diff --git a/translations/it/.co-op-translator.json b/translations/it/.co-op-translator.json
index d04519da1..d0c276b16 100644
--- a/translations/it/.co-op-translator.json
+++ b/translations/it/.co-op-translator.json
@@ -210,8 +210,8 @@
"language_code": "it"
},
"4-Classification/3-Classifiers-2/solution/notebook.ipynb": {
- "original_hash": "70f41fe4fd4253adb44cd9d291406e4f",
- "translation_date": "2026-02-28T08:46:07+00:00",
+ "original_hash": "382c1f542f31fcc58137ce6d14751413",
+ "translation_date": "2026-04-24T11:08:03+00:00",
"source_file": "4-Classification/3-Classifiers-2/solution/notebook.ipynb",
"language_code": "it"
},
diff --git a/translations/it/4-Classification/3-Classifiers-2/solution/notebook.ipynb b/translations/it/4-Classification/3-Classifiers-2/solution/notebook.ipynb
index 676864b84..a3c939929 100644
--- a/translations/it/4-Classification/3-Classifiers-2/solution/notebook.ipynb
+++ b/translations/it/4-Classification/3-Classifiers-2/solution/notebook.ipynb
@@ -2,51 +2,28 @@
"cells": [
{
"source": [
- "# Costruire Altri Modelli di Classificazione\n"
+ "# Costruisci Altri Modelli di Classificazione\n"
],
"cell_type": "markdown",
"metadata": {}
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Panoramica del Dataset\n",
+ "Questo dataset contiene singoli campioni (ad esempio, ricette) etichettati per cucina.\n",
+ "Ogni riga corrisponde a un singolo campione/record, e le colonne rappresentano ingredienti o altri attributi utilizzati per la classificazione, inclusa l'etichetta `cuisine`.\n"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- " Unnamed: 0 cuisine almond angelica anise anise_seed apple \\\n",
- "0 0 indian 0 0 0 0 0 \n",
- "1 1 indian 1 0 0 0 0 \n",
- "2 2 indian 0 0 0 0 0 \n",
- "3 3 indian 0 0 0 0 0 \n",
- "4 4 indian 0 0 0 0 0 \n",
- "\n",
- " apple_brandy apricot armagnac ... whiskey white_bread white_wine \\\n",
- "0 0 0 0 ... 0 0 0 \n",
- "1 0 0 0 ... 0 0 0 \n",
- "2 0 0 0 ... 0 0 0 \n",
- "3 0 0 0 ... 0 0 0 \n",
- "4 0 0 0 ... 0 0 0 \n",
- "\n",
- " whole_grain_wheat_flour wine wood yam yeast yogurt zucchini \n",
- "0 0 0 0 0 0 0 0 \n",
- "1 0 0 0 0 0 0 0 \n",
- "2 0 0 0 0 0 0 0 \n",
- "3 0 0 0 0 0 0 0 \n",
- "4 0 0 0 0 0 1 0 \n",
- "\n",
- "[5 rows x 382 columns]"
- ],
- "text/html": "
\n\n
\n \n \n | \n Unnamed: 0 | \n cuisine | \n almond | \n angelica | \n anise | \n anise_seed | \n apple | \n apple_brandy | \n apricot | \n armagnac | \n ... | \n whiskey | \n white_bread | \n white_wine | \n whole_grain_wheat_flour | \n wine | \n wood | \n yam | \n yeast | \n yogurt | \n zucchini | \n
\n \n \n \n | 0 | \n 0 | \n indian | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n ... | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n
\n \n | 1 | \n 1 | \n indian | \n 1 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n ... | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n
\n \n | 2 | \n 2 | \n indian | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n ... | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n
\n \n | 3 | \n 3 | \n indian | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n ... | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n
\n \n | 4 | \n 4 | \n indian | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n ... | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 1 | \n 0 | \n
\n \n
\n
5 rows × 382 columns
\n
"
- },
- "metadata": {},
- "execution_count": 1
- }
- ],
+ "outputs": [],
"source": [
"import pandas as pd\n",
+ "# Load dataset containing cuisine features\n",
"cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
"cuisines_df.head()"
