covid prediction-final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import warnings\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "warnings.filterwarnings(action='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>county</th>\n",
       "      <th>state</th>\n",
       "      <th>fips</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>Snohomish</td>\n",
       "      <td>Washington</td>\n",
       "      <td>53061.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Snohomish</td>\n",
       "      <td>Washington</td>\n",
       "      <td>53061.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>Snohomish</td>\n",
       "      <td>Washington</td>\n",
       "      <td>53061.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>Cook</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>17031.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>Snohomish</td>\n",
       "      <td>Washington</td>\n",
       "      <td>53061.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date     county       state     fips  cases  deaths\n",
       "0  2020-01-21  Snohomish  Washington  53061.0      1     0.0\n",
       "1  2020-01-22  Snohomish  Washington  53061.0      1     0.0\n",
       "2  2020-01-23  Snohomish  Washington  53061.0      1     0.0\n",
       "3  2020-01-24       Cook    Illinois  17031.0      1     0.0\n",
       "4  2020-01-24  Snohomish  Washington  53061.0      1     0.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load dataset and display top 5 rows\n",
    "\n",
    "df_covid=pd.read_csv(\"us_counties_covid19_daily.csv\")\n",
    "df_covid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>county</th>\n",
       "      <th>state</th>\n",
       "      <th>fips</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>800432</th>\n",
       "      <td>2020-12-05</td>\n",
       "      <td>Sweetwater</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>56037.0</td>\n",
       "      <td>2098</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800433</th>\n",
       "      <td>2020-12-05</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>56039.0</td>\n",
       "      <td>1739</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800434</th>\n",
       "      <td>2020-12-05</td>\n",
       "      <td>Uinta</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>56041.0</td>\n",
       "      <td>1187</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800435</th>\n",
       "      <td>2020-12-05</td>\n",
       "      <td>Washakie</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>56043.0</td>\n",
       "      <td>519</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800436</th>\n",
       "      <td>2020-12-05</td>\n",
       "      <td>Weston</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>56045.0</td>\n",
       "      <td>419</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              date      county    state     fips  cases  deaths\n",
       "800432  2020-12-05  Sweetwater  Wyoming  56037.0   2098    10.0\n",
       "800433  2020-12-05       Teton  Wyoming  56039.0   1739     2.0\n",
       "800434  2020-12-05       Uinta  Wyoming  56041.0   1187     5.0\n",
       "800435  2020-12-05    Washakie  Wyoming  56043.0    519     8.0\n",
       "800436  2020-12-05      Weston  Wyoming  56045.0    419     2.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_covid.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date          0\n",
       "county        0\n",
       "state         0\n",
       "fips       7591\n",
       "cases         0\n",
       "deaths    16733\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Discover missing values\n",
    "\n",
    "df_covid.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values with median\n",
    "\n",
    "# method = 'pad' 바로 앞에꺼 값을 결측치로 대체하는 것\n",
    "# mean 보다 median이 이상치의 영향을 덜 받는 특징때문에 median을 사용했다.\n",
    "\n",
    "df_covid['fips'] = df_covid['fips'].fillna(method = 'pad')# The values of 'fips' are mostly similiar to values next to each other \n",
    "df_covid['deaths'] = df_covid['deaths'].fillna(df_covid['deaths'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 시각화를 위해서 결측치 제거전 df를  미리 만들어 놓기 \n",
    "df = df_covid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop death, fips variable ( death는 예측하려는 변수가 아니여서 제거, fips는 state와 일치하는 변수임으로 제거)\n",
    "df_covid = df_covid.drop('deaths', axis=1, inplace=False)\n",
    "df_covid = df_covid.drop('fips', axis=1, inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date      0\n",
       "county    0\n",
       "state     0\n",
       "cases     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check missing values again\n",
