covid prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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": 7,
     "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": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_covid.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Discover missing values\n",
    "\n",
    "df_covid.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date      0\n",
       "county    0\n",
       "state     0\n",
       "fips      0\n",
       "cases     0\n",
       "deaths    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check missing values again\n",
    "\n",
    "df_covid.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 800437 entries, 0 to 800436\n",
      "Data columns (total 6 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   fips    800437 non-null  float64\n",
      " 4   cases   800437 non-null  int64  \n",
      " 5   deaths  800437 non-null  float64\n",
      "dtypes: float64(2), int64(1), object(3)\n",
      "memory usage: 36.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# Discover data type\n",
    "\n",
    "df_covid.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fips</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>800437.000000</td>\n",
       "      <td>800437.000000</td>\n",
       "      <td>800437.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>31222.390284</td>\n",
       "      <td>1589.667358</td>\n",
       "      <td>47.857548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16296.226412</td>\n",
       "      <td>8385.201329</td>\n",
       "      <td>435.903842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1001.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>18179.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>29205.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>46093.000000</td>\n",
       "      <td>775.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>78030.000000</td>\n",
       "      <td>439538.000000</td>\n",
       "      <td>24346.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                fips          cases         deaths\n",
       "count  800437.000000  800437.000000  800437.000000\n",
       "mean    31222.390284    1589.667358      47.857548\n",
       "std     16296.226412    8385.201329     435.903842\n",
       "min      1001.000000       0.000000       0.000000\n",
       "25%     18179.000000      29.000000       0.000000\n",
       "50%     29205.000000     175.000000       3.000000\n",
       "75%     46093.000000     775.000000      17.000000\n",
       "max     78030.000000  439538.000000   24346.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_covid.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_covid.cases\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Target: covid cases'}, xlabel='cases', ylabel='Density'>"
      ]
     },
     "execution_count": 17,
     "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": [
    "# Original target variable histogram\n",
    "\n",
    "plt.title('Target: covid cases')\n",
    "sns.distplot(df_covid['cases'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# covid plot with raw data\n",
    "\n",
    "df_date = df_covid.groupby('date').mean()['cases'].reset_index()\n",
    "df_fips = df_covid.groupby('fips').mean()['cases'].reset_index()\n",
    "df_county = df_covid.groupby('county').mean()['cases'].reset_index()\n",
    "df_state = df_covid.groupby('state').mean()['cases'].reset_index()\n",
    "\n",
    "fig, ax = plt.subplots(2, 2, figsize=(15, 8))\n",
    "\n",
    "sns.scatterplot(data=df_date, ax=ax[0, 0], x='date', y='cases').set_xticklabels(rotation=90, labels=labels)\n",
    "sns.scatterplot(data=df_fips, ax=ax[0, 1], x='fips', y='cases')\n",
    "sns.scatterplot(data=df_fips, ax=ax[0, 1], x='fips', y='cases')\n",
    "sns.scatterplot(data=df_county, ax=ax[1, 0], x='county', y='cases').set_xticklabels(rotation=90, labels=labels)\n",
    "sns.scatterplot(data=df_state, ax=ax[1, 1], x='state', y='cases').set_xticklabels(rotation=90, labels=labels)\n",
    "\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(df_covid.corr(),annot=True, \n",
    "fmt = '.2f', linewidths=.5, cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Log Transformed Target: covid cases'}, xlabel='cases', ylabel='Density'>"
      ]
     },
     "execution_count": 21,
     "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": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test data transformation\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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>800433</th>\n",
       "      <td>56039.0</td>\n",
       "      <td>1739</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800434</th>\n",
       "      <td>56041.0</td>\n",
       "      <td>1187</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800435</th>\n",
       "      <td>56043.0</td>\n",
       "      <td>519</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800436</th>\n",
       "      <td>56045.0</td>\n",
       "      <td>419</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preprocess \n",
    "\n",
    "df_covid_preprocessed = preprocess(df_covid)\n",
    "df_covid_preprocessed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run after preprocess\n",
    "\n",
    "df_covid.date = df_covid.date.apply(pd.to_datetime)\n",
    "df_covid['year'] = df_covid.date.apply(lambda x: x.year)\n",
    "df_covid['month'] = df_covid.date.apply(lambda x: x.month)\n",
    "df_covid['day'] = df_covid.date.apply(lambda x: x.day)\n",
    "df_covid.drop(['date'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split preprocessed dataset\n",
    "# Train test split\n",
    "\n",
    "y_PP = df_covid_preprocessed['cases']\n",
