Ridge regression on the data.ipynb

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   "source": [
    "import pickle\n",
    "from sklearn.linear_model import Ridge\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>MEDIC_PROB</th>\n",
       "      <th>INJURY_SURGERY</th>\n",
       "      <th>DRUG_HISTORY</th>\n",
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       "        AGE  SEX  MEDIC_PROB  INJURY_SURGERY  DRUG_HISTORY       AVG\n",
       "0  0.000000  0.0         0.0             0.0           0.0  1.000000\n",
       "1  0.125000  1.0         0.0             0.0           0.0  0.616764\n",
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     "execution_count": 6,
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   "source": [
    "# get the pre-processed data.\n",
    "with open('data_processed_scaled.pickle', 'rb') as f:\n",
    "    data = pickle.load(f)\n",
    "    \n",
    "# dropping category since it is only necessary for calssification\n",
    "data.drop('CATEGORY', axis=1, inplace=True)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   "outputs": [],
   "source": [
    "# segregate inputs and results\n",
    "X = data\n",
    "Y = data['AVG']\n",
    "\n",
    "# drop the avg from training model\n",
    "X.drop('AVG', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "(37, 5)"
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# divide data into testing and training models\n",
    "from sklearn import model_selection\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X,Y)\n",
    "X_train.shape"
   ]
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   "execution_count": null,
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