    "\n",
    "df_covid.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 800437 entries, 0 to 800436\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count   Dtype \n",
      "---  ------  --------------   ----- \n",
      " 0   date    800437 non-null  object\n",
      " 1   county  800437 non-null  object\n",
      " 2   state   800437 non-null  object\n",
      " 3   cases   800437 non-null  int64 \n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 24.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# Discover data type\n",
    "\n",
    "df_covid.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>800437.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1589.667358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8385.201329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>175.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>775.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>439538.000000</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "               cases\n",
       "count  800437.000000\n",
       "mean     1589.667358\n",
       "std      8385.201329\n",
       "min         0.000000\n",
       "25%        29.000000\n",
       "50%       175.000000\n",
       "75%       775.000000\n",
       "max    439538.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_covid.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            1\n",
       "1            1\n",
       "2            1\n",
       "3            1\n",
       "4            1\n",
       "          ... \n",
       "800432    2098\n",
       "800433    1739\n",
       "800434    1187\n",
       "800435     519\n",
       "800436     419\n",
       "Name: cases, Length: 800437, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_covid.cases\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Target: covid cases'}, xlabel='cases', ylabel='Density'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Hku4H/gR8TNIA8HznqjX2lcdRJvTJYypmZkmzU98vBo4H5kfEi8CzwMJOVmysK7dO+vvk7i8zs6TZlgrAkWTPq+TLXNPm+owbpVxLxd1fZmaZZqe+vxY4HLgT2JlWBz0dKtm7WypmZsOabanMB+aFBw+GlC9Ff58895eZWdLs3V/3AC/pZEXGmxhqqfS5pWJmljTbUjkIuFfS7cD28sqIeE9HajUOeEzFzKxWs6FyUScrMR55TMXMrFZToRIRv5D0MmBuRNwqaW+gv7NVG9uGWir9fk7FzKys2bm//hr4HvDNtGoW8KMO1WlcyA/U56fBNzPrZc0O1J8PvAl4GiAi1gMzOlWp8aDc/TXB3V9mZkOaDZXtEfFC+UN6ALKnf5OWhloqfR6oNzNLmg2VX0j6HLCXpLcD3wV+PFohSadIWidpg6TFdbZL0mVp+12Sjh6trKTpkm6RtD69H5DWv13SHZLuTu8nNnluhQwP1Pv7VMzMypoNlcXAIHA38FHgJuC/NSogqR+4AlgAzAPOkDSvarcFwNz0Ohe4somyi4EVETEXWJE+A2wF3h0RrwYWAdc2eW6FRAR9gj65+8vMrKzZu79Kkn4E/CgiBps89jHAhojYCCDperJJKO/N7bMQuCY9qb9S0jRJM4E5DcouBE5I5ZcCPwcujIg1ueOuBaZImhwR2+mAiCxQJD+nYmZW1rClkrqnLpK0FbgfWCdpUNIXmjj2LODh3OdNaV0z+zQqe3BEbAZI7/VuGHgfsKZeoEg6V9JqSasHB5vNx1qlCCTok7+ky8ysbLTur0+Q3fX1xog4MCKmA8cCb5L0t6OUVZ111b99R9qnmbL1f6h0FHApWTdd7UEiroqI+RExf2BgoJlD1lUKkESfPPeXmVnZaKFyJnBGRPy+vCJ1SX04bWtkE3Bo7vNs4NEm92lU9rHURUZ631LeSdJs4IfAmRHxwCj1a8nwmArsdP+XmRkweqhMjIit1SvTuMrEOvvnrQLmSjpM0iTgdGBZ1T7LgDNTN9txwFOpS6tR2WVkA/Gk9xsBJE0Dfgp8NiJ+NUrdWlaKoC+1VNz9ZWaWGW2g/oWC24iIHZIuAJaTTelydUSslXRe2r6E7C6yU4ENwHPAWY3KpkNfAtwg6WzgIeD9af0FwBHA5yV9Pq07OSKGWjLtVEoD9X2SWypmZsloofJaSU/XWS9gymgHj4ibyIIjv25JbjnIntZvqmxa/zhwUp31XwS+OFqd2mVooL4PXtzpUDEzg1FCJSJ6etLIRiLXUnH3l5lZptmHH61KKQ3U+zkVM7NhDpWChgfqPU2LmVmZQ6Wg/HMqbqmYmWUcKgVF4CfqzcyqOFQKCo+pmJnVcKgU5DEVM7NaDpWCSr6l2MyshkOloOFZit39ZWZW5lApaPj7VDxQb2ZW5lApqJT75kdniplZxqFS0PCYilsqZmZlDpWCKsdUHCpmZuBQKSzSLcWSKJW6XRszs7HBoVJQqTT8RL2fUzEzyzhUCgry3/zY7dqYmY0NDpWChiaU7PNAvZlZmUOlIM/9ZWZWy6FSUP6WYo+pmJllHCoF5R9+dPeXmVnGoVKQv6TLzKyWQ6Wg4TEVD9SbmZU5VAoa/j4Vz/1lZlbmUCmoVPLcX2Zm1RwqBXnuLzOzWg6VgoJsPMXPqZiZDXOoFBT+jnozsxoOlYIqv6O+27UxMxsbHCoFDY+peKDezKzMoVJQaeg76rNbit0FZmbmUCksctO0AO4CMzPDoVJYKTdQX/5sZtbrHCoFZd/8KPr6yi0Vh4qZmUOloFJV95czxczMoVJY5L5PBdxSMTMDh0ph+Wlass9drpCZ2RjgUCkoKN9SnH12S8XMDCZ0uwLjVbmlsuahJwH47qpN7DWpf2j7h459aZdqZmbWPR1tqUg6RdI6SRskLa6zXZIuS9vvknT0aGUlTZd0i6T16f2AtP5AST+TtE3S5Z08LxgeUym3VPzwo5lZB0NFUj9wBbAAmAecIWle1W4LgLnpdS5wZRNlFwMrImIusCJ9Bnge+DzwqU6dU15p6Jsf05jK7vihZmZjXCdbKscAGyJiY0S8AFwPLKzaZyFwTWRWAtMkzRyl7EJgaVpeCpwGEBHPRsQvycKl48oPP6aGilsqZmZ0NlRmAQ/nPm9K65rZp1HZgyNiM0B6n7ErlZJ0rqTVklYPDg7uStEK5Ycfh7q/Ch/JzGzP0clQUZ111b97R9qnmbKFRMRVETE/IuYPDAy0cpzs4Uf88KOZWVknQ2UTcGju82zg0Sb3aVT2sdRFRnrf0sY6N63kgXozsxqdDJVVwFxJh0maBJwOLKvaZxlwZroL7DjgqdSl1ajsMmBRWl4E3NjBcxhRKYK+Ptz9ZWaW07HnVCJih6QLgOVAP3B1RKyVdF7avgS4CTgV2AA8B5zVqGw69CXADZLOBh4C3l/+mZIeBPYDJkk6DTg5Iu7txPmVIo2puPvLzGxIRx9+jIibyIIjv25JbjmA85stm9Y/Dpw0Qpk5LVR3FwUCd3+ZmeV4mpaCasZUulsdM7MxwaFS0NDDj+7+MjMb4lApqFSKyudUnCpmZg6Voobn/kotlS7Xx8xsLHCoFDTc/ZVxQ8XMzKFSWCmgry8/UO9UMTNzqBRU/j4VD9SbmQ1zqBRU+30q3a2PmdlY4FApaPj7VLLP7v4yM3OoFFaKQHiaFjOzPIdKQQGVLRWnipmZQ6WIiCDCX9JlZlbNoVJAuVHSl5uluORUMTNzqBRRSqnigXozs0oOlQLKrZKKhx+dKWZmDpUiyi0VP/xoZlbJoVJAfkylz91fZmZDHCoFVIyppHVuqZiZOVQKGQ6V3NT3ThUzM4dKEeWBennuLzOzCg6VAsqtEpEbqO9ifczMxgqHSgHDA/WepsXMLM+hUsDQmEruORU/UW9m5lAppHJMxd1fZmZlDpUCou4txY4VMzOHSgGl/ISSnqXYzGyIQ6WA/MOPE/qyS/jijlI3q2RmNiY4VAoYnvtL7L/XRPr7xNZtL3S5VmZm3edQKSA/91d/nzhwn0kMPvN8dytlZjYGOFQKyHd/AcyYOpktz2zvYo3MzMYGh0oBw7cUZ+8DU6fwxLMvsGOnx1XMrLc5VAqI3ISSAANTJxPA1mc9rmJmvc2hUkD+4UfIur8ABt0FZmY9zqFSQFSNqRy072QEbPFgvZn1OIdKAfmHHwEmTehj2t4T3VIxs57nUCmg+u4vyMZVNj/1vKdrMbOe5lApIP/wY9mRM/dj8Jnt3Lbx8W5Vy8ys6yZ0uwLjUVR1fwEcM2c6929+hpvv+SMH7Tu5SzUzM+uujrZUJJ0iaZ2kDZIW19kuSZel7XdJOnq0spKmS7pF0vr0fkBu22fT/uskvaNT51Wv+0sS73vDbKbvPYn//esH+ei1q1lx32M898KOTlXDzGzM6VhLRVI/cAXwdmATsErSsoi4N7fbAmBueh0LXAkcO0rZxcCKiLgkhc1i4EJJ84DTgaOAQ4BbJb08Ina2+9z6JKZOmcDE/spM3nfyBC448Qh+8btBVm58guVrH6O/Txw+sA8v2X8vDp46mRn7TWbqlIlMnTKBqVMmsl9633fyBCb2iwl9ffT3iwl95Vf2OZtmfzjFco2kGvltjcrkP+a78qoPLdXfz8ysWie7v44BNkTERgBJ1wMLgXyoLASuiWx0e6WkaZJmAnMalF0InJDKLwV+DlyY1l8fEduB30vakOpwW7tP7FWz9ufui7KG0HW/eahi28T+Pt525MGc8IoBNg4+yx8ef5Y/Pr2dB7Zs486H/oNt23fssd8SWRlm+fWVQVS5Lb9+5NQbqUx1OWee2egEnPKqmfzzB17b9mN3MlRmAQ/nPm8ia42Mts+sUcoeHBGbASJis6QZuWOtrHOsCpLOBc5NH7dJWtfsCY3gIGBri8fYk/h61PI1qeVrUmu3XpN7ga9+sHDxl420oZOhUu9vxuq/0Ufap5myRX4eEXEVcNUox2qapNURMb9dxxvvfD1q+ZrU8jWptadck04O1G8CDs19ng082uQ+jco+lrrISO9bduHnmZlZB3UyVFYBcyUdJmkS2SD6sqp9lgFnprvAjgOeSl1bjcouAxal5UXAjbn1p0uaLOkwssH/2zt1cmZmVqtj3V8RsUPSBcByoB+4OiLWSjovbV8C3AScCmwAngPOalQ2HfoS4AZJZwMPAe9PZdZKuoGsq3AHcH4n7vyqo21daXsIX49avia1fE1q7RHXRJ5WxMzM2sXTtJiZWds4VMzMrG0cKgWNNgXNeCPpaklbJN2TW7fLU+JIeoOku9O2y5SefEw3UHwnrf+NpDm5MovSz1gvqXwTRtdJOlTSzyTdJ2mtpP+a1vfkdZE0RdLtkn6brsfFaX1PXo88Sf2S1kj6Sfrcu9ckIvzaxRfZzQMPAH8GTAJ+C8zrdr1aPKc/B44G7smt+zKwOC0vBi5Ny/PSOU8GDkvXoj9tux04nuy5oZuBBWn9x4Alafl04DtpeTqwMb0fkJYP6Pb1SHWbCRydlqcCv0vn3pPXJdV937Q8EfgNcFyvXo+qa/NJ4DrgJ73+/07X/zHG4yv9wy/Pff4s8Nlu16sN5zWHylBZB8xMyzOBdfXOl+wuvePTPvfn1p8BfDO/T1qeQPbksPL7pG3fBM7o9rUY4frcSDYfXc9fF2Bv4N/JZrro6etB9kzcCuBEhkOlZ6+Ju7+KGWl6mT1NxZQ4QH5KnJGm19lUZ31FmYjYATwFHNjgWGNK6nJ4Pdlf5z17XVI3z51kDx3fEhE9fT2SrwOfAUq5dT17TRwqxRSZRmZPUmR6nXZOybNbSdoX+D7wiYh4utGuddbtUdclInZGxOvI/jo/RtKrGuy+x18PSe8CtkTEHc0WqbNuj7omDpViemVKmF2dEmdTWq5eX1FG0gRgf+CJBscaEyRNJAuUf42IH6TVPX9dIuJJshnCT6G3r8ebgPdIehC4HjhR0rfo5WvS7f638fgi69fcSDbQVh6oP6rb9WrDec2hckzln6gcbPxyWj6KysHGjQwPNq4iG7wtDzaemtafT+Vg4w1peTrwe7KBxgPS8vRuX4tUNwHXAF+vWt+T1wUYAKal5b2A/we8q1evR53rcwLDYyo9e026/g8xXl9k08v8juzujb/vdn3acD7fBjYDL5L9BXQ2Wb/tCmB9ep+e2//v07mvI92lktbPB+5J2y5neNaGKcB3yabkuR34s1yZj6T1G4Czun0tcvV6M1l3wl3Anel1aq9eF+A1wJp0Pe4BvpDW9+T1qHN9TmA4VHr2mniaFjMzaxuPqZiZWds4VMzMrG0cKmZm1jYOFTMzaxuHipmZtY1DxczM2sahYmZmbeNQMdsNJJ0p6a70XSTXSnp3+m6MNZJulXRw2u+tku5MrzWSpqb1n5a0Kh2j/D0m+0j6aTrmPZI+2M1zNINsuhEz6yBJR5E9Rf2miNgqaTrZk/rHRURIOodsltu/Az4FnB8Rv0oTWT4v6WRgLnAM2RQeyyT9Odm0KY9GxDvTz9l/t5+cWRW3VMw670TgexGxFSAiniCb/G+5pLuBT5PNCQXwK+Crkj5ONs/WDuDk9FpD9h0mryQLmbuBt0m6VNJbIuKp3XlSZvU4VMw6T9ROSf4vwOUR8Wrgo2TzOxERlwDnkE3YuFLSK1P5L0XE69LriIj4nxHxO+ANZOHyJUlf2E3nYzYih4pZ560APiDpQMi+v5xs+vJH0vZF5R0lHR4Rd0fEpcBqslbJcuAjqTsMSbMkzZB0CPBcRHwL+ArZ10GbdZXHVMw6LCLWSvoH4BeSdpJ1Y10EfFfSI8BKsmnQAT4h6S+AncC9wM0RsV3SkcBtkgC2AR8GjgD+SVKJbHbpv9mNp2VWl2cpNjOztnH3l5mZtY1DxczM2sahYmZmbeNQMTOztnGomJlZ2zhUzMysbRwqZmbWNv8fu3BLEjDvZHMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Original target variable histogram\n",
    "\n",
    "plt.title('Target: covid cases')\n",
    "sns.distplot(df_covid['cases'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Log Transformed Target: covid cases'}, xlabel='cases', ylabel='Density'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Log transformed target variable histogram\n",
    "\n",
    "log_covid = np.log1p(df_covid['cases'])\n",
    "\n",
    "plt.title('Log Transformed Target: covid cases')\n",
    "sns.distplot(log_covid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Discover monthly covid cases\n",
    "\n",
    "df_date = df_covid[['date', 'cases']].copy()\n",
    "df_date.date = pd.to_datetime(df_date.date) \n",
    "df_date.set_index('date', inplace=True)\n",
    "df_date = df_date.resample('MS').mean().reset_index()\n",
    "\n",
    "plt.title(\"Monthly covid cases \")\n",
    "labels = df_date.date.dt.to_period('M')\n",
    "date_plot = sns.pointplot(data=df_date, x='date', y='cases').set_xticklabels(rotation=90, labels=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig,(ax1,ax2,ax3)= plt.subplots(nrows=3)\n",
    "fig.set_size_inches(15,8)\n",
    "\n",
    "sns.scatterplot(data=df_date, x='date', y='cases', ax=ax1).set_xticklabels(rotation=90, labels=labels)\n",
    "\n",
    "sns.scatterplot(data=df_county, x='county', y='cases', ax=ax2)..set_xticklabels(rotation=90, labels=labels)\n",
