diff --git a/M2/Machine Learning/TP_1/2025_TP_1_M2_ISF.ipynb b/M2/Machine Learning/TP_1/2025_TP_1_M2_ISF.ipynb
index f585fdd..ef726ec 100644
--- a/M2/Machine Learning/TP_1/2025_TP_1_M2_ISF.ipynb
+++ b/M2/Machine Learning/TP_1/2025_TP_1_M2_ISF.ipynb
@@ -42,7 +42,7 @@
},
{
"cell_type": "code",
- "execution_count": 75,
+ "execution_count": 76,
"id": "e5631491",
"metadata": {},
"outputs": [
@@ -71,7 +71,7 @@
},
{
"cell_type": "code",
- "execution_count": 76,
+ "execution_count": 77,
"id": "b8acd836",
"metadata": {},
"outputs": [
@@ -107,7 +107,7 @@
},
{
"cell_type": "code",
- "execution_count": 77,
+ "execution_count": 78,
"id": "acc09d5e",
"metadata": {},
"outputs": [
@@ -130,7 +130,7 @@
},
{
"cell_type": "code",
- "execution_count": 78,
+ "execution_count": 79,
"id": "8590bf3a",
"metadata": {},
"outputs": [
@@ -149,7 +149,7 @@
},
{
"cell_type": "code",
- "execution_count": 79,
+ "execution_count": 80,
"id": "20165055",
"metadata": {},
"outputs": [
@@ -159,7 +159,7 @@
"4"
]
},
- "execution_count": 79,
+ "execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
@@ -178,7 +178,7 @@
},
{
"cell_type": "code",
- "execution_count": 80,
+ "execution_count": 81,
"id": "4028380e",
"metadata": {},
"outputs": [
@@ -205,7 +205,7 @@
},
{
"cell_type": "code",
- "execution_count": 81,
+ "execution_count": 82,
"id": "f2ab3b16",
"metadata": {},
"outputs": [
@@ -237,7 +237,7 @@
},
{
"cell_type": "code",
- "execution_count": 82,
+ "execution_count": 83,
"id": "817fddfd",
"metadata": {},
"outputs": [
@@ -292,7 +292,7 @@
},
{
"cell_type": "code",
- "execution_count": 83,
+ "execution_count": 84,
"id": "98874653",
"metadata": {},
"outputs": [
@@ -301,10 +301,10 @@
"output_type": "stream",
"text": [
"[6, 7, 8]\n",
- "172 ns ± 55.2 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)\n",
+ "198 ns ± 83.1 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)\n",
"[24333339, 24333340, 24333341]\n",
"[6, 7, 8]\n",
- "84.7 ns ± 3.38 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)\n",
+ "84.6 ns ± 2.61 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)\n",
"[3, 4, 5]\n",
"0 3\n",
"1 4\n",
@@ -332,7 +332,7 @@
},
{
"cell_type": "code",
- "execution_count": 84,
+ "execution_count": 85,
"id": "fd4a92a4",
"metadata": {},
"outputs": [
@@ -394,7 +394,7 @@
},
{
"cell_type": "code",
- "execution_count": 85,
+ "execution_count": 86,
"id": "02221362",
"metadata": {},
"outputs": [
@@ -464,7 +464,7 @@
},
{
"cell_type": "code",
- "execution_count": 86,
+ "execution_count": 87,
"id": "dc035fc6",
"metadata": {},
"outputs": [
@@ -536,7 +536,7 @@
},
{
"cell_type": "code",
- "execution_count": 87,
+ "execution_count": 88,
"id": "fb8a02fc",
"metadata": {},
"outputs": [
@@ -544,14 +544,14 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "1 0.637553\n",
- "2 1.280335\n",
- "3 -0.488665\n",
- "5 1.361516\n",
- "dtype: float64 1 -0.631333\n",
- "2 -0.312044\n",
- "3 -0.909777\n",
- "4 -0.493984\n",
+ "1 0.928099\n",
+ "2 -0.172455\n",
+ "3 0.141799\n",
+ "5 -0.588096\n",
+ "dtype: float64 1 1.069825\n",
+ "2 -1.470268\n",
+ "3 -0.167872\n",
+ "4 -1.178947\n",
"dtype: float64\n"
]
}
@@ -576,7 +576,7 @@
},
{
"cell_type": "code",
- "execution_count": 88,
+ "execution_count": 89,
"id": "c414f7ab",
"metadata": {},
"outputs": [
@@ -585,11 +585,11 @@
"output_type": "stream",
"text": [
" s t\n",
- "1 0.637553 -0.631333\n",
- "2 1.280335 -0.312044\n",
- "3 -0.488665 -0.909777\n",
- "4 NaN -0.493984\n",
- "5 1.361516 NaN\n"
+ "1 0.928099 1.069825\n",
+ "2 -0.172455 -1.470268\n",
+ "3 0.141799 -0.167872\n",
+ "4 NaN -1.178947\n",
+ "5 -0.588096 NaN\n"
]
}
],
@@ -615,7 +615,7 @@
},
{
"cell_type": "code",
- "execution_count": 89,
+ "execution_count": 90,
"id": "d385358c",
"metadata": {},
"outputs": [
@@ -624,11 +624,11 @@
"output_type": "stream",
"text": [
" s t SUM\n",
- "1 0.637553 -0.631333 0.006220\n",
- "2 1.280335 -0.312044 0.968291\n",
- "3 -0.488665 -0.909777 -1.398441\n",
- "4 NaN -0.493984 NaN\n",
- "5 1.361516 NaN NaN\n"
+ "1 0.928099 1.069825 1.997924\n",
+ "2 -0.172455 -1.470268 -1.642723\n",
+ "3 0.141799 -0.167872 -0.026073\n",
+ "4 NaN -1.178947 NaN\n",
+ "5 -0.588096 NaN NaN\n"
]
}
],
@@ -649,7 +649,7 @@
},
{
"cell_type": "code",
- "execution_count": 90,
+ "execution_count": 91,
"id": "004ea932",
"metadata": {},
"outputs": [
@@ -657,7 +657,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "-0.14131029153083904\n"
+ "0.10970936625453565\n"
]
}
],
@@ -698,7 +698,7 @@
},
{
"cell_type": "code",
- "execution_count": 91,
+ "execution_count": 92,
"id": "90c3ce0c",
"metadata": {},
"outputs": [
@@ -706,69 +706,48 @@
"name": "stdout",
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+ " -1.3210893 0.40108827 -0.16607253 -0.21043376 -1.94425434 -1.23837013\n",
+ " -2.28520531 -0.20832611 -0.07126827 1.30135451]\n",
"250\n"
]
}
@@ -785,7 +764,7 @@
},
{
