main.ipynb

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     "text": [
      "0.00984440500563\n",
      "2.36577968333\n",
      "[ 5.11136902]\n",
      "quality    5.0\n",
      "Name: 0, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.linear_model import ElasticNetCV\n",
    "\n",
    "df = pd.read_csv('winequality-red.csv')\n",
    "train_label = df[1:][['fixed acidity', 'volatile acidity', 'citric acid', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol']]\n",
    "X = train_label.values\n",
    "train_result = df[1:][['quality']]\n",
    "y = train_result.values.flatten()\n",
    "test_label = df.iloc[0][['fixed acidity', 'volatile acidity', 'citric acid', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol']].values\n",
    "test_result = df.iloc[0][['quality']]\n",
    "\n",
    "# alpha 1.0 ElasticNet\n",
    "# regr = ElasticNet(alpha=.1)\n",
    "# regr.fit(X, y)\n",
    "# print(regr.coef_)\n",
    "# print(regr.intercept_)\n",
    "# print(regr.predict([test_label]))\n",
    "# print(test_result)\n",
    "\n",
    "# gridsearch for alpha\n",
    "regr = ElasticNetCV(cv=5, random_state=0)\n",
    "regr.fit(X, y)\n",
    "print(regr.alpha_)\n",
    "print(regr.intercept_)\n",
    "print(regr.predict([test_label]))\n",
    "print(test_result)"
   ]
  }
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https://archive.ics.uci.edu/ml/datasets/Wine