    "\n",
    "sns.scatterplot(data=df_state, x='state', y='cases', hue='state', ax=ax3).set_xticklabels(rotation=90, labels=labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fips 와 death의 변수의 제거가 맞는지 확인하기 위해서 그래프를 그렸다.\n",
    "\n",
    "sns.heatmap(df.corr(), annot = True, cmap = 'viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fips 와 death는 case 변수와의 상관관계가 각각 -0.045,-0.02 로 매우 낮음을 보여주기 때문에 제거했음이 맞음을 알 수 있다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test data transformation (get dummy variables from date variable)\n",
    "\n",
    "def preprocess(df):\n",
    "    # To right date format\n",
    "    df2 = df.copy()\n",
    "    df2.date = df2.date.apply(pd.to_datetime)\n",
    "    df2['year'] = df2.date.apply(lambda x: x.year)\n",
    "    df2['month'] = df2.date.apply(lambda x: x.month)\n",
    "    df2['day'] = df2.date.apply(lambda x: x.day)\n",
    "    df2.drop(['date'], axis=1, inplace=True)\n",
    "    \n",
    "    #original_target = df2['cases']\n",
    "    #df2['case'] = np.log1p(df2['cases'])\n",
    "    \n",
    "    # Transform dummy variable\n",
    "    df2 = pd.get_dummies(df2, columns=['year', 'month', 'day','county','state'])\n",
    "    \n",
    "    return df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>county</th>\n",
       "      <th>state</th>\n",
       "      <th>fips</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>Snohomish</td>\n",
       "      <td>Washington</td>\n",
       "      <td>53061.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Snohomish</td>\n",
       "      <td>Washington</td>\n",
       "      <td>53061.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>Snohomish</td>\n",
       "      <td>Washington</td>\n",
       "      <td>53061.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>Cook</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>17031.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>Snohomish</td>\n",
       "      <td>Washington</td>\n",
       "      <td>53061.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800432</th>\n",
       "      <td>2020-12-05</td>\n",
       "      <td>Sweetwater</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>56037.0</td>\n",
       "      <td>2098</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800433</th>\n",
       "      <td>2020-12-05</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>56039.0</td>\n",
       "      <td>1739</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800434</th>\n",
       "      <td>2020-12-05</td>\n",
       "      <td>Uinta</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>56041.0</td>\n",
       "      <td>1187</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800435</th>\n",
       "      <td>2020-12-05</td>\n",
       "      <td>Washakie</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>56043.0</td>\n",
       "      <td>519</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800436</th>\n",
       "      <td>2020-12-05</td>\n",
       "      <td>Weston</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>56045.0</td>\n",
       "      <td>419</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>800437 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              date      county       state     fips  cases  deaths\n",
       "0       2020-01-21   Snohomish  Washington  53061.0      1     0.0\n",
       "1       2020-01-22   Snohomish  Washington  53061.0      1     0.0\n",
       "2       2020-01-23   Snohomish  Washington  53061.0      1     0.0\n",
       "3       2020-01-24        Cook    Illinois  17031.0      1     0.0\n",
       "4       2020-01-24   Snohomish  Washington  53061.0      1     0.0\n",
       "...            ...         ...         ...      ...    ...     ...\n",
       "800432  2020-12-05  Sweetwater     Wyoming  56037.0   2098    10.0\n",
       "800433  2020-12-05       Teton     Wyoming  56039.0   1739     2.0\n",
       "800434  2020-12-05       Uinta     Wyoming  56041.0   1187     5.0\n",
       "800435  2020-12-05    Washakie     Wyoming  56043.0    519     8.0\n",
       "800436  2020-12-05      Weston     Wyoming  56045.0    419     2.0\n",
       "\n",
       "[800437 rows x 6 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_covid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
       "<p>800437 rows × 2031 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           fips  cases  deaths  year_2020  month_1  month_2  month_3  month_4  \\\n",
       "0       53061.0      1     0.0          1        1        0        0        0   \n",
