healthcare_fees_starting_kit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IPP M2DS</a>\n",
    "# Predicting the France healthcare fees\n",
    "\n",
    "<i> LAMJOUN Jihane, BONNETIER Isabel, MERRHEIM Maïssane, CREUSILLET Augustin, MESSIN Guerlain, PANTELEICIUC Ion </i>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Introduction : \n",
    "\n",
    "The dataset is provided by the french online health insurance : Ameli. Link : https://data.ameli.fr/explore/dataset/honoraires/\n",
    "\n",
    "The training data contains 37859 lines and the test data contains 9466 lines. The data contains : \n",
    "- the year (annee)\n",
    "- the health_profession (profession_sante)\n",
    "- the region (region)\n",
    "- the department (departement)\n",
    "- fees_without_exceeding_means (hono_sans_depassement_moyens)\n",
    "\n",
    "The goal is to predict the fees_without_exceeding_means column. The prediction quality is measured by the RMSE.\n",
    "The challenge in this RAMP is to predict as precisely as possible the fees of medical consultations knowing the year of the consultation, the heal_profession of the doctor, the region and the department of the health appointment. The are a lot of rows in the dataset, therefore the training of the model should lead to qualitative and interesting results. The goal here is to have the best precision score on predicting the fees of medical consultations (fees_without_exceeding_means column)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import problem\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import OrdinalEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>annee</th>\n",
       "      <th>profession_sante</th>\n",
       "      <th>region</th>\n",
       "      <th>departement</th>\n",
       "      <th>hono_sans_depassement_moyens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>Ensemble des médecins spécialistes (hors génér...</td>\n",
       "      <td>53</td>\n",
       "      <td>35</td>\n",
       "      <td>262137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>Orthoptistes</td>\n",
       "      <td>84</td>\n",
       "      <td>15</td>\n",
       "      <td>50893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021</td>\n",
       "      <td>Sages-femmes</td>\n",
       "      <td>76</td>\n",
       "      <td>34</td>\n",
       "      <td>72474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>Ophtalmologues</td>\n",
       "      <td>75</td>\n",
       "      <td>24</td>\n",
       "      <td>371491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>Sages-femmes</td>\n",
       "      <td>24</td>\n",
       "      <td>41</td>\n",
       "      <td>37371</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   annee                                   profession_sante  region  \\\n",
       "0   2015  Ensemble des médecins spécialistes (hors génér...      53   \n",
       "1   2016                                       Orthoptistes      84   \n",
       "2   2021                                       Sages-femmes      76   \n",
       "3   2019                                     Ophtalmologues      75   \n",
       "4   2012                                       Sages-femmes      24   \n",
       "\n",
       "  departement  hono_sans_depassement_moyens  \n",
       "0          35                        262137  \n",
       "1          15                         50893  \n",
       "2          34                         72474  \n",
       "3          24                        371491  \n",
       "4          41                         37371  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_file = \"./data/train.csv\"\n",
    "df = pd.read_csv(data_file)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We look at the dataset, the datatypes and carry out necessary conversions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 37858 entries, 0 to 37857\n",
      "Data columns (total 5 columns):\n",
      " #   Column                        Non-Null Count  Dtype \n",
      "---  ------                        --------------  ----- \n",
      " 0   annee                         37858 non-null  int64 \n",
      " 1   profession_sante              37858 non-null  object\n",
      " 2   region                        37858 non-null  int64 \n",
      " 3   departement                   37858 non-null  object\n",
      " 4   hono_sans_depassement_moyens  37858 non-null  int64 \n",
      "dtypes: int64(3), object(2)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have different datatypes: int64 and object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "annee                           False\n",
       "profession_sante                False\n",
       "region                          False\n",
       "departement                     False\n",
       "hono_sans_depassement_moyens    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no missing values. Let's look at the 'annee' column, we will want to transform the years into datetime data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2015\n",
       "1        2016\n",
       "2        2021\n",
       "3        2019\n",
       "4        2012\n",
       "         ... \n",
