train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather = pd.read_csv(\"local_weather.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>STATION</th>\n",
       "      <th>NAME</th>\n",
       "      <th>DATE</th>\n",
       "      <th>ACMH</th>\n",
       "      <th>ACSH</th>\n",
       "      <th>AWND</th>\n",
       "      <th>FMTM</th>\n",
       "      <th>PGTM</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>PSUN</th>\n",
       "      <th>...</th>\n",
       "      <th>WT09</th>\n",
       "      <th>WT10</th>\n",
       "      <th>WT11</th>\n",
       "      <th>WT13</th>\n",
       "      <th>WT14</th>\n",
       "      <th>WT16</th>\n",
       "      <th>WT18</th>\n",
       "      <th>WT21</th>\n",
       "      <th>WT22</th>\n",
       "      <th>WV01</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USW00023188</td>\n",
       "      <td>SAN DIEGO INTERNATIONAL AIRPORT, CA US</td>\n",
       "      <td>1960-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USW00023188</td>\n",
       "      <td>SAN DIEGO INTERNATIONAL AIRPORT, CA US</td>\n",
       "      <td>1960-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USW00023188</td>\n",
       "      <td>SAN DIEGO INTERNATIONAL AIRPORT, CA US</td>\n",
       "      <td>1960-01-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>USW00023188</td>\n",
       "      <td>SAN DIEGO INTERNATIONAL AIRPORT, CA US</td>\n",
       "      <td>1960-01-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>USW00023188</td>\n",
       "      <td>SAN DIEGO INTERNATIONAL AIRPORT, CA US</td>\n",
       "      <td>1960-01-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23489</th>\n",
       "      <td>USW00023188</td>\n",
       "      <td>SAN DIEGO INTERNATIONAL AIRPORT, CA US</td>\n",
       "      <td>2024-04-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23490</th>\n",
       "      <td>USW00023188</td>\n",
       "      <td>SAN DIEGO INTERNATIONAL AIRPORT, CA US</td>\n",
       "      <td>2024-04-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23491</th>\n",
       "      <td>USW00023188</td>\n",
       "      <td>SAN DIEGO INTERNATIONAL AIRPORT, CA US</td>\n",
       "      <td>2024-04-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23492</th>\n",
       "      <td>USW00023188</td>\n",
       "      <td>SAN DIEGO INTERNATIONAL AIRPORT, CA US</td>\n",
       "      <td>2024-04-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23493</th>\n",
       "      <td>USW00023188</td>\n",
       "      <td>SAN DIEGO INTERNATIONAL AIRPORT, CA US</td>\n",
       "      <td>2024-04-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23494 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           STATION                                    NAME        DATE  ACMH  \\\n",
       "0      USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US  1960-01-01   NaN   \n",
       "1      USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US  1960-01-02   NaN   \n",
       "2      USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US  1960-01-03   NaN   \n",
       "3      USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US  1960-01-04   NaN   \n",
       "4      USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US  1960-01-05   NaN   \n",
       "...            ...                                     ...         ...   ...   \n",
       "23489  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US  2024-04-23   NaN   \n",
       "23490  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US  2024-04-24   NaN   \n",
       "23491  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US  2024-04-25   NaN   \n",
       "23492  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US  2024-04-26   NaN   \n",
       "23493  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US  2024-04-27   NaN   \n",
       "\n",
       "       ACSH  AWND  FMTM  PGTM  PRCP  PSUN  ...  WT09  WT10  WT11  WT13  WT14  \\\n",
       "0       NaN   NaN   NaN   NaN  0.03   NaN  ...   NaN   NaN   NaN   NaN   NaN   \n",
       "1       NaN   NaN   NaN   NaN  0.00   NaN  ...   NaN   NaN   NaN   NaN   NaN   \n",
       "2       NaN   NaN   NaN   NaN  0.00   NaN  ...   NaN   NaN   NaN   NaN   NaN   \n",
       "3       NaN   NaN   NaN   NaN  0.00   NaN  ...   NaN   NaN   NaN   NaN   NaN   \n",
       "4       NaN   NaN   NaN   NaN  0.00   NaN  ...   NaN   NaN   NaN   NaN   NaN   \n",
       "...     ...   ...   ...   ...   ...   ...  ...   ...   ...   ...   ...   ...   \n",
       "23489   NaN  7.83   NaN   NaN  0.00   NaN  ...   NaN   NaN   NaN   NaN   NaN   \n",
       "23490   NaN  7.16   NaN   NaN  0.00   NaN  ...   NaN   NaN   NaN   NaN   NaN   \n",
       "23491   NaN  7.61   NaN   NaN  0.00   NaN  ...   NaN   NaN   NaN   NaN   NaN   \n",
       "23492   NaN   NaN   NaN   NaN  0.00   NaN  ...   NaN   NaN   NaN   NaN   NaN   \n",
       "23493   NaN   NaN   NaN   NaN   NaN   NaN  ...   NaN   NaN   NaN   NaN   NaN   \n",
       "\n",
       "       WT16  WT18  WT21  WT22  WV01  \n",