"cell_type": "code",
- "execution_count": 92,
+ "execution_count": 93,
"id": "756550e8",
"metadata": {},
"outputs": [
@@ -793,17 +772,17 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "2012-01-01 -0.976660\n",
- "2012-01-02 -0.172139\n",
- "2012-01-03 -0.645412\n",
- "2012-01-04 0.643526\n",
- "2012-01-05 -0.022172\n",
+ "2012-01-01 -2.187168\n",
+ "2012-01-02 -1.009439\n",
+ "2012-01-03 1.363163\n",
+ "2012-01-04 -1.837257\n",
+ "2012-01-05 -0.735785\n",
" ... \n",
- "2012-09-02 -1.296994\n",
- "2012-09-03 0.845635\n",
- "2012-09-04 -0.497421\n",
- "2012-09-05 0.058465\n",
- "2012-09-06 -0.788075\n",
+ "2012-09-02 -1.238370\n",
+ "2012-09-03 -2.285205\n",
+ "2012-09-04 -0.208326\n",
+ "2012-09-05 -0.071268\n",
+ "2012-09-06 1.301355\n",
"Freq: D, Length: 250, dtype: float64\n"
]
}
@@ -817,7 +796,7 @@
},
{
"cell_type": "code",
- "execution_count": 93,
+ "execution_count": 94,
"id": "91f53912",
"metadata": {},
"outputs": [
@@ -867,7 +846,7 @@
},
{
"cell_type": "code",
- "execution_count": 94,
+ "execution_count": 95,
"id": "97d58527",
"metadata": {},
"outputs": [],
@@ -891,7 +870,7 @@
},
{
"cell_type": "code",
- "execution_count": 95,
+ "execution_count": 96,
"id": "c9597b48",
"metadata": {},
"outputs": [],
@@ -918,7 +897,7 @@
},
{
"cell_type": "code",
- "execution_count": 96,
+ "execution_count": 97,
"id": "8051b5f4",
"metadata": {},
"outputs": [],
@@ -937,7 +916,7 @@
},
{
"cell_type": "code",
- "execution_count": 97,
+ "execution_count": 98,
"id": "14b2a9c7",
"metadata": {},
"outputs": [
@@ -1036,7 +1015,7 @@
"type": "float"
}
],
- "ref": "3830f8e9-ad12-4096-9213-f4e0a26ccabb",
+ "ref": "c17d7661-09fb-457f-83cc-21c3198e9eb2",
"rows": [
[
"0",
@@ -1317,7 +1296,7 @@
"4 (66,74] [10000;15000[ 0 0.0 1.000000 "
]
},
- "execution_count": 97,
+ "execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
@@ -1329,7 +1308,7 @@
},
{
"cell_type": "code",
- "execution_count": 98,
+ "execution_count": 99,
"id": "e85ee86b",
"metadata": {},
"outputs": [
@@ -1428,7 +1407,7 @@
"type": "float"
}
],
- "ref": "41dd285e-24c0-4c17-a118-8caae9549c21",
+ "ref": "b19485aa-8448-4c7b-ac09-eda944baecc6",
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@@ -1716,7 +1695,7 @@
"14263 0.142466 "
]
},
- "execution_count": 98,
+ "execution_count": 99,
"metadata": {},
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}
@@ -1728,7 +1707,7 @@
},
{
"cell_type": "code",
- "execution_count": 99,
+ "execution_count": 100,
"id": "dbdd334a",
"metadata": {},
"outputs": [
@@ -1827,7 +1806,7 @@
"type": "float"
}
],
- "ref": "00b001fb-7129-404c-a519-d82bccadc879",
+ "ref": "c6767962-2f82-449c-97bf-946ec3adf45d",
"rows": [
[
"count",
@@ -2385,7 +2364,7 @@
"max 83421.850000 1.000000 "
]
},
- "execution_count": 99,
+ "execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
@@ -2397,7 +2376,7 @@
},
{
"cell_type": "code",
- "execution_count": 100,
+ "execution_count": 101,
"id": "6a32bf11",
"metadata": {},
"outputs": [
@@ -2407,7 +2386,7 @@
"(14264, 17)"
]
},
- "execution_count": 100,
+ "execution_count": 101,
"metadata": {},
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}
@@ -2419,7 +2398,7 @@
},
{
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+ "execution_count": 102,
"id": "0e30e0ac",
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"outputs": [
@@ -2434,7 +2413,7 @@
" dtype='object')"
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+ "execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
@@ -2446,7 +2425,7 @@
},
{
"cell_type": "code",
- "execution_count": 102,
+ "execution_count": 103,
"id": "1ca6278f",
"metadata": {},
"outputs": [
@@ -2465,7 +2444,7 @@
"type": "unknown"
}
],
- "ref": "c6a49c5d-9a4e-44b5-b8bb-390f34ee145b",
+ "ref": "fd6568eb-58c2-42af-8d3a-c519789cf21f",
"rows": [
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"ANNEE_CTR",
@@ -2562,7 +2541,7 @@
"dtype: object"
]
},
- "execution_count": 102,
+ "execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
@@ -2583,7 +2562,7 @@
},
{
"cell_type": "code",
- "execution_count": 103,
+ "execution_count": 104,
"id": "184234b2",
"metadata": {},
"outputs": [
@@ -2624,7 +2603,7 @@
},
{
"cell_type": "code",
- "execution_count": 104,
+ "execution_count": 105,
"id": "74e39ca9",
"metadata": {},
"outputs": [
@@ -2656,6 +2635,18 @@
"**Quels sont vos premiers commentaires ? Type de produit ? Périmètre temporel ? Modalités particulières ?**"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "290bafa5",
+ "metadata": {},
+ "source": [
+ "*Réponse :*\n",
+ "* Ptf Auto (on a des sinistres, coût et expo) de 2016 à 2021\n",
+ "* 4 types de variables (caractéristiques du contrant, du conducteur, de la voiture et de la sinistralité)\n",
+ "* présence de NA et valeurs aberrantes qu'on devra traiter\n",
+ "* Exposition annuelle et pas journalière "
+ ]
+ },
{
"cell_type": "markdown",