       "37853    2014\n",
       "37854    2022\n",
       "37855    2012\n",
       "37856    2013\n",
       "37857    2013\n",
       "Name: annee, Length: 37858, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, 'annee']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 37858 entries, 0 to 37857\n",
      "Data columns (total 5 columns):\n",
      " #   Column                        Non-Null Count  Dtype         \n",
      "---  ------                        --------------  -----         \n",
      " 0   annee                         37858 non-null  datetime64[ns]\n",
      " 1   profession_sante              37858 non-null  object        \n",
      " 2   region                        37858 non-null  int64         \n",
      " 3   departement                   37858 non-null  object        \n",
      " 4   hono_sans_depassement_moyens  37858 non-null  int64         \n",
      "dtypes: datetime64[ns](1), int64(2), object(2)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# Convert the 'annee' column to datetime format\n",
    "df.loc[:, 'annee'] = pd.to_datetime(df.loc[:, 'annee'])\n",
    "# Display the updated information about the DataFrame df\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get the data we want to predict through the problem. The model will take a pd.DataFrame as input and output a np.array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type(X) : <class 'pandas.core.frame.DataFrame'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 37858 entries, 0 to 37857\n",
      "Data columns (total 4 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   annee             37858 non-null  int64 \n",
      " 1   profession_sante  37858 non-null  object\n",
      " 2   region            37858 non-null  int64 \n",
      " 3   departement       37858 non-null  object\n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 1.2+ MB\n",
      "None\n",
      "type(y) : <class 'numpy.ndarray'>\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>annee</th>\n",
       "      <th>profession_sante</th>\n",
       "      <th>region</th>\n",
       "      <th>departement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>Ensemble des médecins spécialistes (hors génér...</td>\n",
       "      <td>53</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>Orthoptistes</td>\n",
       "      <td>84</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021</td>\n",
       "      <td>Sages-femmes</td>\n",
       "      <td>76</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>Ophtalmologues</td>\n",
       "      <td>75</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>Sages-femmes</td>\n",
       "      <td>24</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   annee                                   profession_sante  region  \\\n",
       "0   2015  Ensemble des médecins spécialistes (hors génér...      53   \n",
       "1   2016                                       Orthoptistes      84   \n",
       "2   2021                                       Sages-femmes      76   \n",
       "3   2019                                     Ophtalmologues      75   \n",
       "4   2012                                       Sages-femmes      24   \n",
       "\n",
       "  departement  \n",
       "0          35  \n",
       "1          15  \n",
       "2          34  \n",
       "3          24  \n",
       "4          41  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the training data X and y from the problem\n",
    "X, y = problem.get_train_data()\n",
    "print(\"type(X) :\",type(X))\n",
    "print(X.info())\n",
    "print(\"type(y) :\",type(y))\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2010\n",
      "2022\n",
      "[2015 2016 2021 2019 2012 2020 2022 2017 2018 2011 2010 2013 2014]\n"
     ]
    }
   ],
   "source": [
    "print(min(X['annee']))\n",
    "print(max(X['annee']))\n",
    "print(X['annee'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[262137  50893  72474 371491  37371 144955 182554]\n",
      "Mean: 193614.0\n",
      "Standard Deviation: 240161.0\n",
      "Median: 153346.0\n",
      "Support (Number of elements): 37858\n"
     ]
    }
   ],
   "source": [
    "# Inspect the y np.array ie the mean fees of medical consultations without exceeding\n",
    "print(y[:7])\n",
    "\n",
    "# Calculate mean\n",
    "mean_y = np.mean(y)\n",
    "# Calculate standard deviation\n",
    "std_y = np.std(y)\n",
    "# Calculate median\n",
    "median_y = np.median(y)\n",
    "# Get the number of elements in the array (support)\n",
    "support_y = len(y)\n",
    "\n",
    "# Print the calculated statistics\n",
    "print(\"Mean:\", np.round(mean_y, 0))\n",
    "print(\"Standard Deviation:\", np.round(std_y,0))\n",
    "print(\"Median:\", np.round(median_y,0))\n",
    "print(\"Support (Number of elements):\", support_y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram of the mean fees of medical consultations (without exceeding)\n",
    "plt.hist(y, bins=50)\n",
    "plt.title(\"Mean fees of medical consultations (without exceeding)\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram of the years of the medical consultations\n",
    "plt.hist(X['annee'], bins=50)\n",