       "1       53061.0      1     0.0          1        1        0        0        0   \n",
       "2       53061.0      1     0.0          1        1        0        0        0   \n",
       "3       17031.0      1     0.0          1        1        0        0        0   \n",
       "4       53061.0      1     0.0          1        1        0        0        0   \n",
       "...         ...    ...     ...        ...      ...      ...      ...      ...   \n",
       "800432  56037.0   2098    10.0          1        0        0        0        0   \n",
       "800433  56039.0   1739     2.0          1        0        0        0        0   \n",
       "800434  56041.0   1187     5.0          1        0        0        0        0   \n",
       "800435  56043.0    519     8.0          1        0        0        0        0   \n",
       "800436  56045.0    419     2.0          1        0        0        0        0   \n",
       "\n",
       "        month_5  month_6  ...  state_Tennessee  state_Texas  state_Utah  \\\n",
       "0             0        0  ...                0            0           0   \n",
       "1             0        0  ...                0            0           0   \n",
       "2             0        0  ...                0            0           0   \n",
       "3             0        0  ...                0            0           0   \n",
       "4             0        0  ...                0            0           0   \n",
       "...         ...      ...  ...              ...          ...         ...   \n",
       "800432        0        0  ...                0            0           0   \n",
       "800433        0        0  ...                0            0           0   \n",
       "800434        0        0  ...                0            0           0   \n",
       "800435        0        0  ...                0            0           0   \n",
       "800436        0        0  ...                0            0           0   \n",
       "\n",
       "        state_Vermont  state_Virgin Islands  state_Virginia  state_Washington  \\\n",
       "0                   0                     0               0                 1   \n",
       "1                   0                     0               0                 1   \n",
       "2                   0                     0               0                 1   \n",
       "3                   0                     0               0                 0   \n",
       "4                   0                     0               0                 1   \n",
       "...               ...                   ...             ...               ...   \n",
       "800432              0                     0               0                 0   \n",
       "800433              0                     0               0                 0   \n",
       "800434              0                     0               0                 0   \n",
       "800435              0                     0               0                 0   \n",
       "800436              0                     0               0                 0   \n",
       "\n",
       "        state_West Virginia  state_Wisconsin  state_Wyoming  \n",
       "0                         0                0              0  \n",
       "1                         0                0              0  \n",
       "2                         0                0              0  \n",
       "3                         0                0              0  \n",
       "4                         0                0              0  \n",
       "...                     ...              ...            ...  \n",
       "800432                    0                0              1  \n",
       "800433                    0                0              1  \n",
       "800434                    0                0              1  \n",
       "800435                    0                0              1  \n",
       "800436                    0                0              1  \n",
       "\n",
       "[800437 rows x 2031 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preprocess \n",
    "\n",
    "df_covid_preprocessed = preprocess(df_covid)\n",
    "df_covid_preprocessed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cases                  0\n",
       "year_2020              0\n",
       "month_1                0\n",
       "month_2                0\n",
       "month_3                0\n",
       "                      ..\n",
       "state_Virginia         0\n",
       "state_Washington       0\n",
       "state_West Virginia    0\n",
       "state_Wisconsin        0\n",
       "state_Wyoming          0\n",