"id": "1b157d45",
@@ -2666,7 +2657,7 @@
},
{
"cell_type": "code",
- "execution_count": 105,
+ "execution_count": 106,
"id": "0be945ad",
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@@ -3528,7 +3519,7 @@
},
{
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+ "execution_count": 107,
"id": "762adc47",
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+ "(-1,0]",
+ "(-1,0]",
+ "(-1,0]"
],
"xaxis": "x",
"yaxis": "y"
@@ -18609,14 +18600,21 @@
},
"xaxis": {
"anchor": "y",
+ "categoryarray": [
+ "(-1,0]",
+ "(0,1]",
+ "(1,2]",
+ "(2,5]",
+ "(5,10]"
+ ],
+ "categoryorder": "array",
"domain": [
0,
1
],
"title": {
"text": "CONTRAT_ANCIENNETE"
- },
- "type": "category"
+ }
},
"yaxis": {
"anchor": "x",
@@ -18636,16 +18634,14 @@
}
],
"source": [
- "#Ecrivez votre code ici\n",
- "fig = px.histogram(data_set.sort_values('CONTRAT_ANCIENNETE'), x=\"CONTRAT_ANCIENNETE\")\n",
- "fig.update_xaxes(type='category') #Cette ligne permet de forcer la variable comme variable catégorielle et non numérique\n",
- "\n",
+ "fig = px.histogram(data_set, x=\"CONTRAT_ANCIENNETE\",\n",
+ " category_orders={'CONTRAT_ANCIENNETE': ['(-1,0]','(0,1]',\"(1,2]\",\"(2,5]\",\"(5,10]\"]})\n",
"fig.show()"
]
},
{
"cell_type": "code",
- "execution_count": 107,
+ "execution_count": 108,
"id": "bb7eaef1",
"metadata": {},
"outputs": [
@@ -18675,28 +18671,81 @@
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- "TRIMESTRIEL",
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+ "ANNUEL",
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"xaxis": "x",
"yaxis": "y"
@@ -33732,8 +33728,7 @@
],
"title": {
"text": "FREQUENCE_PAIEMENT_COTISATION"
- },
- "type": "category"
+ }
},
"yaxis": {
"anchor": "x",
@@ -33753,16 +33748,14 @@
}
],
"source": [
- "#Ecrivez votre code ici\n",
- "fig = px.histogram(data_set.sort_values('FREQUENCE_PAIEMENT_COTISATION'), x=\"FREQUENCE_PAIEMENT_COTISATION\")\n",
- "fig.update_xaxes(type='category') #Cette ligne permet de forcer la variable comme variable catégorielle et non numérique\n",
+ "fig = px.histogram(data_set, x=\"FREQUENCE_PAIEMENT_COTISATION\")\n",
"\n",
- "fig.show()"
+ "fig.show()\n"
]
},
{
"cell_type": "code",
- "execution_count": 108,
+ "execution_count": 109,
"id": "01231a32",
"metadata": {},
"outputs": [
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],
"xaxis": "x",
"yaxis": "y"
@@ -48843,14 +48836,20 @@
},
"xaxis": {
"anchor": "y",
+ "categoryarray": [
+ "[0;20000[",
+ "[20000;40000[",
+ "[40000;60000[",
+ "[60000;99999["
+ ],
+ "categoryorder": "array",
"domain": [
0,
1
],
"title": {
"text": "GROUPE_KM"
- },
- "type": "category"
+ }
},
"yaxis": {
"anchor": "x",
@@ -48870,16 +48869,14 @@
}
],
"source": [
- "#Ecrivez votre code ici\n",
- "fig = px.histogram(data_set.sort_values('GROUPE_KM'), x=\"GROUPE_KM\")\n",
- "fig.update_xaxes(type='category') #Cette ligne permet de forcer la variable comme variable catégorielle et non numérique\n",
- "\n",
+ "fig = px.histogram(data_set, x=\"GROUPE_KM\",\n",
+ " category_orders={'GROUPE_KM': [\"[0;20000[\",\"[20000;40000[\",\"[40000;60000[\",\"[60000;99999[\"]})\n",
"fig.show()"
]
},
{
"cell_type": "code",
- "execution_count": 109,
+ "execution_count": 110,
"id": "1535eec5",
"metadata": {},
"outputs": [
@@ -63996,7 +63993,7 @@
},
{
"cell_type": "code",
- "execution_count": 110,
+ "execution_count": 111,
"id": "71dfb629",
"metadata": {},
"outputs": [
@@ -64851,7 +64848,7 @@
},
{
"cell_type": "code",
- "execution_count": 111,
+ "execution_count": 112,
"id": "27489ee0",
"metadata": {},
"outputs": [
@@ -79976,7 +79973,7 @@
},
{
"cell_type": "code",
- "execution_count": 112,
+ "execution_count": 113,
"id": "e833f992",
"metadata": {},
"outputs": [
@@ -80010,7 +80007,7 @@
},
{
"cell_type": "code",
- "execution_count": 113,
+ "execution_count": 114,
"id": "a5fd2159",
"metadata": {},
"outputs": [],
@@ -80025,7 +80022,7 @@
},
{
"cell_type": "code",
- "execution_count": 114,
+ "execution_count": 115,
"id": "7c73dd35",
"metadata": {},
"outputs": [],
@@ -80038,7 +80035,7 @@
},
{
"cell_type": "code",
- "execution_count": 115,
+ "execution_count": 116,
"id": "50b1c279",
"metadata": {},
"outputs": [
@@ -80928,7 +80925,7 @@
},
{
"cell_type": "code",
- "execution_count": 116,
+ "execution_count": 117,
"id": "9b344a1e",
"metadata": {},
"outputs": [
@@ -81806,6 +81803,47 @@
"### Retraitement des NA "
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 118,
+ "id": "b6a3542a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Nombre_NA Pourcentage\n",
+ "ANNEE_CTR 0 0.000000\n",
+ "CONTRAT_ANCIENNETE 0 0.000000\n",
+ "FREQUENCE_PAIEMENT_COTISATION 0 0.000000\n",
+ "GROUPE_KM 3 0.021032\n",
+ "ZONE_RISQUE 1 0.007011\n",
+ "AGE_ASSURE_PRINCIPAL 0 0.000000\n",
+ "GENRE 742 5.201907\n",
+ "DEUXIEME_CONDUCTEUR 647 4.535895\n",
+ "ANCIENNETE_PERMIS 0 0.000000\n",
+ "ANNEE_CONSTRUCTION 206 1.444195\n",
