nb_wineclassification.ipynb

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   "id": "6d0f67c1-8c3e-41c2-877f-b7ebd49ac4d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e020721a-ef0d-4e81-a8d9-ae9a2cdb0cba",
   "metadata": {},
   "outputs": [],
   "source": [
    "wine = datasets.load_wine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f763bce7-1ea0-4d3a-afe8-cf6f4ff7faf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR', 'data', 'feature_names', 'frame', 'target', 'target_names']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ac50396e-501f-4030-a23c-e4e9e0a7a5eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.423e+01, 1.710e+00, 2.430e+00, 1.560e+01, 1.270e+02, 2.800e+00,\n",
       "       3.060e+00, 2.800e-01, 2.290e+00, 5.640e+00, 1.040e+00, 3.920e+00,\n",
       "       1.065e+03])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c9c0bc21-9e18-4af9-adba-906d9c2be5fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alcohol',\n",
       " 'malic_acid',\n",
       " 'ash',\n",
       " 'alcalinity_of_ash',\n",
       " 'magnesium',\n",
       " 'total_phenols',\n",
       " 'flavanoids',\n",
       " 'nonflavanoid_phenols',\n",
       " 'proanthocyanins',\n",
       " 'color_intensity',\n",
       " 'hue',\n",
       " 'od280/od315_of_diluted_wines',\n",
       " 'proline']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2df7d457-95ce-4b15-89e4-fdf9df72018a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(wine.data, columns = wine.feature_names)\n",
    "df['type'] = wine.target\n",
    "input = df.drop(['type'], axis =1)\n",
    "target = df.type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "01a8bf38-20b1-4cea-85b7-f96f255a071f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.3, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b1196279-7aa3-41a5-a612-2fa9e4ee0938",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kf = KFold(n_splits = 3)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "f40e1b0c-5ce1-436b-9de8-4e2aa2822290",
   "metadata": {},
   "source": [
    "for train_index, test_index in kf.split(df.drop(['type'],df.type):\n",
    "    X_train, X_test, y_train, y_test = digits.data[train_index], digits.data[test_index],digits.target[train_index], digits.target[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e6cf81c1-3acd-4eda-a3c4-d53ab107a301",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB, MultinomialNB\n",
    "model = GaussianNB()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "593e2d1c-9ce2-4eb7-8251-3448d29849db",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "acce478d-7adb-43f2-befa-dbd73d68bbcb",
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     "execution_count": 34,
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   "source": [
    "scores_gauss = cross_val_score(GaussianNB(),input, target, cv=10)\n",
    "np.average(scores_gauss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0bcae00e-6a30-4167-9b30-03516ae03e87",
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     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "scores_gauss = cross_val_score(MultinomialNB(),input, target, cv=10)\n",
    "np.average(scores_gauss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9f7a302-2cd0-448c-be17-c3f75a46e80e",
   "metadata": {},
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   "source": []
  }
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