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   "source": [
    "# Bucătării Delicioase Asiatice și Indiene\n",
    "\n",
    "## Introducere\n",
    "\n",
    "Bucătăriile asiatice și indiene sunt cunoscute pentru aromele lor bogate, utilizarea condimentelor și diversitatea incredibilă. Fiecare regiune are propriile sale rețete unice, care reflectă cultura și tradițiile locale.\n",
    "\n",
    "## Ce face aceste bucătării speciale?\n",
    "\n",
    "- **Condimentele**: Multe feluri de mâncare asiatice și indiene se bazează pe o combinație complexă de condimente, cum ar fi turmeric, coriandru, chimen și ghimbir.\n",
    "- **Tehnici de gătit**: De la prăjire rapidă în wok până la gătitul lent în tandoor, metodele de preparare sunt variate și fascinante.\n",
    "- **Ingrediente proaspete**: Legumele, ierburile și proteinele proaspete sunt esențiale pentru a crea mâncăruri autentice.\n",
    "\n",
    "## Feluri de mâncare populare\n",
    "\n",
    "### Bucătăria Indiană\n",
    "\n",
    "1. **Butter Chicken**: Un preparat cremos și aromat, făcut cu pui gătit într-un sos bogat de roșii și smântână.\n",
    "2. **Biryani**: Orez aromat combinat cu carne, legume și un amestec de condimente.\n",
    "3. **Samosa**: Pachețele crocante umplute cu cartofi, mazăre și condimente.\n",
    "\n",
    "### Bucătăria Asiatică\n",
    "\n",
    "1. **Pad Thai**: Tăiței de orez prăjiți cu ouă, tofu, creveți și o combinație de sosuri dulci și acrișoare.\n",
    "2. **Sushi**: O combinație de orez, pește crud și alte ingrediente, servite sub formă de rulouri sau bucăți mici.\n",
    "3. **Dumplings**: Găluște umplute cu carne, legume sau o combinație a acestora, gătite la abur sau prăjite.\n",
    "\n",
    "## Sfaturi pentru a găti acasă\n",
    "\n",
    "- **Planifică din timp**: Asigură-te că ai toate ingredientele necesare înainte de a începe.\n",
    "- **Nu te teme de condimente**: Experimentează cu diferite combinații pentru a găsi echilibrul perfect.\n",
    "- **Folosește ustensilele potrivite**: De exemplu, un wok este ideal pentru prăjirea rapidă.\n",
    "\n",
    "[!TIP] Dacă ești începător, începe cu rețete simple și crește treptat nivelul de dificultate.\n",
    "\n",
    "## Concluzie\n",
    "\n",
    "Bucătăriile asiatice și indiene oferă o experiență culinară de neuitat. Fie că ești un pasionat al mâncărurilor picante sau preferi arome mai subtile, există ceva pentru toată lumea. Încearcă să gătești acasă și descoperă magia acestor bucătării!\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "Instalează Imblearn, care va permite utilizarea SMOTE. Acesta este un pachet Scikit-learn care ajută la gestionarea datelor dezechilibrate în timpul clasificării. (https://imbalanced-learn.org/stable/)\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Requirement already satisfied: imblearn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.0)\n",
      "Requirement already satisfied: imbalanced-learn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imblearn) (0.8.0)\n",
      "Requirement already satisfied: numpy>=1.13.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.19.2)\n",
      "Requirement already satisfied: scipy>=0.19.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.4.1)\n",
      "Requirement already satisfied: scikit-learn>=0.24 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.24.2)\n",
      "Requirement already satisfied: joblib>=0.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.16.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.24->imbalanced-learn->imblearn) (2.1.0)\n",
      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install imblearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df  = pd.read_csv('../../data/cuisines.csv')"
   ]
  },
  {
   "source": [
    "Acest set de date include 385 de coloane care indică toate tipurile de ingrediente din diverse bucătării dintr-un set dat de bucătării.\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
       "0          65  indian       0         0      0           0      0   \n",
       "1          66  indian       1         0      0           0      0   \n",
       "2          67  indian       0         0      0           0      0   \n",
       "3          68  indian       0         0      0           0      0   \n",
       "4          69  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 385 columns]"
      ],
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     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2448 entries, 0 to 2447\nColumns: 385 entries, Unnamed: 0 to zucchini\ndtypes: int64(384), object(1)\nmemory usage: 7.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "korean      799\n",
       "indian      598\n",
       "chinese     442\n",
       "japanese    320\n",
       "thai        289\n",
       "Name: cuisine, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "df.cuisine.value_counts()"
   ]
  },
  {
   "source": [
    "Arată bucătăriile într-un grafic cu bare\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d18799710>"
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     },
     "metadata": {},
     "execution_count": 7
    },
    {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "df.cuisine.value_counts().plot.barh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "thai df: (289, 385)\njapanese df: (320, 385)\nchinese df: (442, 385)\nindian df: (598, 385)\nkorean df: (799, 385)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "thai_df = df[(df.cuisine == \"thai\")]\n",
    "japanese_df = df[(df.cuisine == \"japanese\")]\n",
    "chinese_df = df[(df.cuisine == \"chinese\")]\n",
    "indian_df = df[(df.cuisine == \"indian\")]\n",
    "korean_df = df[(df.cuisine == \"korean\")]\n",
    "\n",
    "print(f'thai df: {thai_df.shape}')\n",
    "print(f'japanese df: {japanese_df.shape}')\n",
    "print(f'chinese df: {chinese_df.shape}')\n",
    "print(f'indian df: {indian_df.shape}')\n",
    "print(f'korean df: {korean_df.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_ingredient_df(df):\n",
    "    # transpose df, drop cuisine and unnamed rows, sum the row to get total for ingredient and add value header to new df\n",
    "    ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')\n",
    "    # drop ingredients that have a 0 sum\n",
    "    ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]\n",
    "    # sort df\n",
    "    ingredient_df = ingredient_df.sort_values(by='value', ascending=False, inplace=False)\n",
    "    return ingredient_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d5917db00>"
      ]
     },
     "metadata": {},
     "execution_count": 10
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "thai_ingredient_df = create_ingredient_df(thai_df)\r\n",
    "thai_ingredient_df.head(10).plot.barh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d389634a8>"
      ]
     },
     "metadata": {},
     "execution_count": 11
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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   "source": [
    "japanese_ingredient_df = create_ingredient_df(japanese_df)\r\n",