+ "ENERGIE 0 0.000000\n",
+ "EQUIPEMENT_SECURITE 0 0.000000\n",
+ "PUISSANCE_VEHICULE 2175 15.248177\n",
+ "VALEUR_DU_BIEN 206 1.444195\n",
+ "NB 0 0.000000\n",
+ "CHARGE 0 0.000000\n",
+ "EXPO 0 0.000000\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Nombre de NA par variable\n",
+ "nan_count = pd.DataFrame(data_set.isna().sum(), columns=[\"Nombre_NA\"])\n",
+ "\n",
+ "#Ajout du % par rapport à la taille de la BD\n",
+ "nan_count[\"Pourcentage\"]= nan_count.divide(data_set.shape[0])*100\n",
+ "\n",
+ "print(nan_count)"
+ ]
+ },
{
"cell_type": "markdown",
"id": "7b605c28",
@@ -81814,6 +81852,21 @@
"**Quelles possibilités s'offrent à nous ?**"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "f1f0ffbd",
+ "metadata": {
+ "vscode": {
+ "languageId": "latex"
+ }
+ },
+ "source": [
+ "*Réponse :*\n",
+ "* Suppression s'il y a trop de NA \n",
+ "* Remplacer par la valeur moyenne/ classe la plus représenter\n",
+ "* Remplacer par une valeur prudente"
+ ]
+ },
{
"cell_type": "markdown",
"id": "e2aff9a3",
@@ -81824,7 +81877,7 @@
},
{
"cell_type": "code",
- "execution_count": 117,
+ "execution_count": 119,
"id": "efd61bfc",
"metadata": {},
"outputs": [
@@ -81897,11 +81950,6 @@
"rawType": "object",
"type": "string"
},
- {
- "name": "PUISSANCE_VEHICULE",
- "rawType": "object",
- "type": "unknown"
- },
{
"name": "VALEUR_DU_BIEN",
"rawType": "object",
@@ -81923,7 +81971,7 @@
"type": "float"
}
],
- "ref": "9b00fbe5-d4a9-4089-8620-85a05d2b2775",
+ "ref": "32354740-b8f6-4ec8-89b0-1e11f20aff2e",
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@@ -82019,7 +82063,6 @@
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@@ -82027,7 +82070,7 @@
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+ "columns": 16,
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@@ -82062,7 +82105,6 @@
" | ANNEE_CONSTRUCTION | \n",
" ENERGIE | \n",
" EQUIPEMENT_SECURITE | \n",
- " PUISSANCE_VEHICULE | \n",
" VALEUR_DU_BIEN | \n",
" NB | \n",
" CHARGE | \n",
@@ -82084,7 +82126,6 @@
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" FAUX | \n",
- " NaN | \n",
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@@ -82104,7 +82145,6 @@
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" VRAI | \n",
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" FAUX | \n",
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@@ -82144,7 +82183,6 @@
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- " (120,596] | \n",
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@@ -82164,7 +82202,6 @@
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@@ -82196,15 +82233,15 @@
"3 44 2018.0 ESSENCE VRAI \n",
"4 31 2009.0 DIESEL FAUX \n",
"\n",
- " PUISSANCE_VEHICULE VALEUR_DU_BIEN NB CHARGE EXPO \n",
- "0 NaN [10000;15000[ 0 0.0 0.672131 \n",
- "1 (85,96] [20000;25000[ 0 0.0 0.631148 \n",
- "2 (74,80] [15000;20000[ 0 0.0 0.821918 \n",
- "3 (120,596] [35000;99999[ 0 0.0 0.832877 \n",
- "4 (66,74] [10000;15000[ 0 0.0 1.000000 "
+ " VALEUR_DU_BIEN NB CHARGE EXPO \n",
+ "0 [10000;15000[ 0 0.0 0.672131 \n",
+ "1 [20000;25000[ 0 0.0 0.631148 \n",
+ "2 [15000;20000[ 0 0.0 0.821918 \n",
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+ "4 [10000;15000[ 0 0.0 1.000000 "
]
},
- "execution_count": 117,
+ "execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
@@ -82212,45 +82249,275 @@
"source": [
"data_retraitee = data_set\n",
"\n",
- "# Option 1 : pour la variable \"PUISSANCE_VEHICULE\"\n",
- "data_retraitee['PUISSANCE_VEHICULE'] = data_retraitee['PUISSANCE_VEHICULE'].dropna()\n",
+ "# Option 1 : Suppression des variables avec trop de NA (PUISSANCE_VEHICULE)\n",
+ "data_retraitee = data_retraitee.drop(\"PUISSANCE_VEHICULE\", axis='columns')\n",
"data_retraitee.head()"
]
},
{
"cell_type": "code",
- "execution_count": 118,
+ "execution_count": 120,
"id": "312ff05c",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/tp/_ld5_pzs6nx6mv1pbjhq1l740000gn/T/ipykernel_18546/3750079679.py:8: FutureWarning:\n",
+ "\n",
+ "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
+ "\n"
+ ]
+ }
+ ],
"source": [
- "#Option 2 : pour les variables (GROUPE_KM,GENRE,\n",
+ "#Option 2 : Remplacer par la classe la plus représentée/valeur moyenne (GROUPE_KM,GENRE,\n",
"# ANNEE_CONSTRUCTION,VALEUR_DU_BIEN,DEUXIEME_CONDUCTEUR)\n",
"\n",
- "data_retraitee[\"GROUPE_KM\"] = data_retraitee[\"GROUPE_KM\"].fillna(\"[0;20000[\")\n",
- "data_retraitee[\"GENRE\"] = data_retraitee[\"GENRE\"].fillna(\"Autre\")\n",
- "data_retraitee[\"ANNEE_CONSTRUCTION\"] = data_retraitee[\"ANNEE_CONSTRUCTION\"].fillna(\"2021\")\n",
- "data_retraitee[\"VALEUR_DU_BIEN\"] = data_retraitee[\"VALEUR_DU_BIEN\"].fillna(\"Inconnu\")\n",
- "data_retraitee[\"DEUXIEME_CONDUCTEUR\"] = data_retraitee[\"DEUXIEME_CONDUCTEUR\"].fillna(\"False\")"
+ "data_retraitee[\"GROUPE_KM\"] = data_retraitee[\"GROUPE_KM\"].fillna(data_retraitee[\"GROUPE_KM\"].mode()[0])\n",
