\n","      <td id=\"T_8a765_row9_col5\" class=\"data row9 col5\" >0.1367</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_8a765_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n","      <td id=\"T_8a765_row10_col0\" class=\"data row10 col0\" >3.7463</td>\n","      <td id=\"T_8a765_row10_col1\" class=\"data row10 col1\" >25.9321</td>\n","      <td id=\"T_8a765_row10_col2\" class=\"data row10 col2\" >5.0707</td>\n","      <td id=\"T_8a765_row10_col3\" class=\"data row10 col3\" >0.9074</td>\n","      <td id=\"T_8a765_row10_col4\" class=\"data row10 col4\" >0.1740</td>\n","      <td id=\"T_8a765_row10_col5\" class=\"data row10 col5\" >0.1282</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_8a765_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n","      <td id=\"T_8a765_row11_col0\" class=\"data row11 col0\" >0.3621</td>\n","      <td id=\"T_8a765_row11_col1\" class=\"data row11 col1\" >4.6185</td>\n","      <td id=\"T_8a765_row11_col2\" class=\"data row11 col2\" >0.4691</td>\n","      <td id=\"T_8a765_row11_col3\" class=\"data row11 col3\" >0.0131</td>\n","      <td id=\"T_8a765_row11_col4\" class=\"data row11 col4\" >0.0371</td>\n","      <td id=\"T_8a765_row11_col5\" class=\"data row11 col5\" >0.0171</td>\n","    </tr>\n","  </tbody>\n","</table>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Processing:   0%|          | 0/7 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bf13bcc874074e3687673ec991ae241b"}},"metadata":{"application/vnd.jupyter.widget-view+json":{"colab":{"custom_widget_manager":{"url":"https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"}}}}},{"output_type":"stream","name":"stdout","text":["Fitting 10 folds for each of 10 candidates, totalling 100 fits\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one).\n"]},{"output_type":"execute_result","data":{"text/plain":["LGBMRegressor(n_jobs=-1, random_state=123)"],"text/html":["<style>#sk-container-id-2 {\n","  /* Definition of color scheme common for light and dark mode */\n","  --sklearn-color-text: black;\n","  --sklearn-color-line: gray;\n","  /* Definition of color scheme for unfitted estimators */\n","  --sklearn-color-unfitted-level-0: #fff5e6;\n","  --sklearn-color-unfitted-level-1: #f6e4d2;\n","  --sklearn-color-unfitted-level-2: #ffe0b3;\n","  --sklearn-color-unfitted-level-3: chocolate;\n","  /* Definition of color scheme for fitted estimators */\n","  --sklearn-color-fitted-level-0: #f0f8ff;\n","  --sklearn-color-fitted-level-1: #d4ebff;\n","  --sklearn-color-fitted-level-2: #b3dbfd;\n","  --sklearn-color-fitted-level-3: cornflowerblue;\n","\n","  /* Specific color for light theme */\n","  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n","  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-icon: #696969;\n","\n","  @media (prefers-color-scheme: dark) {\n","    /* Redefinition of color scheme for dark theme */\n","    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n","    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-icon: #878787;\n","  }\n","}\n","\n","#sk-container-id-2 {\n","  color: var(--sklearn-color-text);\n","}\n","\n","#sk-container-id-2 pre {\n","  padding: 0;\n","}\n","\n","#sk-container-id-2 input.sk-hidden--visually {\n","  border: 0;\n","  clip: rect(1px 1px 1px 1px);\n","  clip: rect(1px, 1px, 1px, 1px);\n","  height: 1px;\n","  margin: -1px;\n","  overflow: hidden;\n","  padding: 0;\n","  position: absolute;\n","  width: 1px;\n","}\n","\n","#sk-container-id-2 div.sk-dashed-wrapped {\n","  border: 1px dashed var(--sklearn-color-line);\n","  margin: 0 0.4em 0.5em 0.4em;\n","  box-sizing: border-box;\n","  padding-bottom: 0.4em;\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","#sk-container-id-2 div.sk-container {\n","  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n","     but bootstrap.min.css set `[hidden] { display: none !important; }`\n","     so we also need the `!important` here to be able to override the\n","     default hidden behavior on the sphinx rendered scikit-learn.org.