]
@@ -263,7 +240,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "---\n\n\n**Disclaimer**: \nQuesto documento è stato tradotto utilizzando il servizio di traduzione automatica AI [Co-op Translator](https://github.com/Azure/co-op-translator). Pur impegnandoci per garantire l’accuratezza, si prega di considerare che le traduzioni automatiche possono contenere errori o imprecisioni. Il documento originale nella sua lingua natale deve essere considerato la fonte autorevole. Per informazioni critiche, si raccomanda la traduzione professionale effettuata da un traduttore umano. Non siamo responsabili per eventuali incomprensioni o interpretazioni errate derivanti dall’uso di questa traduzione.\n\n"
+ "---\n\n\n**Disclaimer**: \nQuesto documento è stato tradotto utilizzando il servizio di traduzione automatica [Co-op Translator](https://github.com/Azure/co-op-translator). Pur impegnandoci per l’accuratezza, si prega di considerare che le traduzioni automatiche possono contenere errori o inesattezze. Il documento originale nella sua lingua nativa deve essere considerato la fonte autorevole. Per informazioni critiche, è consigliata una traduzione professionale effettuata da un umano. Non siamo responsabili per eventuali malintesi o interpretazioni errate derivanti dall’uso di questa traduzione.\n\n"
]
}
],
diff --git a/translations/pl/.co-op-translator.json b/translations/pl/.co-op-translator.json
index 7f35c2146..a63e416d0 100644
--- a/translations/pl/.co-op-translator.json
+++ b/translations/pl/.co-op-translator.json
@@ -210,8 +210,8 @@
"language_code": "pl"
},
"4-Classification/3-Classifiers-2/solution/notebook.ipynb": {
- "original_hash": "70f41fe4fd4253adb44cd9d291406e4f",
- "translation_date": "2026-02-28T08:46:21+00:00",
+ "original_hash": "382c1f542f31fcc58137ce6d14751413",
+ "translation_date": "2026-04-24T11:08:14+00:00",
"source_file": "4-Classification/3-Classifiers-2/solution/notebook.ipynb",
"language_code": "pl"
},
diff --git a/translations/pl/4-Classification/3-Classifiers-2/solution/notebook.ipynb b/translations/pl/4-Classification/3-Classifiers-2/solution/notebook.ipynb
index e3967d0a2..bcfbfa2c8 100644
--- a/translations/pl/4-Classification/3-Classifiers-2/solution/notebook.ipynb
+++ b/translations/pl/4-Classification/3-Classifiers-2/solution/notebook.ipynb
@@ -2,51 +2,28 @@
"cells": [
{
"source": [
- "# Buduj więcej modeli klasyfikacyjnych\n"
+ "# Zbuduj więcej modeli klasyfikacji\n"
],
"cell_type": "markdown",
"metadata": {}
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Przegląd zbioru danych\n",
+ "Ten zbiór danych zawiera pojedyncze próbki (na przykład przepisy) oznaczone według kuchni.\n",
+ "Każdy wiersz odpowiada pojedynczej próbce/rekordowi, a kolumny reprezentują składniki lub inne atrybuty używane do klasyfikacji, w tym etykietę `cuisine`.\n"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- " Unnamed: 0 cuisine almond angelica anise anise_seed apple \\\n",
- "0 0 indian 0 0 0 0 0 \n",
- "1 1 indian 1 0 0 0 0 \n",
- "2 2 indian 0 0 0 0 0 \n",
- "3 3 indian 0 0 0 0 0 \n",
- "4 4 indian 0 0 0 0 0 \n",
- "\n",
- " apple_brandy apricot armagnac ... whiskey white_bread white_wine \\\n",
- "0 0 0 0 ... 0 0 0 \n",
- "1 0 0 0 ... 0 0 0 \n",
- "2 0 0 0 ... 0 0 0 \n",
- "3 0 0 0 ... 0 0 0 \n",
- "4 0 0 0 ... 0 0 0 \n",
- "\n",
- " whole_grain_wheat_flour wine wood yam yeast yogurt zucchini \n",
- "0 0 0 0 0 0 0 0 \n",
- "1 0 0 0 0 0 0 0 \n",
- "2 0 0 0 0 0 0 0 \n",
- "3 0 0 0 0 0 0 0 \n",
- "4 0 0 0 0 0 1 0 \n",
- "\n",
- "[5 rows x 382 columns]"
- ],
- "text/html": "\n\n