    "plt.title('Histogram of the years of the medical consultations')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictor of fees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the ordinal encoder\n",
    "categorical_encoder = OrdinalEncoder()\n",
    "# Define the list of categorical columns\n",
    "categorical_cols = [\"annee\", \"profession_sante\", \"region\", \"departement\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;ordinalencoder&#x27;,\n",
       "                                                  OrdinalEncoder(),\n",
       "                                                  [&#x27;annee&#x27;, &#x27;profession_sante&#x27;,\n",
       "                                                   &#x27;region&#x27;,\n",
       "                                                   &#x27;departement&#x27;])])),\n",
       "                (&#x27;randomforestregressor&#x27;,\n",
       "                 RandomForestRegressor(max_depth=10, max_features=10,\n",
       "                                       n_estimators=10, n_jobs=4))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;ordinalencoder&#x27;,\n",
       "                                                  OrdinalEncoder(),\n",
       "                                                  [&#x27;annee&#x27;, &#x27;profession_sante&#x27;,\n",
       "                                                   &#x27;region&#x27;,\n",
       "                                                   &#x27;departement&#x27;])])),\n",
       "                (&#x27;randomforestregressor&#x27;,\n",
       "                 RandomForestRegressor(max_depth=10, max_features=10,\n",
       "                                       n_estimators=10, n_jobs=4))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;columntransformer: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for columntransformer: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;ordinalencoder&#x27;, OrdinalEncoder(),\n",
       "                                 [&#x27;annee&#x27;, &#x27;profession_sante&#x27;, &#x27;region&#x27;,\n",
       "                                  &#x27;departement&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">ordinalencoder</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;annee&#x27;, &#x27;profession_sante&#x27;, &#x27;region&#x27;, &#x27;departement&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OrdinalEncoder()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">remainder</label><div class=\"sk-toggleable__content fitted\"><pre>[]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">passthrough</label><div class=\"sk-toggleable__content fitted\"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_depth=10, max_features=10, n_estimators=10, n_jobs=4)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('columntransformer',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('ordinalencoder',\n",
       "                                                  OrdinalEncoder(),\n",
       "                                                  ['annee', 'profession_sante',\n",
       "                                                   'region',\n",
       "                                                   'departement'])])),\n",
       "                ('randomforestregressor',\n",
       "                 RandomForestRegressor(max_depth=10, max_features=10,\n",
       "                                       n_estimators=10, n_jobs=4))])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a column transformer with the categorical encoder\n",
    "preprocessor = make_column_transformer(\n",
    "    (categorical_encoder, categorical_cols),\n",
    "    remainder='passthrough',  \n",
    ")\n",
    "\n",
    "# Initialize a predictor with specified parameters\n",
    "regressor = RandomForestRegressor(\n",
    "    n_estimators=10, max_depth=10, max_features=10, n_jobs=4\n",
    ")\n",
    "\n",
    "# Create a pipeline by chaining the preprocessor and regressor\n",
    "pipeline = make_pipeline(preprocessor, regressor)\n",
    "\n",
    "# Fit the pipeline to the training data (X) and target labels (y)\n",
    "pipeline.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 78738.54421592  73840.78580014   2882.38514815 ... 521978.07447909\n",
      "  79837.58559796 207794.65683395]\n",
      "RMSE =  113746.48919785266\n",
      "MAE =  40808.70800497042\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/lib/python3.9/site-packages/sklearn/metrics/_regression.py:483: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Get the test data from the problem\n",
    "X_test, y_test = problem.get_test_data()\n",
    "\n",
    "# Predict the target values for the test data using the pipeline\n",
    "y_pred = pipeline.predict(X_test)\n",
    "print(y_pred)\n",
    "\n",
    "# Compute the root mean squared error (RMSE) between the true and predicted target values\n",
    "print(\"RMSE = \",mean_squared_error(y_test, y_pred, squared=False))\n",
    "# Compute the mean absolute error (MAE) between the true and predicted target values\n",
    "print(\"MAE = \", mean_absolute_error(y_test, y_pred))"
   ]
  }
 ],
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