       "Length: 2029, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_covid_preprocessed.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split preprocessed dataset\n",
    "\n",
    "y_PP = df_covid_preprocessed['cases']\n",
    "X_PP = df_covid_preprocessed.drop('cases', axis=1, inplace=False)\n",
    "\n",
    "X_train_PP, X_test_PP, y_train_PP, y_test_PP = train_test_split(X_PP, y_PP, test_size=0.2, random_state=156)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define functions to calculate metrics\n",
    "\n",
    "def evaluate(model, X_test, y_test, exp):\n",
    "    # return np.expm1 for log normalized data\n",
    "    if exp:\n",
    "        pred = np.expm1(model.predict(X_test))\n",
    "        y_true = np.expm1(y_test)\n",
    "\n",
    "        MSE = mean_squared_error(y_true, pred)\n",
    "        MAE = mean_absolute_error(y_true, pred)\n",
    "        R_2 = r2_score(y_true, pred)\n",
    "\n",
    "        print(\"Model - {} -\".format(model), \"MSE: {0:3f}, MAE: {1:3f}, R Squared: {2:3f}\".format(MSE, MAE, R_2))\n",
    "        \n",
    "        return MSE, MAE, R_2\n",
    "        \n",
    "    else:\n",
    "        pred = model.predict(X_test)\n",
    "        y_true = y_test\n",
    "\n",
    "        MSE = mean_squared_error(y_true, pred)\n",
    "        MAE = mean_absolute_error(y_true, pred)\n",
    "        R_2 = r2_score(y_true, pred)\n",
    "\n",
    "        print(\"Model - {} -\".format(model), \"MSE: {0:3f}, MAE: {1:3f}, R Squared: {2:3f}\".format(MSE, MAE, R_2))\n",
    "        \n",
    "        return MSE, MAE, R_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model train - Linear Regression with propossed data\n",
    "\n",
    "model_lr = LinearRegression()\n",
    "model_lr.fit(X_train_PP,y_train_PP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model - LinearRegression() - MSE: 13598165.145220, MAE: 1057.921541, R Squared: 0.814883\n"
     ]
    }
   ],
   "source": [
    "LINEAR =  evaluate(model_lr, X_test=X_test_PP, y_test=y_test_PP, exp=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge()"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model train - Ridge\n",
    "\n",
    "model_ridge = Ridge()\n",
    "model_ridge.fit(X_train_PP, y_train_PP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso()"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model train - Lasso\n",
    "\n",
    "model_lasso = Lasso()\n",
    "model_lasso.fit(X_train_PP, y_train_PP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model - Ridge(random_state=156) - MSE: 13600911.513503, MAE: 1058.811013, R Squared: 0.814845\n",
      "Model - Lasso() - MSE: 14284873.963408, MAE: 1081.651878, R Squared: 0.805534\n"
     ]
    }
   ],
   "source": [
    "RIDGE = evaluate(model_ridge, X_test=X_test_PP, y_test=y_test_PP, exp=False)\n",
    "LASSO = evaluate(model_lasso, X_test=X_test_PP, y_test=y_test_PP, exp=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LINEAR,RIDGE,LASSO 중에서 LASSO가 제일 낮음을 알 수 있다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyper Parameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_best_alpha(MODEL, PARAMS, CV=10):\n",
    "    # GridSearchCV to find best alpha\n",
    "    grid = GridSearchCV(MODEL, PARAMS, cv=CV, return_train_score=True, scoring='neg_mean_squared_error')\n",
    "    grid.fit(X_PP, y_PP)\n",
    "    mse = -1 * grid.best_score_\n",
    "    best_alpha = grid.best_params_\n",
    "    \n",
    "    alphas = PARAMS['alpha']\n",
    "    \n",
    "    train_scores_mean = grid.cv_results_[\"mean_train_score\"]\n",
    "    train_scores_std = grid.cv_results_[\"std_train_score\"]\n",
    "    test_scores_mean = grid.cv_results_[\"mean_test_score\"]\n",
    "    test_scores_std = grid.cv_results_[\"std_test_score\"]\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.title('Model')\n",
    "    plt.xlabel('$\\\\alpha$ (alpha)')\n",
    "    plt.ylabel('Score')\n",
    "\n",
    "    # plot train scores\n",
    "    plt.semilogx(alphas, train_scores_mean, label='Mean Train score',\n",
    "                 color='navy')\n",
    "\n",
    "    # create a shaded area between [mean - std, mean + std]\n",
    "    plt.gca().fill_between(alphas,\n",
    "                           train_scores_mean - train_scores_std,\n",
    "                           train_scores_mean + train_scores_std,\n",
    "                           alpha=0.2,\n",
    "                           color='navy')\n",
    "    plt.semilogx(alphas, test_scores_mean,\n",
    "                 label='Mean Test score', color='darkorange')\n",
    "\n",
    "    # create a shaded area between [mean - std, mean + std]\n",