\n \n \n | \n Unnamed: 0 | \n cuisine | \n almond | \n angelica | \n anise | \n anise_seed | \n apple | \n apple_brandy | \n apricot | \n armagnac | \n ... | \n whiskey | \n white_bread | \n white_wine | \n whole_grain_wheat_flour | \n wine | \n wood | \n yam | \n yeast | \n yogurt | \n zucchini | \n
\n \n \n \n | 0 | \n 0 | \n indian | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n ... | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n
\n \n | 1 | \n 1 | \n indian | \n 1 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n ... | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n
\n \n | 2 | \n 2 | \n indian | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n ... | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n
\n \n | 3 | \n 3 | \n indian | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n ... | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n
\n \n | 4 | \n 4 | \n indian | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n ... | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 1 | \n 0 | \n
\n \n
\n
5 rows × 382 columns
\n
"
- },
- "metadata": {},
- "execution_count": 1
- }
- ],
+ "outputs": [],
"source": [
"import pandas as pd\n",
+ "# Load dataset containing cuisine features\n",
"cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
"cuisines_df.head()"
]
@@ -263,7 +240,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "---\n\n\n**Zastrzeżenie**:\nNiniejszy dokument został przetłumaczony przy użyciu automatycznej usługi tłumaczeniowej [Co-op Translator](https://github.com/Azure/co-op-translator). Chociaż dokładamy starań, aby tłumaczenie było jak najbardziej precyzyjne, należy mieć na uwadze, że automatyczne przekłady mogą zawierać błędy lub nieścisłości. Oryginalny dokument w języku źródłowym należy uznać za wersję autorytatywną. W przypadku informacji o krytycznym znaczeniu zaleca się skorzystanie z profesjonalnego tłumaczenia wykonanego przez człowieka. Nie ponosimy odpowiedzialności za jakiekolwiek nieporozumienia lub błędne interpretacje wynikające z korzystania z tego tłumaczenia.\n\n"
+ "---\n\n\n**Zastrzeżenie**:\nDokument ten został przetłumaczony za pomocą usługi tłumaczeń AI [Co-op Translator](https://github.com/Azure/co-op-translator). Chociaż dążymy do dokładności, prosimy pamiętać, że tłumaczenia automatyczne mogą zawierać błędy lub nieścisłości. Oryginalny dokument w języku źródłowym powinien być uznawany za autorytatywne źródło. W przypadku istotnych informacji zalecane jest skorzystanie z profesjonalnego tłumaczenia ludzkiego. Nie ponosimy odpowiedzialności za jakiekolwiek nieporozumienia lub błędne interpretacje wynikające z użycia tego tłumaczenia.\n\n"
]
}
],
diff --git a/translations/tr/.co-op-translator.json b/translations/tr/.co-op-translator.json
index 3d4f2403a..44f0555b5 100644
--- a/translations/tr/.co-op-translator.json
+++ b/translations/tr/.co-op-translator.json
@@ -210,8 +210,8 @@
"language_code": "tr"
},
"4-Classification/3-Classifiers-2/solution/notebook.ipynb": {
- "original_hash": "70f41fe4fd4253adb44cd9d291406e4f",
- "translation_date": "2026-02-28T08:46:31+00:00",
+ "original_hash": "382c1f542f31fcc58137ce6d14751413",
+ "translation_date": "2026-04-24T11:08:23+00:00",
"source_file": "4-Classification/3-Classifiers-2/solution/notebook.ipynb",
"language_code": "tr"
},
diff --git a/translations/tr/4-Classification/3-Classifiers-2/solution/notebook.ipynb b/translations/tr/4-Classification/3-Classifiers-2/solution/notebook.ipynb
index e7981f014..96b28b94a 100644
--- a/translations/tr/4-Classification/3-Classifiers-2/solution/notebook.ipynb
