Assignment 01 Classification Wine Quality.ipynb

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   "source": [
    "# Predict Wine Quality using Logistic Regression"
   ]
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   "source": [
    "Question: Predict Wine Quality using Wine Quality dataset. You can download dataset directly from Kaggle or load from sklearn library.  \n",
    "\n",
    "Hint: Loading from sklearm library and save into a pandas dataframe  \n",
    "\n",
    "from sklearn.datasets import load_wine  \n",
    "import pandas as pd  \n",
    "wine = load_wine()  \n",
    "df = pd.DataFrame(wine.data) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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   "source": [
    "from sklearn.datasets import load_wine\n",
    "import pandas as pd\n",
    "wine = load_wine()\n",
    "df = pd.DataFrame(wine.data)"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
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     "name": "stdout",
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     "text": [
      "['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline']\n"
     ]
    }
   ],
   "source": [
    "print(wine.feature_names)\n",
    "df.columns = wine.feature_names\n",
    "\n",
    "df['Wine_Quality'] = wine.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().values.any())\n",
    "X = df.iloc[:, :-1].values\n",
    "y = df.iloc[:, -1].values\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 0)\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_X = StandardScaler()\n",
    "X_train = sc_X.fit_transform(X_train)\n",
    "X_test = sc_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[20  0  0]\n",
      " [ 0 24  0]\n",
      " [ 0  0 15]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "classifier = LogisticRegression(random_state = 0)\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "y_pred = classifier.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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