Ridge_Regression.ipynb

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    "# RIDGE REGRESSION\n",
    "# using sklearn\n",
    "# importing required libraries\n",
    "\n",
    "from pandas import read_csv\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preparing data\n",
    "\n",
    "filename = 'bengin_traffic.csv'\n",
    "dataset = read_csv(filename)\n",
    "array = dataset.values\n",
    "x = array[:,0:114]\n",
    "y = array[:,114]\n",
    "kfold = KFold(n_splits=10, random_state=7)"
   ]
  },
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   "cell_type": "code",
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     "text": [
      "D:\\Anaconda\\lib\\site-packages\\scipy\\linalg\\basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.0179422709452416e-38 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "D:\\Anaconda\\lib\\site-packages\\scipy\\linalg\\basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.943134319610105e-39 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "D:\\Anaconda\\lib\\site-packages\\scipy\\linalg\\basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.993765874159209e-39 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "D:\\Anaconda\\lib\\site-packages\\scipy\\linalg\\basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.2016356671337441e-38 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "D:\\Anaconda\\lib\\site-packages\\scipy\\linalg\\basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.0001542103101952e-38 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "D:\\Anaconda\\lib\\site-packages\\scipy\\linalg\\basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.965826362831957e-39 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "D:\\Anaconda\\lib\\site-packages\\scipy\\linalg\\basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.0793402418363916e-38 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "D:\\Anaconda\\lib\\site-packages\\scipy\\linalg\\basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.3712740188055169e-38 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "D:\\Anaconda\\lib\\site-packages\\scipy\\linalg\\basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.338115471244365e-38 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "D:\\Anaconda\\lib\\site-packages\\scipy\\linalg\\basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.99922960479372e-39 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.00380045 -0.05219156 -0.00110011 -0.00132814 -0.00090993 -0.00122645\n",
      " -0.00105076 -0.00102128 -0.00160116 -0.00097983]\n",
      "-0.006520967860826639\n"
     ]
    }
   ],
   "source": [
    "# the real deal\n",
    "\n",
    "model = Ridge()\n",
    "scoring = 'neg_mean_squared_error'\n",
    "results = cross_val_score(model, x, y, cv=kfold, scoring=scoring)\n",
    "print(results)\n",
    "print(results.mean())"
   ]
  }
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