       "0       1.0   NaN   NaN   NaN   NaN  \n",
       "1       NaN   NaN   NaN   NaN   NaN  \n",
       "2       1.0   NaN   NaN   NaN   NaN  \n",
       "3       NaN   NaN   NaN   NaN   NaN  \n",
       "4       NaN   NaN   NaN   NaN   NaN  \n",
       "...     ...   ...   ...   ...   ...  \n",
       "23489   NaN   NaN   NaN   NaN   NaN  \n",
       "23490   NaN   NaN   NaN   NaN   NaN  \n",
       "23491   NaN   NaN   NaN   NaN   NaN  \n",
       "23492   NaN   NaN   NaN   NaN   NaN  \n",
       "23493   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[23494 rows x 44 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=23494, step=1)\n"
     ]
    }
   ],
   "source": [
    "print(weather.index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather.set_index('DATE', inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                STATION                                    NAME  ACMH  ACSH  \\\n",
      "DATE                                                                          \n",
      "1960-01-01  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-02  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-03  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-04  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-05  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-06  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-07  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-08  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-09  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-10  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-11  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-12  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-13  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-14  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-15  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-16  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-17  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-18  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-19  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-20  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-21  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-22  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-23  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-24  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-25  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-26  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-27  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-28  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-29  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-30  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "1960-01-31  USW00023188  SAN DIEGO INTERNATIONAL AIRPORT, CA US   NaN   NaN   \n",
      "\n",
      "            AWND  FMTM  PGTM  PRCP  PSUN  SNOW  ...  WT09  WT10  WT11  WT13  \\\n",
      "DATE                                            ...                           \n",
      "1960-01-01   NaN   NaN   NaN  0.03   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-02   NaN   NaN   NaN  0.00   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-03   NaN   NaN   NaN  0.00   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-04   NaN   NaN   NaN  0.00   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-05   NaN   NaN   NaN  0.00   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-06   NaN   NaN   NaN  0.00   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-07   NaN   NaN   NaN  0.00   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-08   NaN   NaN   NaN  0.00   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-09   NaN   NaN   NaN  0.00   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-10   NaN   NaN   NaN  0.47   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-11   NaN   NaN   NaN  0.03   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-12   NaN   NaN   NaN  1.21   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-13   NaN   NaN   NaN  0.01   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-14   NaN   NaN   NaN  0.55   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-15   NaN   NaN   NaN  0.34   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-16   NaN   NaN   NaN  0.00   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-17   NaN   NaN   NaN  0.00   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-18   NaN   NaN   NaN  0.00   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-19   NaN   NaN   NaN  0.00   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-20   NaN   NaN   NaN  0.00   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-21   NaN   NaN   NaN  0.00   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-22   NaN   NaN   NaN  0.00   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-23   NaN   NaN   NaN  0.00   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-24   NaN   NaN   NaN  0.00   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-25   NaN   NaN   NaN  0.14   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-26   NaN   NaN   NaN  0.21   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-27   NaN   NaN   NaN  0.00   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-28   NaN   NaN   NaN  0.00   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-29   NaN   NaN   NaN  0.00   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-30   NaN   NaN   NaN  0.00   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "1960-01-31   NaN   NaN   NaN  0.00   NaN   0.0  ...   NaN   NaN   NaN   NaN   \n",
