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Refactor code in Jupyter notebooks for clarity and consistency

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- Set execution_count to null for specific code cells in 2025_TP_1_M2_ISF.ipynb to reset execution state.
- Replace output display of DataFrames with print statements in 2025_TP_1_M2_ISF.ipynb for better visibility during execution.
- Clean up import statements in 2025_TP_2_M2_ISF.ipynb by adding noqa comments for better linting and readability.
This commit is contained in:
Arthur Danjou
2025-10-08 10:24:51 +02:00
parent b6cfa3349e
commit 185de1142d
2 changed files with 18 additions and 19 deletions
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26
M2/Machine Learning/TP_1/2025_TP_1_M2_ISF.ipynb
View File

@@ -83899,7 +83899,7 @@
},
{
"cell_type": "code",
"execution_count": 129,
"execution_count": null,
"id": "1b7e6ff4",
"metadata": {},
"outputs": [
@@ -84752,7 +84752,7 @@
"#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",
"print(plot_data)\n",
"\n",
"#Représentation graphique\n",
"fig = px.scatter(plot_data, x=\"GENRE\", y=\"FREQ\", title=\"Sinistralité selon le genre\")\n",
@@ -84793,7 +84793,7 @@
"#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",
"print(plot_data)\n",
"\n",
"#Représentation graphique\n",
"fig = px.scatter(plot_data, x=\"ZONE_RISQUE\", y=\"FREQ\", title=\"Sinistralité selon la zone géographique\")\n",
@@ -84810,7 +84810,7 @@
},
{
"cell_type": "code",
"execution_count": 131,
"execution_count": null,
"id": "2185245a",
"metadata": {},
"outputs": [
@@ -85664,7 +85664,7 @@
"#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",
"print(plot_data)\n",
"\n",
"#Représentation graphique\n",
"fig = px.scatter(plot_data, x=\"ENERGIE\", y=\"FREQ\", title=\"Sinistralité selon le carburant\")\n",
@@ -85681,7 +85681,7 @@
},
{
"cell_type": "code",
"execution_count": 132,
"execution_count": null,
"id": "2e2486e5",
"metadata": {},
"outputs": [
@@ -86539,7 +86539,7 @@
"#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",
"print(plot_data)\n",
"\n",
"#Représentation graphique\n",
"fig = px.scatter(plot_data, x=\"VALEUR_DU_BIEN\", y=\"CM\", title=\"Coût moyen selon le prix\")\n",
@@ -86556,7 +86556,7 @@
},
{
"cell_type": "code",
"execution_count": 133,
"execution_count": null,
"id": "63b97150",
"metadata": {},
"outputs": [
@@ -87413,7 +87413,7 @@
"#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",
"print(plot_data)\n",
"\n",
"#Représentation graphique\n",
"fig = px.line(plot_data, x=\"ANNEE_CONSTRUCTION\", y=\"CM\", title=\"Coût moyen selon l'ancienneté du bien\")\n",
@@ -87430,7 +87430,7 @@
},
{
"cell_type": "code",
"execution_count": 134,
"execution_count": null,
"id": "d1506c6c",
"metadata": {},
"outputs": [
@@ -88284,7 +88284,7 @@
"#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",
"print(plot_data)\n",
"\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",
@@ -88301,7 +88301,7 @@
},
{
"cell_type": "code",
"execution_count": 135,
"execution_count": null,
"id": "9f19cc0d",
"metadata": {},
"outputs": [
@@ -89155,7 +89155,7 @@
"#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",
"print(plot_data)\n",
"\n",
"#Représentation graphique\n",
"fig = px.scatter(plot_data, x=\"GENRE\", y=\"CM\", title=\"Coût moyen selon l'âge de l'assuré\")\n",

11
M2/Machine Learning/TP_2/2025_TP_2_M2_ISF.ipynb
View File

@@ -43,7 +43,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"id": "f6e62631",
"metadata": {},
"outputs": [],
@@ -56,15 +56,14 @@
"import seaborn as sns\n",
"\n",
"sns.set()\n",
"import matplotlib.pyplot as plt\n",
"import plotly.graph_objects as gp\n",
"from scipy.cluster.hierarchy import dendrogram, linkage\n",
"import matplotlib.pyplot as plt # noqa: E402\n",
"from scipy.cluster.hierarchy import dendrogram, linkage # noqa: E402\n",
"\n",
"# Statistiques\n",
"from scipy.stats import chi2_contingency\n",
"from scipy.stats import chi2_contingency # noqa: E402, F401\n",
"\n",
"# Machine Learning\n",
"from sklearn.cluster import AgglomerativeClustering, KMeans\n"
"from sklearn.cluster import AgglomerativeClustering, KMeans # noqa: E402\n"
]
},
{