\n","     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n","  display: inline-block !important;\n","  position: relative;\n","}\n","\n","#sk-container-id-2 div.sk-text-repr-fallback {\n","  display: none;\n","}\n","\n","div.sk-parallel-item,\n","div.sk-serial,\n","div.sk-item {\n","  /* draw centered vertical line to link estimators */\n","  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n","  background-size: 2px 100%;\n","  background-repeat: no-repeat;\n","  background-position: center center;\n","}\n","\n","/* Parallel-specific style estimator block */\n","\n","#sk-container-id-2 div.sk-parallel-item::after {\n","  content: \"\";\n","  width: 100%;\n","  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n","  flex-grow: 1;\n","}\n","\n","#sk-container-id-2 div.sk-parallel {\n","  display: flex;\n","  align-items: stretch;\n","  justify-content: center;\n","  background-color: var(--sklearn-color-background);\n","  position: relative;\n","}\n","\n","#sk-container-id-2 div.sk-parallel-item {\n","  display: flex;\n","  flex-direction: column;\n","}\n","\n","#sk-container-id-2 div.sk-parallel-item:first-child::after {\n","  align-self: flex-end;\n","  width: 50%;\n","}\n","\n","#sk-container-id-2 div.sk-parallel-item:last-child::after {\n","  align-self: flex-start;\n","  width: 50%;\n","}\n","\n","#sk-container-id-2 div.sk-parallel-item:only-child::after {\n","  width: 0;\n","}\n","\n","/* Serial-specific style estimator block */\n","\n","#sk-container-id-2 div.sk-serial {\n","  display: flex;\n","  flex-direction: column;\n","  align-items: center;\n","  background-color: var(--sklearn-color-background);\n","  padding-right: 1em;\n","  padding-left: 1em;\n","}\n","\n","\n","/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n","clickable and can be expanded/collapsed.\n","- Pipeline and ColumnTransformer use this feature and define the default style\n","- Estimators will overwrite some part of the style using the `sk-estimator` class\n","*/\n","\n","/* Pipeline and ColumnTransformer style (default) */\n","\n","#sk-container-id-2 div.sk-toggleable {\n","  /* Default theme specific background. It is overwritten whether we have a\n","  specific estimator or a Pipeline/ColumnTransformer */\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","/* Toggleable label */\n","#sk-container-id-2 label.sk-toggleable__label {\n","  cursor: pointer;\n","  display: block;\n","  width: 100%;\n","  margin-bottom: 0;\n","  padding: 0.5em;\n","  box-sizing: border-box;\n","  text-align: center;\n","}\n","\n","#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n","  /* Arrow on the left of the label */\n","  content: \"▸\";\n","  float: left;\n","  margin-right: 0.25em;\n","  color: var(--sklearn-color-icon);\n","}\n","\n","#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n","  color: var(--sklearn-color-text);\n","}\n","\n","/* Toggleable content - dropdown */\n","\n","#sk-container-id-2 div.sk-toggleable__content {\n","  max-height: 0;\n","  max-width: 0;\n","  overflow: hidden;\n","  text-align: left;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-2 div.sk-toggleable__content.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-2 div.sk-toggleable__content pre {\n","  margin: 0.2em;\n","  border-radius: 0.25em;\n","  color: var(--sklearn-color-text);\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n","  /* Expand drop-down */\n","  max-height: 200px;\n","  max-width: 100%;\n","  overflow: auto;\n","}\n","\n","#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n","  content: \"▾\";\n","}\n","\n","/* Pipeline/ColumnTransformer-specific style */\n","\n","#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator-specific style */\n","\n","/* Colorize estimator box */\n","#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n","#sk-container-id-2 div.sk-label label {\n","  /* The background is the default theme color */\n","  color: var(--sklearn-color-text-on-default-background);\n","}\n","\n","/* On hover, darken the