    "    plt.gca().fill_between(alphas,\n",
    "                           test_scores_mean - test_scores_std,\n",
    "                           test_scores_mean + test_scores_std,\n",
    "                           alpha=0.2,\n",
    "                           color='darkorange')\n",
    "\n",
    "    plt.legend(loc='best')\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"Best MSE: {:.2f}\".format(grid.best_score_))\n",
    "    print(\"Best Alpha: \", grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best MSE: -38578380.76\n",
      "Best Alpha:  {'alpha': 100.0}\n"
     ]
    }
   ],
   "source": [
    "ridge_params = {'alpha':np.logspace(-3, 3, 7)}\n",
    "find_best_alpha(Ridge(), ridge_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Model with Best Alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "Unable to allocate 12.1 GiB for an array with shape (2030, 800437) and data type float64",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-45-81af0d018303>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mmodel_ridge3\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRidge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mmodel_ridge3\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_PP\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_PP\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mRIDGE_BEST\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_ridge3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mX_test_PP\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my_test_PP\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    760\u001b[0m         \u001b[0mself\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mreturns\u001b[0m \u001b[0man\u001b[0m \u001b[0minstance\u001b[0m \u001b[0mof\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    761\u001b[0m         \"\"\"\n\u001b[1;32m--> 762\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    763\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    764\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    540\u001b[0m         _accept_sparse = _get_valid_accept_sparse(sparse.issparse(X),\n\u001b[0;32m    541\u001b[0m                                                   self.solver)\n\u001b[1;32m--> 542\u001b[1;33m         X, y = self._validate_data(X, y,\n\u001b[0m\u001b[0;32m    543\u001b[0m                                    \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_accept_sparse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    544\u001b[0m                                    \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_dtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    430\u001b[0m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    431\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 432\u001b[1;33m                 \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    433\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    434\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m    793\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"y cannot be None\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    794\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 795\u001b[1;33m     X = check_array(X, accept_sparse=accept_sparse,\n\u001b[0m\u001b[0;32m    796\u001b[0m                     \u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    797\u001b[0m                     \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    596\u001b[0m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"unsafe\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    597\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 598\u001b[1;33m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    599\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    600\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\numpy\\core\\_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m     81\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     82\u001b[0m     \"\"\"\n\u001b[1;32m---> 83\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     84\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     85\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m   1779\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1780\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1781\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1782\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1783\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__array_wrap__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_values\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   