      "\n",
      "            WT14  WT16  WT18  WT21  WT22  WV01  \n",
      "DATE                                            \n",
      "1960-01-01   NaN   1.0   NaN   NaN   NaN   NaN  \n",
      "1960-01-02   NaN   NaN   NaN   NaN   NaN   NaN  \n",
      "1960-01-03   NaN   1.0   NaN   NaN   NaN   NaN  \n",
      "1960-01-04   NaN   NaN   NaN   NaN   NaN   NaN  \n",
      "1960-01-05   NaN   NaN   NaN   NaN   NaN   NaN  \n",
      "1960-01-06   NaN   NaN   NaN   NaN   NaN   NaN  \n",
      "1960-01-07   NaN   NaN   NaN   NaN   NaN   NaN  \n",
      "1960-01-08   NaN   NaN   NaN   NaN   NaN   NaN  \n",
      "1960-01-09   NaN   NaN   NaN   NaN   NaN   NaN  \n",
      "1960-01-10   NaN   1.0   NaN   NaN   NaN   NaN  \n",
      "1960-01-11   NaN   1.0   NaN   NaN   NaN   NaN  \n",
      "1960-01-12   NaN   1.0   NaN   NaN   NaN   NaN  \n",
      "1960-01-13   NaN   1.0   NaN   NaN   NaN   NaN  \n",
      "1960-01-14   NaN   1.0   NaN   NaN   NaN   NaN  \n",
      "1960-01-15   NaN   1.0   NaN   NaN   NaN   NaN  \n",
      "1960-01-16   NaN   NaN   NaN   NaN   NaN   NaN  \n",
      "1960-01-17   NaN   NaN   NaN   NaN   NaN   NaN  \n",
      "1960-01-18   NaN   NaN   NaN   NaN   NaN   NaN  \n",
      "1960-01-19   NaN   NaN   NaN   NaN   NaN   NaN  \n",
      "1960-01-20   NaN   NaN   NaN   NaN   NaN   NaN  \n",
      "1960-01-21   NaN   NaN   NaN   NaN   NaN   NaN  \n",
      "1960-01-22   NaN   NaN   NaN   NaN   NaN   NaN  \n",
      "1960-01-23   NaN   NaN   NaN   NaN   NaN   NaN  \n",
      "1960-01-24   NaN   NaN   NaN   NaN   NaN   NaN  \n",
      "1960-01-25   NaN   1.0   NaN   NaN   NaN   NaN  \n",
      "1960-01-26   NaN   1.0   NaN   NaN   NaN   NaN  \n",
      "1960-01-27   NaN   NaN   NaN   NaN   NaN   NaN  \n",
      "1960-01-28   NaN   NaN   NaN   NaN   NaN   NaN  \n",
      "1960-01-29   NaN   NaN   NaN   NaN   NaN   NaN  \n",
      "1960-01-30   NaN   NaN   NaN   NaN   NaN   NaN  \n",
      "1960-01-31   NaN   NaN   NaN   NaN   NaN   NaN  \n",
      "\n",
      "[31 rows x 43 columns]\n"
     ]
    }
   ],
   "source": [
    "print(weather.loc[\"1960-01-01\":\"1960-01-31\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATION    0.000000\n",
       "NAME       0.000000\n",
       "ACMH       0.509109\n",
       "ACSH       0.509024\n",
       "AWND       0.373287\n",
       "FMTM       0.567081\n",
       "PGTM       0.381161\n",
       "PRCP       0.000043\n",
       "PSUN       0.704648\n",
       "SNOW       0.386992\n",
       "SNWD       0.389717\n",
       "TAVG       0.714310\n",
       "TMAX       0.000043\n",
       "TMIN       0.000043\n",
       "TSUN       0.456968\n",
       "WDF1       0.998723\n",
       "WDF2       0.567592\n",
       "WDF5       0.571891\n",
       "WDFG       0.586660\n",
       "WDFM       0.535498\n",
       "WESD       0.751256\n",
       "WSF1       0.998723\n",
       "WSF2       0.567592\n",
       "WSF5       0.571891\n",
       "WSFG       0.586618\n",
       "WSFM       0.535456\n",
       "WT01       0.695156\n",
       "WT02       0.959351\n",
       "WT03       0.991189\n",
       "WT04       0.999787\n",
       "WT05       0.994552\n",
       "WT07       0.986763\n",
       "WT08       0.615051\n",
       "WT09       0.998127\n",
       "WT10       0.999745\n",
       "WT11       0.999957\n",
       "WT13       0.904614\n",
       "WT14       0.984592\n",
       "WT16       0.845109\n",
       "WT18       0.999957\n",
       "WT21       0.999532\n",
       "WT22       0.999957\n",
       "WV01       0.997914\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.apply(pd.isnull).sum()/weather.shape[0] #models don't work well with null values, so this will help us prep dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "core_weather = weather[[\"PRCP\", \"SNOW\", \"SNWD\", \"TMAX\", \"TMIN\"]].copy()\n",
    "core_weather.columns = [\"precip\", \"snow\", \"snow_depth\", \"temp_max\", \"temp_min\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>precip</th>\n",
       "      <th>snow</th>\n",
       "      <th>snow_depth</th>\n",
       "      <th>temp_max</th>\n",
