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Ajout de l'exécution de cellules pour le One Hot Encoding, la normalisation des variables numériques et la séparation des données en ensembles d'apprentissage et de test.

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Arthur Danjou
2025-10-13 18:24:13 +02:00
parent d5ac1ff381
commit af98a95bea
1 changed files with 19 additions and 7 deletions
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26
M2/Machine Learning/TP_3/2025_TP_3_M2_ISF.ipynb
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@@ -2104,7 +2104,7 @@
"\n",
"# Visualiser avec Plotly\n",
"fig = px.imshow(correlation_cat,\n",
" text_auto='.2f',\n",
" text_auto='.2f', # type: ignore\n",
" aspect=\"auto\",\n",
" color_continuous_scale='RdBu_r',\n",
" title='Matrice de corrélation des variables catégorielles (V de Cramér)')\n",
@@ -3066,11 +3066,16 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 38,
"id": "b8530717",
"metadata": {},
"outputs": [],
"source": []
"source": [
"encoder = preproc.OneHotEncoder(sparse_output=False, drop='first')\n",
"encoder.fit(vars_categorielles)\n",
"vars_categorielles_enc = encoder.transform(vars_categorielles)\n",
"vars_categorielles_enc = pd.DataFrame(vars_categorielles_enc, columns=encoder.get_feature_names_out()) # type: ignore"
]
},
{
"cell_type": "markdown",
@@ -3082,11 +3087,16 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 39,
"id": "4ff3847d",
"metadata": {},
"outputs": [],
"source": []
"source": [
"scaler = preproc.StandardScaler()\n",
"scaler.fit(vars_numeriques)\n",
"vars_numeriques_scaled = scaler.transform(vars_numeriques)\n",
"vars_numeriques_scaled = pd.DataFrame(vars_numeriques_scaled, columns=vars_numeriques.columns)"
]
},
{
"cell_type": "markdown",
@@ -3106,11 +3116,13 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 40,
"id": "6a1c7907",
"metadata": {},
"outputs": [],
"source": []
"source": [
"train, test = train_test_split(data_model, test_size=0.2, random_state=42)"
]
},
{
"cell_type": "markdown",