5346\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5347\u001b[0m         \u001b[1;34m\"\"\"internal implementation\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5348\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5349\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5350\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mvalues\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   5341\u001b[0m         \"\"\"\n\u001b[0;32m   5342\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_consolidate_inplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5343\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_AXIS_REVERSED\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5344\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5345\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mas_array\u001b[1;34m(self, transpose, dtype, copy, na_value)\u001b[0m\n\u001b[0;32m    851\u001b[0m                     \u001b[0marr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    852\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 853\u001b[1;33m             \u001b[0marr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_interleave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mna_value\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mna_value\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    854\u001b[0m             \u001b[1;31m# The underlying data was copied within _interleave\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    855\u001b[0m             \u001b[0mcopy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36m_interleave\u001b[1;34m(self, dtype, na_value)\u001b[0m\n\u001b[0;32m    880\u001b[0m             \u001b[0mdtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"object\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    881\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 882\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    883\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    884\u001b[0m         \u001b[0mitemmask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMemoryError\u001b[0m: Unable to allocate 12.1 GiB for an array with shape (2030, 800437) and data type float64"
     ]
    }
   ],
   "source": [
    "# fitting ridge with best alpha=100\n",
    "\n",
    "model_ridge3 = Ridge(alpha=100)\n",
    "model_ridge3.fit(X_PP, y_PP)\n",
    "RIDGE_BEST = evaluate(model_ridge3, X_test=X_test_PP, y_test=y_test_PP, exp=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary of Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>R_Squared</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LINEAR</th>\n",
       "      <td>1.359817e+07</td>\n",
       "      <td>1057.921541</td>\n",
       "      <td>0.814883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RIDGE</th>\n",
       "      <td>1.360091e+07</td>\n",
       "      <td>1058.811013</td>\n",
       "      <td>0.814845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LASSO</th>\n",
       "      <td>1.428487e+07</td>\n",
       "      <td>1081.651878</td>\n",
       "      <td>0.805534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 MSE          MAE  R_Squared\n",
       "Model                                       \n",
       "LINEAR  1.359817e+07  1057.921541   0.814883\n",
       "RIDGE   1.360091e+07  1058.811013   0.814845\n",
       "LASSO   1.428487e+07  1081.651878   0.805534"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Model comparison \n",
    "\n",
    "df_conclusion = pd.DataFrame(columns=['MSE', 'MAE', 'R_Squared'])\n",
    "df_conclusion = df_conclusion.append(pd.Series(list(LINEAR), index=df_conclusion.columns), ignore_index=True)\n",
    "df_conclusion = df_conclusion.append(pd.Series(list(RIDGE), index=df_conclusion.columns), ignore_index=True)\n",
    "df_conclusion = df_conclusion.append(pd.Series(list(RIDGE_BEST), index=df_conclusion.columns), ignore_index=True)\n",
    "df_conclusion = df_conclusion.append(pd.Series(list(LASSO), index=df_conclusion.columns), ignore_index=True)\n",
    "df_conclusion['Model'] = ['LINEAR', 'RIDGE','LASSO']\n",
    "df_conclusion = df_conclusion[['Model', 'MSE', 'MAE', 'R_Squared']]\n",
    "df_conclusion.set_index('Model', inplace=True)\n",
    "display(df_conclusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Model'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# THIS PLOT IS NORMALIZED\n",
    "df_conclusion.apply(lambda x: x/x.max(), axis=0).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#LINEAR가 제일 점수가 높고 LASSO가 점수가 제일 낮았다.\n",
    "하지만 전반적으로 R^2가 0.8 점 이상으로 높게 나와서 세 가지 모델 모두 예측력이 있다는 것을 알 수 있다."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}