       "      <th>temp_min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-02</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-03</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-04</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-05</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-23</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-24</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-25</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-26</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23494 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            precip  snow  snow_depth  temp_max  temp_min\n",
       "DATE                                                    \n",
       "1960-01-01    0.03   0.0         0.0      56.0      42.0\n",
       "1960-01-02    0.00   0.0         0.0      58.0      38.0\n",
       "1960-01-03    0.00   0.0         0.0      59.0      38.0\n",
       "1960-01-04    0.00   0.0         0.0      63.0      45.0\n",
       "1960-01-05    0.00   0.0         0.0      63.0      40.0\n",
       "...            ...   ...         ...       ...       ...\n",
       "2024-04-23    0.00   NaN         NaN      66.0      59.0\n",
       "2024-04-24    0.00   NaN         NaN      67.0      59.0\n",
       "2024-04-25    0.00   NaN         NaN      67.0      58.0\n",
       "2024-04-26    0.00   NaN         NaN      66.0      57.0\n",
       "2024-04-27     NaN   NaN         NaN       NaN       NaN\n",
       "\n",
       "[23494 rows x 5 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "precip        0.000043\n",
       "snow          0.386992\n",
       "snow_depth    0.389717\n",
       "temp_max      0.000043\n",
       "temp_min      0.000043\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_weather.apply(pd.isnull).sum()/core_weather.shape[0] #calculated percentage of missing null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "snow\n",
       "0.0    14402\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_weather[\"snow\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "del core_weather[\"snow\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "snow_depth\n",
       "0.0    14338\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_weather[\"snow_depth\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "del core_weather[\"snow_depth\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>precip</th>\n",
       "      <th>temp_max</th>\n",
       "      <th>temp_min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-04-27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            precip  temp_max  temp_min\n",
       "DATE                                  \n",
       "2024-04-27     NaN       NaN       NaN"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_weather[pd.isnull(core_weather[\"precip\"])]\n"
   ]
  },
  {
   "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>precip</th>\n",
       "      <th>temp_max</th>\n",
       "      <th>temp_min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-02-27</th>\n",
       "      <td>0.00</td>\n",
       "      <td>67.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-28</th>\n",
       "      <td>0.00</td>\n",
       "      <td>65.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-29</th>\n",
       "      <td>0.00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-01</th>\n",
       "      <td>0.00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-02</th>\n",
       "      <td>0.17</td>\n",
       "      <td>64.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-23</th>\n",
       "      <td>0.00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-24</th>\n",
       "      <td>0.00</td>\n",
       "      <td>67.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-25</th>\n",
       "      <td>0.00</td>\n",
       "      <td>67.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-26</th>\n",
       "      <td>0.00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            precip  temp_max  temp_min\n",
       "DATE                                  \n",
       "2024-02-27    0.00      67.0      56.0\n",
       "2024-02-28    0.00      65.0      54.0\n",
       "2024-02-29    0.00      66.0      59.0\n",
       "2024-03-01    0.00      66.0      53.0\n",
       "2024-03-02    0.17      64.0      57.0\n",
       "...            ...       ...       ...\n",
       "2024-04-23    0.00      66.0      59.0\n",
       "2024-04-24    0.00      67.0      59.0\n",
       "2024-04-25    0.00      67.0      58.0\n",
       "2024-04-26    0.00      66.0      57.0\n",
       "2024-04-27     NaN       NaN       NaN\n",
       "\n",
       "[61 rows x 3 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_weather.loc[\"2024-02-27\":\"2024-04-29\",:] #looks like dataset is 3 days old as of April 29 and that's why there's a null value for precip on 4/27\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "precip\n",
       "0.00    20849\n",
       "0.01      371\n",
       "0.02      205\n",
       "0.03      166\n",
       "0.04      140\n",
       "        ...  \n",
       "1.17        1\n",
       "1.42        1\n",
       "1.31        1\n",
       "0.89        1\n",
       "2.73        1\n",