+ "data_retraitee[\"GENRE\"] = data_retraitee[\"GENRE\"].fillna(\"M\")\n",
+ "data_retraitee[\"ANNEE_CONSTRUCTION\"] = data_retraitee[\"ANNEE_CONSTRUCTION\"].fillna(data_retraitee[\"ANNEE_CONSTRUCTION\"].median())\n",
+ "data_retraitee[\"VALEUR_DU_BIEN\"] = data_retraitee[\"VALEUR_DU_BIEN\"].fillna(data_retraitee[\"VALEUR_DU_BIEN\"].mode()[0])\n",
+ "data_retraitee[\"DEUXIEME_CONDUCTEUR\"] = data_retraitee[\"DEUXIEME_CONDUCTEUR\"].fillna(False)"
]
},
{
"cell_type": "code",
- "execution_count": 119,
+ "execution_count": null,
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- " PUISSANCE_VEHICULE | \n",
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" 0 | \n",
" 0.0 | \n",
@@ -82608,29 +82859,29 @@
"3 2021 (2,5] ANNUEL [0;20000[ \n",
"4 2018 (2,5] MENSUEL [20000;40000[ \n",
"\n",
- " ZONE_RISQUE AGE_ASSURE_PRINCIPAL GENRE DEUXIEME_CONDUCTEUR \\\n",
- "0 B 54 M False \n",
- "1 B 88 F True \n",
- "2 D 35 F True \n",
- "3 C 46 Autre False \n",
- "4 A 46 F False \n",
+ " ZONE_RISQUE AGE_ASSURE_PRINCIPAL GENRE DEUXIEME_CONDUCTEUR \\\n",
+ "0 B 54 M False \n",
+ "1 B 88 F True \n",
+ "2 D 35 F True \n",
+ "3 C 46 M False \n",
+ "4 A 46 F False \n",
"\n",
- " ANCIENNETE_PERMIS ANNEE_CONSTRUCTION ENERGIE EQUIPEMENT_SECURITE \\\n",
- "0 47 2016.0 ESSENCE FAUX \n",
- "1 55 2018.0 DIESEL VRAI \n",
- "2 16 2017.0 ESSENCE FAUX \n",
- "3 44 2018.0 ESSENCE VRAI \n",
- "4 31 2009.0 DIESEL FAUX \n",
+ " ANCIENNETE_PERMIS ANNEE_CONSTRUCTION ENERGIE EQUIPEMENT_SECURITE \\\n",
+ "0 47 2016.0 ESSENCE FAUX \n",
+ "1 55 2018.0 DIESEL VRAI \n",
+ "2 16 2017.0 ESSENCE FAUX \n",
+ "3 44 2018.0 ESSENCE VRAI \n",
+ "4 31 2009.0 DIESEL FAUX \n",
"\n",
- " PUISSANCE_VEHICULE VALEUR_DU_BIEN NB CHARGE EXPO \n",
- "0 NaN [10000;15000[ 0 0.0 0.672131 \n",
- "1 (85,96] [20000;25000[ 0 0.0 0.631148 \n",
- "2 (74,80] [15000;20000[ 0 0.0 0.821918 \n",
- "3 (120,596] [35000;99999[ 0 0.0 0.832877 \n",
- "4 (66,74] [10000;15000[ 0 0.0 1.000000 "
+ " VALEUR_DU_BIEN NB CHARGE EXPO \n",
+ "0 [10000;15000[ 0 0.0 0.672131 \n",
+ "1 [20000;25000[ 0 0.0 0.631148 \n",
+ "2 [15000;20000[ 0 0.0 0.821918 \n",
+ "3 [35000;99999[ 0 0.0 0.832877 \n",
+ "4 [10000;15000[ 0 0.0 1.000000 "
]
},
- "execution_count": 120,
+ "execution_count": 122,
"metadata": {},
"output_type": "execute_result"
}
@@ -82666,12 +82917,13 @@
},
{
"cell_type": "code",
- "execution_count": 121,
+ "execution_count": null,
"id": "25fbe5cf",
"metadata": {},
"outputs": [],
"source": [
- "#Ecrivez votre code ici\n"
+ "# Suppression des individus dont l'âge < 18\n",
+ "data_retraitee = data_retraitee[data_retraitee[\"AGE_ASSURE_PRINCIPAL\"] >= 18]"
]
},
{
@@ -82684,7 +82936,7 @@
},
{
"cell_type": "code",
- "execution_count": 122,
+ "execution_count": null,
"id": "0b5caf1d",
"metadata": {},
"outputs": [],
@@ -82702,7 +82954,7 @@
},
{
"cell_type": "code",
- "execution_count": 123,
+ "execution_count": null,
"id": "6a0f7afa",
"metadata": {},
"outputs": [],
@@ -82730,7 +82982,7 @@
},
{
"cell_type": "code",
- "execution_count": 124,
+ "execution_count": null,
"id": "c0d7ebf9",
"metadata": {},
"outputs": [],
@@ -82767,9 +83019,25 @@
},
{
"cell_type": "code",
- "execution_count": 134,
+ "execution_count": 127,
"id": "d40508d7",
"metadata": {},
+ "outputs": [],
+ "source": [
+ "#Agrégation selon la variable d'intérêt\n",
+ "plot_data = data_retraitee[[\"AGE_ASSURE_PRINCIPAL\", \"NB\",\"EXPO\"]]\n",
+ "plot_data = plot_data.groupby([\"AGE_ASSURE_PRINCIPAL\"], as_index=False).sum()\n",
+ "\n",
+ "#Calcul de la fréquence\n",
+ "plot_data[\"FREQ\"] = plot_data[\"NB\"] / plot_data[\"EXPO\"]\n",
+ "plot_data[\"FREQ\"] = plot_data[\"FREQ\"].fillna(0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 128,
+ "id": "ee960c8f",
+ "metadata": {},
"outputs": [
{
"data": {
@@ -83586,7 +83854,7 @@
}
},
"title": {
- "text": "Fréquence de sinistres en fonction de l'âge du conducteur"
+ "text": "Sinistralité selon l'âge"
},
"xaxis": {
"anchor": "y",
@@ -83595,7 +83863,7 @@
1
],
"title": {
- "text": "Âge du conducteur principal"
+ "text": "AGE_ASSURE_PRINCIPAL"
}
},
"yaxis": {
@@ -83605,7 +83873,7 @@
1
],
"title": {
- "text": "Fréquence"
+ "text": "FREQ"
}
}
}
@@ -83616,19 +83884,8 @@
}
],
"source": [
- "#Ecrivez votre code ici\n",
- "freq_by_age = data_retraitee.groupby('AGE_ASSURE_PRINCIPAL').agg({\n",
- " 'NB': 'sum',\n",
- " 'EXPO': 'sum'\n",
- "}).reset_index()\n",
- "\n",
- "freq_by_age['FREQ'] = freq_by_age['NB'] / freq_by_age['EXPO']\n",
- "freq_by_age['FREQ'] = freq_by_age['FREQ'].fillna(0)\n",
- "\n",
- "fig = px.line(freq_by_age, x='AGE_ASSURE_PRINCIPAL', y='FREQ',\n",
- " title='Fréquence de sinistres en fonction de l\\'âge du conducteur')\n",
- "fig.update_xaxes(title='Âge du conducteur principal')\n",
- "fig.update_yaxes(title='Fréquence')\n",