    "X_PP = df_covid_preprocessed.drop('cases', axis=1, inplace=False)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_PP, y_PP, test_size=0.2, random_state=156)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "could not allocate 24602738688 bytes",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-27-3bf608d7c171>\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[0mtree\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDecisionTreeClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\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[0mtree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    888\u001b[0m         \"\"\"\n\u001b[0;32m    889\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 890\u001b[1;33m         super().fit(\n\u001b[0m\u001b[0;32m    891\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[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    892\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[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    373\u001b[0m                                            min_impurity_split)\n\u001b[0;32m    374\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 375\u001b[1;33m         \u001b[0mbuilder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtree_\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[0mX_idx_sorted\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    376\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    377\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mis_classifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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;32msklearn\\tree\\_tree.pyx\u001b[0m in \u001b[0;36msklearn.tree._tree.DepthFirstTreeBuilder.build\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32msklearn\\tree\\_tree.pyx\u001b[0m in \u001b[0;36msklearn.tree._tree.DepthFirstTreeBuilder.build\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32msklearn\\tree\\_tree.pyx\u001b[0m in \u001b[0;36msklearn.tree._tree.Tree._add_node\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32msklearn\\tree\\_tree.pyx\u001b[0m in \u001b[0;36msklearn.tree._tree.Tree._resize_c\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32msklearn\\tree\\_utils.pyx\u001b[0m in \u001b[0;36msklearn.tree._utils.safe_realloc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mMemoryError\u001b[0m: could not allocate 24602738688 bytes"
     ]
    }
   ],
   "source": [
    "# Decision tree\n",
    "\n",
    "tree = DecisionTreeClassifier(random_state=1)\n",
    "tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_train = tree.predict(X_train)\n",
    "pred_test = tree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(classification_report(y_train, pred_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(classification_report(y_test, pred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "Unable to allocate 9.69 GiB for an array with shape (2030, 640349) 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-28-1f87430f3587>\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_lr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLinearRegression\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----> 4\u001b[1;33m \u001b[0mmodel_lr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    503\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    504\u001b[0m         \u001b[0mn_jobs_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 505\u001b[1;33m         X, y = self._validate_data(X, y, accept_sparse=['csr', 'csc', 'coo'],\n\u001b[0m\u001b[0;32m    506\u001b[0m                                    y_numeric=True, multi_output=True)\n\u001b[0;32m    507\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 9.69 GiB for an array with shape (2030, 640349) and data type float64"
     ]
    }
   ],
   "source": [
    "# Model train - Linear Regression with propossed data\n",
    "\n",
    "model_lr = LinearRegression()\n",
    "model_lr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-29-daf94cd7e5b9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Display coefficients of features\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0mcoef_chart\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mX\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[0m\u001b[0;32m      4\u001b[0m     \u001b[0mcoef\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mcoef_sort\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcoef\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mascending\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;31mNameError\u001b[0m: name 'X' is not defined"
     ]
    }
   ],
   "source": [
    "# Display coefficients of features\n",
    "\n",
    "def coef_chart(model, X=X):\n",
    "    coef = pd.Series(model.coef_, X.columns)\n",
    "    coef_sort = coef.sort_values(ascending=False)\n",
    "    sns.barplot(x=coef_sort.values, y=coef_sort.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "coef_chart(model_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "Unable to allocate 9.69 GiB for an array with shape (2030, 640349) 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-119-d7b053d37d84>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mmodel_lasso2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLasso\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m156\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mmodel_ridge2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train_PP\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train_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      7\u001b[0m \u001b[0mmodel_lasso2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train_PP\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train_PP\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 9.69 GiB for an array with shape (2030, 640349) and data type float64"
     ]
    }
   ],
   "source": [
    "# Model train - Ridge & Lasso\n",
    "\n",
    "model_ridge2 = Ridge(random_state=156)\n",
    "model_lasso2 = Lasso(random_state=156)\n",
    "\n",
    "model_ridge2.fit(X_train_PP, y_train_PP)\n",
    "model_lasso2.fit(X_train_PP, y_train_PP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "RIDGE = evaluate(model_ridge2, X_test=X_test, y_test=y_test, exp=False)\n",
    "LASSO = evaluate(model_lasso2, X_test=X_test, y_test=y_test, exp=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyper Parameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Model with Best Alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary of Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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