       "Name: count, Length: 175, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_weather[\"precip\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "core_weather[\"precip\"] = core_weather[\"precip\"].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>precip</th>\n",
       "      <th>temp_max</th>\n",
       "      <th>temp_min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [precip, temp_max, temp_min]\n",
       "Index: []"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_weather[pd.isnull(core_weather[\"precip\"])] #we filled all the missing precip data with 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>precip</th>\n",
       "      <th>temp_max</th>\n",
       "      <th>temp_min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-04-27</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            precip  temp_max  temp_min\n",
       "DATE                                  \n",
       "2024-04-27     0.0       NaN       NaN"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_weather[pd.isnull(core_weather[\"temp_max\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "core_weather = core_weather.drop('2024-04-27')\n",
    "#just going to remove that day entirely as the data wasn't updated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>precip</th>\n",
       "      <th>temp_max</th>\n",
       "      <th>temp_min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [precip, temp_max, temp_min]\n",
       "Index: []"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_weather[pd.isnull(core_weather[\"temp_max\"])] #now we have no null values here  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "precip      float64\n",
       "temp_max    float64\n",
       "temp_min    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_weather.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['1960-01-01', '1960-01-02', '1960-01-03', '1960-01-04', '1960-01-05',\n",
       "       '1960-01-06', '1960-01-07', '1960-01-08', '1960-01-09', '1960-01-10',\n",
       "       ...\n",
       "       '2024-04-17', '2024-04-18', '2024-04-19', '2024-04-20', '2024-04-21',\n",
       "       '2024-04-22', '2024-04-23', '2024-04-24', '2024-04-25', '2024-04-26'],\n",
       "      dtype='object', name='DATE', length=23493)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_weather.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "core_weather.index = pd.to_datetime(core_weather.index) #convert to datetimeindex for easier data manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1960-01-01', '1960-01-02', '1960-01-03', '1960-01-04',\n",
       "               '1960-01-05', '1960-01-06', '1960-01-07', '1960-01-08',\n",
       "               '1960-01-09', '1960-01-10',\n",
       "               ...\n",
       "               '2024-04-17', '2024-04-18', '2024-04-19', '2024-04-20',\n",
       "               '2024-04-21', '2024-04-22', '2024-04-23', '2024-04-24',\n",
       "               '2024-04-25', '2024-04-26'],\n",
       "              dtype='datetime64[ns]', name='DATE', length=23493, freq=None)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_weather.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([1960, 1960, 1960, 1960, 1960, 1960, 1960, 1960, 1960, 1960,\n",
       "       ...\n",
       "       2024, 2024, 2024, 2024, 2024, 2024, 2024, 2024, 2024, 2024],\n",
       "      dtype='int32', name='DATE', length=23493)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_weather.index.year #can sort by year month etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "precip      0\n",
       "temp_max    0\n",
       "temp_min    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_weather.apply(lambda x: (x==9999).sum()) #table 4 in doccumentation must check for missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='DATE'>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "core_weather[[\"temp_max\",\"temp_min\"]].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Looks good. No holes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='DATE'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "core_weather[[\"precip\"]].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE\n",
       "1960     7.05\n",
       "1961     4.61\n",
       "1962     7.39\n",
       "1963     7.72\n",
       "1964     5.27\n",
       "        ...  \n",
       "2020     7.83\n",
       "2021     7.85\n",
       "2022     5.90\n",
       "2023    14.43\n",
       "2024    10.93\n",
       "Name: precip, Length: 65, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_weather.groupby(core_weather.index.year).sum()[\"precip\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "core_weather[\"target\"] = core_weather.shift(-1)[\"temp_max\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>precip</th>\n",
       "      <th>temp_max</th>\n",
       "      <th>temp_min</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>0.03</td>\n",
       "      <td>56.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-02</th>\n",
       "      <td>0.00</td>\n",