color of the background */\n","#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","/* Label box, darken color on hover, fitted */\n","#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator label */\n","\n","#sk-container-id-2 div.sk-label label {\n","  font-family: monospace;\n","  font-weight: bold;\n","  display: inline-block;\n","  line-height: 1.2em;\n","}\n","\n","#sk-container-id-2 div.sk-label-container {\n","  text-align: center;\n","}\n","\n","/* Estimator-specific */\n","#sk-container-id-2 div.sk-estimator {\n","  font-family: monospace;\n","  border: 1px dotted var(--sklearn-color-border-box);\n","  border-radius: 0.25em;\n","  box-sizing: border-box;\n","  margin-bottom: 0.5em;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-2 div.sk-estimator.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","/* on hover */\n","#sk-container-id-2 div.sk-estimator:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-2 div.sk-estimator.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Specification for estimator info (e.g. \"i\" and \"?\") */\n","\n","/* Common style for \"i\" and \"?\" */\n","\n",".sk-estimator-doc-link,\n","a:link.sk-estimator-doc-link,\n","a:visited.sk-estimator-doc-link {\n","  float: right;\n","  font-size: smaller;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1em;\n","  height: 1em;\n","  width: 1em;\n","  text-decoration: none !important;\n","  margin-left: 1ex;\n","  /* unfitted */\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-unfitted-level-1);\n","}\n","\n",".sk-estimator-doc-link.fitted,\n","a:link.sk-estimator-doc-link.fitted,\n","a:visited.sk-estimator-doc-link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","/* Span, style for the box shown on hovering the info icon */\n",".sk-estimator-doc-link span {\n","  display: none;\n","  z-index: 9999;\n","  position: relative;\n","  font-weight: normal;\n","  right: .2ex;\n","  padding: .5ex;\n","  margin: .5ex;\n","  width: min-content;\n","  min-width: 20ex;\n","  max-width: 50ex;\n","  color: var(--sklearn-color-text);\n","  box-shadow: 2pt 2pt 4pt #999;\n","  /* unfitted */\n","  background: var(--sklearn-color-unfitted-level-0);\n","  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n","}\n","\n",".sk-estimator-doc-link.fitted span {\n","  /* fitted */\n","  background: var(--sklearn-color-fitted-level-0);\n","  border: var(--sklearn-color-fitted-level-3);\n","}\n","\n",".sk-estimator-doc-link:hover span {\n","  display: block;\n","}\n","\n","/* \"?\"-specific style due to the `<a>` HTML tag */\n","\n","#sk-container-id-2 a.estimator_doc_link {\n","  float: right;\n","  font-size: 1rem;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1rem;\n","  height: 1rem;\n","  width: 1rem;\n","  text-decoration: none;\n","  /* unfitted */\n","  color: var(--sklearn-color-unfitted-level-1);\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","}\n","\n","#sk-container-id-2 a.estimator_doc_link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","#sk-container-id-2 a.estimator_doc_link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","}\n","</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMRegressor(n_jobs=-1, random_state=123)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LGBMRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LGBMRegressor(n_jobs=-1, random_state=123)</pre></div> </div></div></div></div>"]},"metadata":{},"execution_count":19}]},{"cell_type":"code","source":["import pickle"],"metadata":{"id":"RrAZ7yCR2BlE","executionInfo":{"status":"ok","timestamp":1718907056758,"user_tz":-60,"elapsed":640,"user":{"displayName":"Oceane Hadama","userId":"07293307270246501044"}}},"execution_count":20,"outputs":[]},{"cell_type":"code","source":["pickle.dump(best_model, open('concrete.pkl', 'wb'))"],"metadata":{"id":"PDgkO3hM2EpL","executionInfo":{"status":"ok","timestamp":1718907081040,"user_tz":-60,"elapsed":659,"user":{"displayName":"Oceane Hadama","userId":"07293307270246501044"}}},"execution_count":21,"outputs":[]}]}