    "japanese_ingredient_df.head(10).plot.barh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d18817f98>"
      ]
     },
     "metadata": {},
     "execution_count": 12
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "chinese_ingredient_df = create_ingredient_df(chinese_df)\r\n",
    "chinese_ingredient_df.head(10).plot.barh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d1880a550>"
      ]
     },
     "metadata": {},
     "execution_count": 13
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "indian_ingredient_df = create_ingredient_df(indian_df)\r\n",
    "indian_ingredient_df.head(10).plot.barh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d1896ce80>"
      ]
     },
     "metadata": {},
     "execution_count": 14
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "korean_ingredient_df = create_ingredient_df(korean_df)\r\n",
    "korean_ingredient_df.head(10).plot.barh()"
   ]
  },
  {
   "source": [],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
       "0       0         0      0           0      0             0        0   \n",
       "1       1         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             0        0   \n",
       "\n",
       "   armagnac  artemisia  artichoke  ...  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 380 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "feature_df= df.drop(['cuisine','Unnamed: 0','rice','garlic','ginger'], axis=1)\n",
    "labels_df = df.cuisine #.unique()\n",
    "feature_df.head()\n"
   ]
  },
  {
   "source": [
    "Echilibrează datele cu supraeșantionarea SMOTE la clasa cea mai mare. Citește mai multe aici: https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTE.html\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "oversample = SMOTE()\n",
    "transformed_feature_df, transformed_label_df = oversample.fit_resample(feature_df, labels_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "new label count: korean      799\nchinese     799\njapanese    799\nindian      799\nthai        799\nName: cuisine, dtype: int64\nold label count: korean      799\nindian      598\nchinese     442\njapanese    320\nthai        289\nName: cuisine, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(f'new label count: {transformed_label_df.value_counts()}')\r\n",
    "print(f'old label count: {df.cuisine.value_counts()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
       "0       0         0      0           0      0             0        0   \n",
       "1       1         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             0        0   \n",
       "\n",
       "   armagnac  artemisia  artichoke  ...  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 380 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "transformed_feature_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     cuisine  almond  angelica  anise  anise_seed  apple  apple_brandy  \\\n",
       "0     indian       0         0      0           0      0             0   \n",
       "1     indian       1         0      0           0      0             0   \n",
       "2     indian       0         0      0           0      0             0   \n",
       "3     indian       0         0      0           0      0             0   \n",
       "4     indian       0         0      0           0      0             0   \n",
       "...      ...     ...       ...    ...         ...    ...           ...   \n",
       "3990    thai       0         0      0           0      0             0   \n",
       "3991    thai       0         0      0           0      0             0   \n",
       "3992    thai       0         0      0           0      0             0   \n",
       "3993    thai       0         0      0           0      0             0   \n",
       "3994    thai       0         0      0           0      0             0   \n",
       "\n",
       "      apricot  armagnac  artemisia  ...  whiskey  white_bread  white_wine  \\\n",
       "0           0         0          0  ...        0            0           0   \n",
       "1           0         0          0  ...        0            0           0   \n",
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       "3           0         0          0  ...        0            0           0   \n",
       "4           0         0          0  ...        0            0           0   \n",
       "...       ...       ...        ...  ...      ...          ...         ...   \n",
       "3990        0         0          0  ...        0            0           0   \n",
       "3991        0         0          0  ...        0            0           0   \n",
       "3992        0         0          0  ...        0            0           0   \n",
       "3993        0         0          0  ...        0            0           0   \n",
       "3994        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",
       "3990                        0     0     0    0      0       0         0  \n",
       "3991                        0     0     0    0      0       0         0  \n",
       "3992                        0     0     0    0      0       0         0  \n",
       "3993                        0     0     0    0      0       0         0  \n",
       "3994                        0     0     0    0      0       0         0  \n",
       "\n",
       "[3995 rows x 381 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3990</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3991</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3992</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3993</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3994</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>3995 rows × 381 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "# export transformed data to new df for classification\n",
    "transformed_df = pd.concat([transformed_label_df,transformed_feature_df],axis=1, join='outer')\n",
    "transformed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 3995 entries, 0 to 3994\nColumns: 381 entries, cuisine to zucchini\ndtypes: int64(380), object(1)\nmemory usage: 11.6+ MB\n"
     ]
    }
   ],
   "source": [
    "transformed_df.info()"
   ]
  },
  {
   "source": [],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "transformed_df.to_csv(\"../../data/cleaned_cuisines.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n---\n\n**Declinarea responsabilității**:  \nAcest document a fost tradus folosind serviciul de traducere AI [Co-op Translator](https://github.com/Azure/co-op-translator). Deși depunem eforturi pentru a asigura acuratețea, vă rugăm să aveți în vedere că traducerile automate pot conține erori sau inexactități. Documentul original în limba sa nativă ar trebui considerat sursa autoritară. Pentru informații critice, se recomandă traducerea profesională realizată de un specialist uman. Nu ne asumăm răspunderea pentru eventualele neînțelegeri sau interpretări greșite care pot apărea din utilizarea acestei traduceri.\n"
   ]
  }
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