       "      <td>58.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-03</th>\n",
       "      <td>0.00</td>\n",
       "      <td>59.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-04</th>\n",
       "      <td>0.00</td>\n",
       "      <td>63.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-05</th>\n",
       "      <td>0.00</td>\n",
       "      <td>63.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-22</th>\n",
       "      <td>0.00</td>\n",
       "      <td>63.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-23</th>\n",
       "      <td>0.00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-24</th>\n",
       "      <td>0.00</td>\n",
       "      <td>67.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-25</th>\n",
       "      <td>0.00</td>\n",
       "      <td>67.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-26</th>\n",
       "      <td>0.00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23493 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            precip  temp_max  temp_min  target\n",
       "DATE                                          \n",
       "1960-01-01    0.03      56.0      42.0    58.0\n",
       "1960-01-02    0.00      58.0      38.0    59.0\n",
       "1960-01-03    0.00      59.0      38.0    63.0\n",
       "1960-01-04    0.00      63.0      45.0    63.0\n",
       "1960-01-05    0.00      63.0      40.0    66.0\n",
       "...            ...       ...       ...     ...\n",
       "2024-04-22    0.00      63.0      58.0    66.0\n",
       "2024-04-23    0.00      66.0      59.0    67.0\n",
       "2024-04-24    0.00      67.0      59.0    67.0\n",
       "2024-04-25    0.00      67.0      58.0    66.0\n",
       "2024-04-26    0.00      66.0      57.0     NaN\n",
       "\n",
       "[23493 rows x 4 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "core_weather = core_weather.iloc[:-1,:].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>precip</th>\n",
       "      <th>temp_max</th>\n",
       "      <th>temp_min</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>0.03</td>\n",
       "      <td>56.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-02</th>\n",
       "      <td>0.00</td>\n",
       "      <td>58.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-03</th>\n",
       "      <td>0.00</td>\n",
       "      <td>59.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-04</th>\n",
       "      <td>0.00</td>\n",
       "      <td>63.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-05</th>\n",
       "      <td>0.00</td>\n",
       "      <td>63.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-21</th>\n",
       "      <td>0.00</td>\n",
       "      <td>68.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-22</th>\n",
       "      <td>0.00</td>\n",
       "      <td>63.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-23</th>\n",
       "      <td>0.00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-24</th>\n",
       "      <td>0.00</td>\n",
       "      <td>67.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-25</th>\n",
       "      <td>0.00</td>\n",
       "      <td>67.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23492 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            precip  temp_max  temp_min  target\n",
       "DATE                                          \n",
       "1960-01-01    0.03      56.0      42.0    58.0\n",
       "1960-01-02    0.00      58.0      38.0    59.0\n",
       "1960-01-03    0.00      59.0      38.0    63.0\n",
       "1960-01-04    0.00      63.0      45.0    63.0\n",
       "1960-01-05    0.00      63.0      40.0    66.0\n",
       "...            ...       ...       ...     ...\n",
       "2024-04-21    0.00      68.0      60.0    63.0\n",
       "2024-04-22    0.00      63.0      58.0    66.0\n",
       "2024-04-23    0.00      66.0      59.0    67.0\n",
       "2024-04-24    0.00      67.0      59.0    67.0\n",
       "2024-04-25    0.00      67.0      58.0    66.0\n",
       "\n",
       "[23492 rows x 4 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_weather #check to see if we removed last row with null valu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pip install scikit-learn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "reg = Ridge(alpha=.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = [\"precip\", \"temp_max\", \"temp_min\"] #these are going to predict tomorrows max temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = core_weather.loc[:\"2022-12-31\"] #training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = core_weather.loc[\"2023-01-01\":] #testing versus everything after that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge(alpha=0.1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Ridge<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Ridge(alpha=0.1)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "Ridge(alpha=0.1)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(train[predictors], train[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = reg.predict(test[predictors])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.2986300509144124"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(test[\"target\"], predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Only about 2 degrees off from actual temperature!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined = pd.concat([test[\"target\"], pd.Series(predictions, index=test.index)], axis=1)\n",