+ "#Représentation graphique\n",
+ "fig = px.line(plot_data, x=\"AGE_ASSURE_PRINCIPAL\", y=\"FREQ\", title=\"Sinistralité selon l'âge\")\n",
"fig.show()"
]
},
@@ -83642,7 +83899,7 @@
},
{
"cell_type": "code",
- "execution_count": 135,
+ "execution_count": 129,
"id": "1b7e6ff4",
"metadata": {},
"outputs": [
@@ -83666,13 +83923,12 @@
"showlegend": false,
"type": "scatter",
"x": [
- "Autre",
"F",
"M"
],
"xaxis": "x",
"y": {
- "bdata": "cwc5yD0iLD8/NYWiuKgsPzW0pZIvKy0/",
+ "bdata": "PzWForioLD+h9SRrpxMtPw==",
"dtype": "f8"
},
"yaxis": "y"
@@ -84459,7 +84715,7 @@
}
},
"title": {
- "text": "Fréquence de sinistres en fonction du genre du conducteur"
+ "text": "Sinistralité selon le genre"
},
"xaxis": {
"anchor": "y",
@@ -84468,7 +84724,7 @@
1
],
"title": {
- "text": "Genre du conducteur principal"
+ "text": "GENRE"
}
},
"yaxis": {
@@ -84478,7 +84734,7 @@
1
],
"title": {
- "text": "Fréquence"
+ "text": "FREQ"
}
}
}
@@ -84489,19 +84745,17 @@
}
],
"source": [
- "#Ecrivez votre code ici\n",
- "freq_by_genre = data_retraitee.groupby('GENRE').agg({\n",
- " 'NB': 'sum',\n",
- " 'EXPO': 'sum'\n",
- "}).reset_index()\n",
+ "#Agrégation selon la variable d'intérêt\n",
+ "plot_data = data_retraitee[[\"GENRE\", \"NB\",\"EXPO\"]]\n",
+ "plot_data = plot_data.groupby([\"GENRE\"], as_index=False).sum()\n",
"\n",
- "freq_by_genre['FREQ'] = freq_by_genre['NB'] / freq_by_genre['EXPO']\n",
- "freq_by_genre['FREQ'] = freq_by_genre['FREQ'].fillna(0)\n",
+ "#Calcul de la fréquence\n",
+ "plot_data[\"FREQ\"] = plot_data[\"NB\"] / plot_data[\"EXPO\"]\n",
+ "plot_data[\"FREQ\"] = plot_data[\"FREQ\"].fillna(0)\n",
+ "plot_data\n",
"\n",
- "fig = px.scatter(freq_by_genre, x='GENRE', y='FREQ',\n",
- " title='Fréquence de sinistres en fonction du genre du conducteur')\n",
- "fig.update_xaxes(title='Genre du conducteur principal')\n",
- "fig.update_yaxes(title='Fréquence')\n",
+ "#Représentation graphique\n",
+ "fig = px.scatter(plot_data, x=\"GENRE\", y=\"FREQ\", title=\"Sinistralité selon le genre\")\n",
"fig.show()"
]
},
@@ -84515,7 +84769,7 @@
},
{
"cell_type": "code",
- "execution_count": 127,
+ "execution_count": 130,
"id": "1ac452e1",
"metadata": {},
"outputs": [
@@ -84529,14 +84783,11 @@
{
"hovertemplate": "ZONE_RISQUE=%{x}
FREQ=%{y}",
"legendgroup": "",
- "line": {
- "color": "#636efa",
- "dash": "solid"
- },
"marker": {
+ "color": "#636efa",
"symbol": "circle"
},
- "mode": "lines",
+ "mode": "markers",
"name": "",
"orientation": "v",
"showlegend": false,
@@ -84551,7 +84802,6 @@
"G",
"H",
"I",
- "Inconnu",
"J",
"K",
"L",
@@ -84563,7 +84813,7 @@
],
"xaxis": "x",
"y": {
- "bdata": "7fsfMSF1Lz/GMPhorzEqP6CNXnecTCs/9h+qj1CmMT8RH5JNZgMjP95qXI+ZnBU/rNRBzalUKD/+84zJ2d4zP04cFEbgvzI/GmcBNp9xZj+6JAR/Ew87P95+F6UZkS4/P3VB4ilHMj8RJoGVljVQPwAAAAAAAAAAAAAAAAAAAACqGA56SYc1PwAAAAAAAAAA",
+ "bdata": "7fsfMSF1Lz/GMPhorzEqP6CNXnecTCs/9h+qj1CmMT8RH5JNZgMjP95qXI+ZnBU/rNRBzalUKD/+84zJ2d4zP04cFEbgvzI/uiQEfxMPOz/efhelGZEuPz91QeIpRzI/ESaBlZY1UD8AAAAAAAAAAAAAAAAAAAAAqhgOekmHNT8AAAAAAAAAAA==",
"dtype": "f8"
},
"yaxis": "y"
@@ -85350,7 +85600,7 @@
}
},
"title": {
- "text": "Fréquence de sinistres en fonction de la zone du risque du conducteur"
+ "text": "Sinistralité selon la zone géographique"
},
"xaxis": {
"anchor": "y",
@@ -85359,7 +85609,7 @@
1
],
"title": {
- "text": "Zone du risque du conducteur principal"
+ "text": "ZONE_RISQUE"
}
},
"yaxis": {
@@ -85369,7 +85619,7 @@
1
],
"title": {
- "text": "Fréquence"
+ "text": "FREQ"
}
}
}
@@ -85380,19 +85630,17 @@
}
],
"source": [
- "#Ecrivez votre code ici\n",
- "freq_by_zone_risque = data_retraitee.groupby('ZONE_RISQUE').agg({\n",
- " 'NB': 'sum',\n",
- " 'EXPO': 'sum'\n",
- "}).reset_index()\n",
+ "#Agrégation selon la variable d'intérêt\n",
+ "plot_data = data_retraitee[[\"ZONE_RISQUE\", \"NB\",\"EXPO\"]]\n",
+ "plot_data = plot_data.groupby([\"ZONE_RISQUE\"], as_index=False).sum()\n",
"\n",
- "freq_by_zone_risque['FREQ'] = freq_by_zone_risque['NB'] / freq_by_zone_risque['EXPO']\n",
- "freq_by_zone_risque['FREQ'] = freq_by_zone_risque['FREQ'].fillna(0)\n",
+ "#Calcul de la fréquence\n",
+ "plot_data[\"FREQ\"] = plot_data[\"NB\"] / plot_data[\"EXPO\"]\n",
+ "plot_data[\"FREQ\"] = plot_data[\"FREQ\"].fillna(0)\n",
+ "plot_data\n",
"\n",
- "fig = px.line(freq_by_zone_risque, x='ZONE_RISQUE', y='FREQ',\n",
- " title='Fréquence de sinistres en fonction de la zone du risque du conducteur')\n",
- "fig.update_xaxes(title='Zone du risque du conducteur principal')\n",
- "fig.update_yaxes(title='Fréquence')\n",
+ "#Représentation graphique\n",
+ "fig = px.scatter(plot_data, x=\"ZONE_RISQUE\", y=\"FREQ\", title=\"Sinistralité selon la zone géographique\")\n",
"fig.show()"
]
},
@@ -85406,7 +85654,7 @@
},
{
"cell_type": "code",
- "execution_count": 136,
+ "execution_count": 131,
"id": "2185245a",
"metadata": {},
"outputs": [
@@ -86223,7 +86471,7 @@
}
},
