diff --git a/preprocess/analysedonnees.ipynb b/preprocess/analysedonnees.ipynb
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..5f37c1db47321a7f8f3e308cbfb3686d5b34da93 100644
--- a/preprocess/analysedonnees.ipynb
+++ b/preprocess/analysedonnees.ipynb
@@ -0,0 +1,311 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n",
+    "\n",
+    "X = pd.read_csv('data/X_train_6ZIKlTY.csv')\n",
+    "y = pd.read_csv('data/y_train_lXj6X5y.csv')\n",
+    "X_challenge = pd.read_csv('data/X_test_oiZ2ukx.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Analyse des données"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Valeurs manquantes dans les données d'entrainement :\n",
+      "Index                        0\n",
+      "patient_id                   0\n",
+      "cohort                       0\n",
+      "sexM                         0\n",
+      "gene                     17958\n",
+      "age_at_diagnosis          2830\n",
+      "age                          0\n",
+      "ledd                     20593\n",
+      "time_since_intake_on     25940\n",
+      "time_since_intake_off    43828\n",
+      "on                       16659\n",
+      "off                      23407\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Valeurs manquantes dans les données d'entrainement :\")\n",
+    "print(X.isna().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Proportions de valeurs manquantes dans les données d'entrainement :\n",
+      "Index                    0.000000\n",
+      "patient_id               0.000000\n",
+      "cohort                   0.000000\n",
+      "sexM                     0.000000\n",
+      "gene                     0.322968\n",
+      "age_at_diagnosis         0.050897\n",
+      "age                      0.000000\n",
+      "ledd                     0.370358\n",
+      "time_since_intake_on     0.466522\n",
+      "time_since_intake_off    0.788231\n",
+      "on                       0.299606\n",
+      "off                      0.420966\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Proportions de valeurs manquantes dans les données d'entrainement :\")\n",
+    "print(X.isna().mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Type des données\n",
+      "Index                      int64\n",
+      "patient_id                object\n",
+      "cohort                    object\n",
+      "sexM                       int64\n",
+      "gene                      object\n",
+      "age_at_diagnosis         float64\n",
+      "age                      float64\n",
+      "ledd                     float64\n",
+      "time_since_intake_on     float64\n",
+      "time_since_intake_off    float64\n",
+      "on                       float64\n",
+      "off                      float64\n",
+      "dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Type des données\")\n",
+    "print(X.dtypes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Comme mentionné dans le contexte du challenge, off et on ont beaucoup de valeurs manquantes et il faudra réfléchir à comment on comble ces valeurs. Cependant time_since_intake_xx ont aussi pas mal de valeurs manquantes notamment _off qui a quand meme 78% de valeurs manquantes donc à voir si on peut pas enlever cette feature."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Distribution des données non NaN\n",
+      "              Index          sexM  age_at_diagnosis           age  \\\n",
+      "count  55603.000000  55603.000000      52773.000000  55603.000000   \n",
+      "mean   27801.000000      0.596353         56.734648     62.516422   \n",
+      "std    16051.347846      0.490633         10.823093     11.484480   \n",
+      "min        0.000000      0.000000         16.500000     16.500000   \n",
+      "25%    13900.500000      0.000000         49.500000     54.900000   \n",
+      "50%    27801.000000      1.000000         56.900000     62.500000   \n",
+      "75%    41701.500000      1.000000         64.400000     70.500000   \n",
+      "max    55602.000000      1.000000         89.900000    103.300000   \n",
+      "\n",
+      "               ledd  time_since_intake_on  time_since_intake_off  \\\n",
+      "count  35010.000000          29663.000000           11775.000000   \n",
+      "mean     638.102228              1.953717              14.199448   \n",
+      "std      219.443182              1.168793               2.844782   \n",
+      "min       50.000000              0.000000               6.900000   \n",
+      "25%      481.000000              1.100000              12.200000   \n",
+      "50%      611.000000              1.600000              13.800000   \n",
+      "75%      765.000000              2.600000              15.700000   \n",
+      "max     1796.000000              6.300000              25.100000   \n",
+      "\n",
+      "                 on           off  \n",
+      "count  38944.000000  32196.000000  \n",
+      "mean      21.985980     26.295316  \n",
+      "std       10.371291     16.534567  \n",
+      "min        0.000000      0.000000  \n",
+      "25%       14.000000     13.000000  \n",
+      "50%       21.000000     24.000000  \n",
+      "75%       28.000000     38.000000  \n",
+      "max       95.000000    106.000000  \n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1500 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# distribution des données non NaN\n",
+    "import matplotlib.pyplot as plt\n",
+    "print(\"Distribution des données non NaN\")\n",
+    "print(X.describe())\n",
+    "X.hist(bins=50, figsize=(20,15))\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Distribution des données du challenge\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1500 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(\"Distribution des données du challenge\")\n",
+    "X_challenge.hist(bins=50, figsize=(20,15))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Faut faire gaffe faut mettre le time since diagnosis dans X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_challenge['time_since_diagnosis']=X_challenge['age']-X_challenge['age_at_diagnosis']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Avec time since diagnosis c'est mieux\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[<Axes: title={'center': 'Index'}>,\n",
+       "        <Axes: title={'center': 'sexM'}>,\n",
+       "        <Axes: title={'center': 'age_at_diagnosis'}>],\n",
+       "       [<Axes: title={'center': 'age'}>,\n",
+       "        <Axes: title={'center': 'ledd'}>,\n",
+       "        <Axes: title={'center': 'time_since_intake_on'}>],\n",
+       "       [<Axes: title={'center': 'time_since_intake_off'}>,\n",
+       "        <Axes: title={'center': 'on'}>, <Axes: title={'center': 'off'}>],\n",
+       "       [<Axes: title={'center': 'time_since_diagnosis'}>, <Axes: >,\n",
+       "        <Axes: >]], dtype=object)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1500 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(\"Avec time since diagnosis c'est mieux\")\n",
+    "X_challenge.hist(bins=50, figsize=(20,15))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}