    "combined.columns = [\"actual\", \"predictions\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>actual</th>\n",
       "      <th>predictions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-01-01</th>\n",
       "      <td>60.0</td>\n",
       "      <td>61.603322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-02</th>\n",
       "      <td>64.0</td>\n",
       "      <td>62.125785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-03</th>\n",
       "      <td>65.0</td>\n",
       "      <td>64.755864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-04</th>\n",
       "      <td>63.0</td>\n",
       "      <td>66.620517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-05</th>\n",
       "      <td>63.0</td>\n",
       "      <td>64.344838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-21</th>\n",
       "      <td>63.0</td>\n",
       "      <td>69.001657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-22</th>\n",
       "      <td>66.0</td>\n",
       "      <td>65.080312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-23</th>\n",
       "      <td>67.0</td>\n",
       "      <td>67.409491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-24</th>\n",
       "      <td>67.0</td>\n",
       "      <td>68.146503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-25</th>\n",
       "      <td>66.0</td>\n",
       "      <td>68.028362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>481 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            actual  predictions\n",
       "DATE                           \n",
       "2023-01-01    60.0    61.603322\n",
       "2023-01-02    64.0    62.125785\n",
       "2023-01-03    65.0    64.755864\n",
       "2023-01-04    63.0    66.620517\n",
       "2023-01-05    63.0    64.344838\n",
       "...            ...          ...\n",
       "2024-04-21    63.0    69.001657\n",
       "2024-04-22    66.0    65.080312\n",
       "2024-04-23    67.0    67.409491\n",
       "2024-04-24    67.0    68.146503\n",
       "2024-04-25    66.0    68.028362\n",
       "\n",
       "[481 rows x 2 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined #shows actual values and then our predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='DATE'>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.73204198,  0.73701236,  0.11814157])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### According to our results, precip has a negative impact on temp according to these resuults. Temp max is biggest input and temp min is smaller input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted max temperature for tommorrow 68.02836174144454\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/noahbakayou/Developer/CodingProjects/VSCodeProjects/AI/AIWeatherPredictor/myenv/lib/python3.12/site-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but Ridge was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Selecting the last row from your prepared dataset to use for prediction\n",
    "last_day_features = core_weather.iloc[-1][[\"precip\", \"temp_max\", \"temp_min\"]]\n",
    "\n",
    "# Predicting the next day's maximum temperature\n",
    "next_day_temp_max = reg.predict([last_day_features])\n",
    "\n",
    "print(\"Predicted max temperature for tommorrow:\", next_day_temp_max[0])\n"
   ]
  },
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    "### In conclusion, our model can accurately predict 1 day in advanced to get the weather within 2 degrees. The way our model works is it uses the previous days temp_max, temp_min and precip in order to predict the next day. The model is trained on historical data but has access to the previous day stats. Since our last day in the dataset is April 25, we can predict the max temperature on April 26th . As shown by the previous cell results, the predicted max temperature for April 26, 2024 is about 68 degrees. If we go to an updated dataset from NOAA to find the actual data recorded for that day, we can see that our model was accurate and came within the expected 2 degrees. The actual max temperature was 66 degrees!! This is in line with our mean_absolute_error test and verifies that our model is accurately predicting the temperature within 2 degrees. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Future works: I'd like to be able to predict the weather a week in advance and possibly connect it to an api so that it automatically updates and we can continue to use our model."
   ]
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