"title": {
- "text": "Fréquence de sinistres en fonction du carburant du véhicule"
+ "text": "Sinistralité selon le carburant"
},
"xaxis": {
"anchor": "y",
@@ -86232,7 +86480,7 @@
1
],
"title": {
- "text": "Carburant du véhicule"
+ "text": "ENERGIE"
}
},
"yaxis": {
@@ -86242,7 +86490,7 @@
1
],
"title": {
- "text": "Fréquence"
+ "text": "FREQ"
}
}
}
@@ -86253,19 +86501,17 @@
}
],
"source": [
- "#Ecrivez votre code ici\n",
- "freq_by_carburant = data_retraitee.groupby('ENERGIE').agg({\n",
- " 'NB': 'sum',\n",
- " 'EXPO': 'sum'\n",
- "}).reset_index()\n",
+ "#Agrégation selon la variable d'intérêt\n",
+ "plot_data = data_retraitee[[\"ENERGIE\", \"NB\",\"EXPO\"]]\n",
+ "plot_data = plot_data.groupby([\"ENERGIE\"], as_index=False).sum()\n",
"\n",
- "freq_by_carburant['FREQ'] = freq_by_carburant['NB'] / freq_by_carburant['EXPO']\n",
- "freq_by_carburant['FREQ'] = freq_by_carburant['FREQ'].fillna(0)\n",
+ "#Calcul de la fréquence\n",
+ "plot_data[\"FREQ\"] = plot_data[\"NB\"] / plot_data[\"EXPO\"]\n",
+ "plot_data[\"FREQ\"] = plot_data[\"FREQ\"].fillna(0)\n",
+ "plot_data\n",
"\n",
- "fig = px.scatter(freq_by_carburant, x='ENERGIE', y='FREQ',\n",
- " title='Fréquence de sinistres en fonction du carburant du véhicule')\n",
- "fig.update_xaxes(title='Carburant du véhicule')\n",
- "fig.update_yaxes(title='Fréquence')\n",
+ "#Représentation graphique\n",
+ "fig = px.scatter(plot_data, x=\"ENERGIE\", y=\"FREQ\", title=\"Sinistralité selon le carburant\")\n",
"fig.show()"
]
},
@@ -86279,7 +86525,7 @@
},
{
"cell_type": "code",
- "execution_count": 129,
+ "execution_count": 132,
"id": "2e2486e5",
"metadata": {},
"outputs": [
@@ -86291,22 +86537,18 @@
},
"data": [
{
- "hovertemplate": "VALEUR_DU_BIEN=%{x}
FREQ=%{y}",
+ "hovertemplate": "VALEUR_DU_BIEN=%{x}
CM=%{y}",
"legendgroup": "",
- "line": {
- "color": "#636efa",
- "dash": "solid"
- },
"marker": {
+ "color": "#636efa",
"symbol": "circle"
},
- "mode": "lines",
+ "mode": "markers",
"name": "",
"orientation": "v",
"showlegend": false,
"type": "scatter",
"x": [
- "Inconnu",
"[0;10000[",
"[10000;15000[",
"[15000;20000[",
@@ -86316,7 +86558,7 @@
],
"xaxis": "x",
"y": {
- "bdata": "Qmvvo5fWND+9UgyXc1EtP9ckegt22ys/sLntqmVlLD/ngWr+9N8nP017HFObVy0/3Z7CazI7Mz8=",
+ "bdata": "6Hme5zmVrkBAbCoN4/G2QACT8h640bZAkBgrdrWqukCC/ve0zAa1QNtZ7ax2f7pA",
"dtype": "f8"
},
"yaxis": "y"
@@ -87103,7 +87345,7 @@
}
},
"title": {
- "text": "Fréquence de sinistres en fonction du prix du bien"
+ "text": "Coût moyen selon le prix"
},
"xaxis": {
"anchor": "y",
@@ -87112,7 +87354,7 @@
1
],
"title": {
- "text": "Prix du bien"
+ "text": "VALEUR_DU_BIEN"
}
},
"yaxis": {
@@ -87122,7 +87364,7 @@
1
],
"title": {
- "text": "Fréquence"
+ "text": "CM"
}
}
}
@@ -87133,19 +87375,18 @@
}
],
"source": [
- "#Ecrivez votre code ici\n",
- "freq_by_prix = data_retraitee.groupby('VALEUR_DU_BIEN').agg({\n",
- " 'NB': 'sum',\n",
- " 'EXPO': 'sum'\n",
- "}).reset_index()\n",
+ "#Agrégation selon la variable d'intérêt\n",
+ "plot_data = data_retraitee[[\"VALEUR_DU_BIEN\", \"CHARGE\",\"NB\"]]\n",
+ "plot_data= plot_data[plot_data['CHARGE'] > 0]\n",
+ "plot_data = plot_data.groupby([\"VALEUR_DU_BIEN\"], as_index=False).sum()\n",
"\n",
- "freq_by_prix['FREQ'] = freq_by_prix['NB'] / freq_by_prix['EXPO']\n",
- "freq_by_prix['FREQ'] = freq_by_prix['FREQ'].fillna(0)\n",
+ "#Calcul du CM\n",
+ "plot_data[\"CM\"] = plot_data[\"CHARGE\"] / plot_data[\"NB\"]\n",
+ "plot_data[\"CM\"] = plot_data[\"CM\"].fillna(0)\n",
+ "plot_data\n",
"\n",
- "fig = px.line(freq_by_prix, x='VALEUR_DU_BIEN', y='FREQ',\n",
- " title='Fréquence de sinistres en fonction du prix du bien')\n",
- "fig.update_xaxes(title='Prix du bien')\n",
- "fig.update_yaxes(title='Fréquence')\n",
+ "#Représentation graphique\n",
+ "fig = px.scatter(plot_data, x=\"VALEUR_DU_BIEN\", y=\"CM\", title=\"Coût moyen selon le prix\")\n",
"fig.show()"
]
},
@@ -87159,7 +87400,7 @@
},
{
"cell_type": "code",
- "execution_count": 130,
+ "execution_count": 133,
"id": "63b97150",
"metadata": {},
"outputs": [
@@ -87171,7 +87412,7 @@
},
"data": [
{
- "hovertemplate": "CONTRAT_ANCIENNETE=%{x}
FREQ=%{y}",
+ "hovertemplate": "ANNEE_CONSTRUCTION=%{x}
CM=%{y}",
"legendgroup": "",
"line": {
"color": "#636efa",
@@ -87185,16 +87426,13 @@
"orientation": "v",
"showlegend": false,
"type": "scatter",
- "x": [
- "(-1,0]",
- "(0,1]",
- "(1,2]",
- "(2,5]",
- "(5,10]"
- ],
+ "x": {
+ "bdata": "AAAAAAA0n0AAAAAAADifQAAAAAAAPJ9AAAAAAABAn0AAAAAAAEifQAAAAAAAUJ9AAAAAAABUn0AAAAAAAFifQAAAAAAAXJ9AAAAAAABgn0AAAAAAAGSfQAAAAAAAaJ9AAAAAAABsn0AAAAAAAHCfQAAAAAAAdJ9AAAAAAAB4n0AAAAAAAHyfQAAAAAAAgJ9AAAAAAACEn0AAAAAAAIifQAAAAAAAjJ9AAAAAAACQn0AAAAAAAJSfQA==",
+ "dtype": "f8"
+ },
"xaxis": "x",
"y": {
- "bdata": "ZQsC467eLD+5zPFIPQovPx5bSaTVQS0/twWaF5bWKT8Mt4mFr9IlPw==",