+++ b/translations/tr/4-Classification/3-Classifiers-2/solution/notebook.ipynb
@@ -7,46 +7,23 @@
"cell_type": "markdown",
"metadata": {}
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Veri Seti Genel Bakışı\n",
+ "Bu veri seti, mutfak türüne göre etiketlenmiş bireysel örnekler (örneğin, tarifler) içerir.\n",
+ "Her satır tek bir örnek/kayıda karşılık gelir ve sütunlar, sınıflandırma için kullanılan malzemeler veya diğer özellikleri, `cuisine` etiketini içerecek şekilde temsil eder.\n"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- " Unnamed: 0 cuisine almond angelica anise anise_seed apple \\\n",
- "0 0 indian 0 0 0 0 0 \n",
- "1 1 indian 1 0 0 0 0 \n",
- "2 2 indian 0 0 0 0 0 \n",
- "3 3 indian 0 0 0 0 0 \n",
- "4 4 indian 0 0 0 0 0 \n",
- "\n",
- " apple_brandy apricot armagnac ... whiskey white_bread white_wine \\\n",
- "0 0 0 0 ... 0 0 0 \n",
- "1 0 0 0 ... 0 0 0 \n",
- "2 0 0 0 ... 0 0 0 \n",
- "3 0 0 0 ... 0 0 0 \n",
- "4 0 0 0 ... 0 0 0 \n",
- "\n",
- " whole_grain_wheat_flour wine wood yam yeast yogurt zucchini \n",
- "0 0 0 0 0 0 0 0 \n",
- "1 0 0 0 0 0 0 0 \n",
- "2 0 0 0 0 0 0 0 \n",
- "3 0 0 0 0 0 0 0 \n",
- "4 0 0 0 0 0 1 0 \n",
- "\n",
- "[5 rows x 382 columns]"
- ],
- "text/html": "\n\n
\n \n \n | \n Unnamed: 0 | \n cuisine | \n almond | \n angelica | \n anise | \n anise_seed | \n apple | \n apple_brandy | \n apricot | \n armagnac | \n ... | \n whiskey | \n white_bread | \n white_wine | \n whole_grain_wheat_flour | \n wine | \n wood | \n yam | \n yeast | \n yogurt | \n zucchini | \n
\n \n \n \n | 0 | \n 0 | \n indian | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n ... | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n
\n \n | 1 | \n 1 | \n indian | \n 1 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n ... | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n
\n \n | 2 | \n 2 | \n indian | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n ... | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n
\n \n | 3 | \n 3 | \n indian | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n ... | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n
\n \n | 4 | \n 4 | \n indian | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n ... | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 0 | \n 1 | \n 0 | \n
\n \n
\n
5 rows × 382 columns
\n
"
- },
- "metadata": {},
- "execution_count": 1
- }
- ],
+ "outputs": [],
"source": [
"import pandas as pd\n",
+ "# Load dataset containing cuisine features\n",
"cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
"cuisines_df.head()"
]
@@ -124,7 +101,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Farklı sınıflandırıcıları dene\n"
+ "# Farklı sınıflandırıcılar deneyin\n"
]
},
{
@@ -263,7 +240,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "---\n\n\n**Feragatname**:\nBu belge, AI çeviri servisi [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluk için çaba gösterilmekle birlikte, otomatik çevirilerin hata veya yanlışlık içerebileceğini lütfen unutmayın. Orijinal belge, kendi dilinde yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımı sonucu oluşabilecek herhangi bir yanlış anlama veya yorum hatasından sorumlu tutulamayız.\n\n"
+ "---\n\n\n**Feragatname**: \nBu belge, AI çeviri servisi [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluk için çaba sarf edilse de, otomatik çevirilerin hatalar veya yanlışlıklar içerebileceğini lütfen unutmayın. Orijinal belge, kendi dilinde yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımı sonucu ortaya çıkabilecek yanlış anlamalar veya yorum farklılıklarından sorumlu tutulamayız.\n\n"
]
}
],