+ "bdata": "AAAAAAAA8H/hehSuRzqxQFyPwvUojItAuB6F61EKrkBSuB6Fq2DMQDCW/GIJX7VA9ihcjzIk1ECX/GLJL47CQLHkF0sekalAuB6F6zmDu0B/5UuymBC2QBFvV92bEbRAozVao1WFuUC7SQwCq5GxQM3MzMxM+7NA3ebYbU7CuEBVL3AXhbS0QO/qKX1Pv7VA50hyhmpSuUCeitWpGF66QOBzS5x6KbhAUd0O1Q0UwEBkqOwwdeq5QA==",
"dtype": "f8"
},
"yaxis": "y"
@@ -87981,7 +88219,7 @@
}
},
"title": {
- "text": "Fréquence de sinistres en fonction de l'ancienneté du contrat"
+ "text": "Coût moyen selon l'ancienneté du bien"
},
"xaxis": {
"anchor": "y",
@@ -87990,7 +88228,7 @@
1
],
"title": {
- "text": "Ancienneté du contrat"
+ "text": "ANNEE_CONSTRUCTION"
}
},
"yaxis": {
@@ -88000,7 +88238,7 @@
1
],
"title": {
- "text": "Fréquence"
+ "text": "CM"
}
}
}
@@ -88011,19 +88249,18 @@
}
],
"source": [
- "#Ecrivez votre code ici\n",
- "freq_by_anciennete = data_retraitee.groupby('CONTRAT_ANCIENNETE').agg({\n",
- " 'NB': 'sum',\n",
- " 'EXPO': 'sum'\n",
- "}).reset_index()\n",
+ "#Agrégation selon la variable d'intérêt\n",
+ "plot_data = data_retraitee[[\"ANNEE_CONSTRUCTION\", \"CHARGE\",\"NB\"]]\n",
+ "plot_data= plot_data[plot_data['CHARGE'] > 0]\n",
+ "plot_data = plot_data.groupby([\"ANNEE_CONSTRUCTION\"], as_index=False).sum()\n",
"\n",
- "freq_by_anciennete['FREQ'] = freq_by_anciennete['NB'] / freq_by_anciennete['EXPO']\n",
- "freq_by_anciennete['FREQ'] = freq_by_anciennete['FREQ'].fillna(0)\n",
+ "#Calcul du CM\n",
+ "plot_data[\"CM\"] = plot_data[\"CHARGE\"] / plot_data[\"NB\"]\n",
+ "plot_data[\"CM\"] = plot_data[\"CM\"].fillna(0)\n",
+ "plot_data\n",
"\n",
- "fig = px.line(freq_by_anciennete, x='CONTRAT_ANCIENNETE', y='FREQ',\n",
- " title='Fréquence de sinistres en fonction de l\\'ancienneté du contrat')\n",
- "fig.update_xaxes(title='Ancienneté du contrat')\n",
- "fig.update_yaxes(title='Fréquence')\n",
+ "#Représentation graphique\n",
+ "fig = px.line(plot_data, x=\"ANNEE_CONSTRUCTION\", y=\"CM\", title=\"Coût moyen selon l'ancienneté du bien\")\n",
"fig.show()"
]
},
@@ -88037,7 +88274,7 @@
},
{
"cell_type": "code",
- "execution_count": 131,
+ "execution_count": 134,
"id": "d1506c6c",
"metadata": {},
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@@ -88049,30 +88286,24 @@
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CM=%{y}",
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@@ -88859,7 +89090,7 @@
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+ "text": "Coût moyen selon l'âge de l'assuré"
},
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@@ -88878,7 +89109,7 @@
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@@ -88889,19 +89120,18 @@
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"source": [
- "#Ecrivez votre code ici\n",
- "freq_by_age = data_retraitee.groupby('CONTRAT_ANCIENNETE').agg({\n",
- " 'NB': 'sum',\n",
- " 'EXPO': 'sum'\n",
- "}).reset_index()\n",
+ "#Agrégation selon la variable d'intérêt\n",
+ "plot_data = data_retraitee[[\"AGE_ASSURE_PRINCIPAL\", \"CHARGE\",\"NB\"]]\n",
+ "plot_data= plot_data[plot_data['CHARGE'] > 0]\n",
+ "plot_data = plot_data.groupby([\"AGE_ASSURE_PRINCIPAL\"], as_index=False).sum()\n",
"\n",
- "freq_by_age['FREQ'] = freq_by_age['NB'] / freq_by_age['EXPO']\n",
- "freq_by_age['FREQ'] = freq_by_age['FREQ'].fillna(0)\n",
+ "#Calcul du CM\n",
+ "plot_data[\"CM\"] = plot_data[\"CHARGE\"] / plot_data[\"NB\"]\n",
+ "plot_data[\"CM\"] = plot_data[\"CM\"].fillna(0)\n",
+ "plot_data\n",
"\n",
- "fig = px.line(freq_by_age, x='CONTRAT_ANCIENNETE', y='FREQ',\n",
- " title='Fréquence de sinistres en fonction de l\\'ancienneté du contrat')\n",
- "fig.update_xaxes(title='Ancienneté du contrat')\n",
- "fig.update_yaxes(title='Fréquence')\n",
+ "#Représentation graphique\n",
+ "fig = px.scatter(plot_data, x=\"AGE_ASSURE_PRINCIPAL\", y=\"CM\", title=\"Coût moyen selon l'âge de l'assuré\")\n",
"fig.show()"
]
},
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"source": [
- "#Ecrivez votre code ici"
+ "#Agrégation selon la variable d'intérêt\n",
+ "plot_data = data_retraitee[[\"GENRE\", \"CHARGE\",\"NB\"]]\n",
+ "plot_data= plot_data[plot_data['CHARGE'] > 0]\n",
+ "plot_data = plot_data.groupby([\"GENRE\"], as_index=False).sum()\n",
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+ "#Calcul du CM\n",
+ "plot_data[\"CM\"] = plot_data[\"CHARGE\"] / plot_data[\"NB\"]\n",
+ "plot_data[\"CM\"] = plot_data[\"CM\"].fillna(0)\n",
+ "plot_data\n",
+ "\n",
+ "#Représentation graphique\n",
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+ "fig.show()"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e497e4b5",
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
diff --git a/M2/Machine Learning/TP_1/2_outputs/base_retraitee.csv b/M2/Machine Learning/TP_1/2_outputs/base_retraitee.csv
index 5693ee1..74c5c28 100644
--- a/M2/Machine Learning/TP_1/2_outputs/base_retraitee.csv
+++ b/M2/Machine Learning/TP_1/2_outputs/base_retraitee.csv
@@ -1,14265 +1,14265 @@
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