weather_predict.ipynb

{
 "cells": [
  {
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   "id": "0d2e7dee-9a89-4b55-a098-fcc3ed584f19",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "weather_data=pd.read_csv(\"weather_data.csv\",index_col=\"DATE\")   \n",
    "#distinguish rows based on the DATE column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fbe4e02c-8698-4b20-969b-de21b306c3be",
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   "outputs": [
    {
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       "<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>PRCP</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>TMAX</th>\n",
       "      <th>TMIN</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-02</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-03</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-04</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-05</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-22</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-23</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-24</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-25</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-26</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22472 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                STATION           NAME  PRCP   TAVG  TMAX  TMIN\n",
       "DATE                                                           \n",
       "1960-01-01  IN005010600  AHMADABAD, IN   0.0    NaN   NaN   NaN\n",
       "1960-01-02  IN005010600  AHMADABAD, IN   0.0    NaN   NaN   NaN\n",
       "1960-01-03  IN005010600  AHMADABAD, IN   0.0   68.0  84.0  57.0\n",
       "1960-01-04  IN005010600  AHMADABAD, IN   0.0    NaN   NaN   NaN\n",
       "1960-01-05  IN005010600  AHMADABAD, IN   0.0    NaN   NaN   NaN\n",
       "...                 ...            ...   ...    ...   ...   ...\n",
       "2024-05-22  IN005010600  AHMADABAD, IN   NaN   99.0   NaN  88.0\n",
       "2024-05-23  IN005010600  AHMADABAD, IN   NaN  100.0   NaN  87.0\n",
       "2024-05-24  IN005010600  AHMADABAD, IN   NaN   98.0   NaN  86.0\n",
       "2024-05-25  IN005010600  AHMADABAD, IN   NaN   97.0   NaN   NaN\n",
       "2024-05-26  IN005010600  AHMADABAD, IN   NaN   96.0   NaN   NaN\n",
       "\n",
       "[22472 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
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   ],
   "source": [
    "weather_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "461c368d-6f16-47ab-97c0-4ec43813a69a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATION        0\n",
       "NAME           0\n",
       "PRCP        8451\n",
       "TAVG        3360\n",
       "TMAX       10325\n",
       "TMIN        8877\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data.apply(pd.isnull).sum()    #no of null values in each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e7cda96b-ff14-45f0-beb2-d1da24291418",
   "metadata": {},
   "outputs": [],
   "source": [
    "NaN_percent=weather_data.apply(pd.isnull).sum()/weather_data.shape[0]\n",
    "#percentage of NaN(null) values in each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3ee7b1cf-66ec-4519-8710-fe20a8ae503d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATION    0.000000\n",
       "NAME       0.000000\n",
       "PRCP       0.376068\n",
       "TAVG       0.149519\n",
       "TMAX       0.459461\n",
       "TMIN       0.395025\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NaN_percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ccd8c47b-e238-4db9-93fd-4fe8b9f426be",
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_columns=weather_data.columns[NaN_percent<0.4]\n",
    "#keep the columns where null percent is <40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "69b9a275-e8d2-4345-91bc-1df240eafaa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['STATION', 'NAME', 'PRCP', 'TAVG', 'TMIN'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "34aefa07-b51d-4206-8ffd-cd7dd1fee002",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_data=weather_data[valid_columns].copy()   \n",
    "#convert the data to only valid columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c7582b7-e04b-46c0-ad5a-724127ab88ad",
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       "      <th></th>\n",
       "      <th>STATION</th>\n",
       "      <th>NAME</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>TMIN</th>\n",
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       "      <th>DATE</th>\n",
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       "      <th>1960-01-01</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1960-01-03</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
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       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
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       "      <th>1960-01-04</th>\n",
       "      <td>IN005010600</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1960-01-05</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>2024-05-22</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "      <td>88.0</td>\n",
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       "    <tr>\n",
       "      <th>2024-05-23</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>87.0</td>\n",
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       "    <tr>\n",
       "      <th>2024-05-24</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-25</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-26</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.0</td>\n",
       "      <td>NaN</td>\n",
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       "<p>22472 rows × 5 columns</p>\n",
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      "text/plain": [
       "                STATION           NAME  PRCP   TAVG  TMIN\n",
       "DATE                                                     \n",
       "1960-01-01  IN005010600  AHMADABAD, IN   0.0    NaN   NaN\n",
       "1960-01-02  IN005010600  AHMADABAD, IN   0.0    NaN   NaN\n",
       "1960-01-03  IN005010600  AHMADABAD, IN   0.0   68.0  57.0\n",
       "1960-01-04  IN005010600  AHMADABAD, IN   0.0    NaN   NaN\n",
       "1960-01-05  IN005010600  AHMADABAD, IN   0.0    NaN   NaN\n",
       "...                 ...            ...   ...    ...   ...\n",
       "2024-05-22  IN005010600  AHMADABAD, IN   NaN   99.0  88.0\n",
       "2024-05-23  IN005010600  AHMADABAD, IN   NaN  100.0  87.0\n",
       "2024-05-24  IN005010600  AHMADABAD, IN   NaN   98.0  86.0\n",
       "2024-05-25  IN005010600  AHMADABAD, IN   NaN   97.0   NaN\n",
       "2024-05-26  IN005010600  AHMADABAD, IN   NaN   96.0   NaN\n",
       "\n",
       "[22472 rows x 5 columns]"
      ]
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     "execution_count": 9,
     "metadata": {},
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   "source": [
    "weather_data"
   ]
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  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "14c59229-2e68-4df5-b6c1-77c87e5e78c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#fill the missing values with the last non-missing values\n",
    "#This works because there is not much tragic change in weather conditions in a day\n",
    "\n",
    "weather_data=weather_data.ffill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a90e312a-aec1-411d-b6a8-9562c542f679",
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   "outputs": [
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       "      <th>NAME</th>\n",
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       "      <th>1960-01-01</th>\n",
       "      <td>IN005010600</td>\n",
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       "      <th>1960-01-03</th>\n",
       "      <td>IN005010600</td>\n",
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       "      <td>68.0</td>\n",
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       "      <th>1960-01-04</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
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       "      <th>1960-01-05</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
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       "      <th>2024-05-22</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>88.0</td>\n",
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       "      <th>2024-05-23</th>\n",
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       "      <td>87.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>86.0</td>\n",
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       "      <th>2024-05-25</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-26</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22472 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                STATION           NAME  PRCP   TAVG  TMIN\n",
       "DATE                                                     \n",
       "1960-01-01  IN005010600  AHMADABAD, IN   0.0    NaN   NaN\n",
       "1960-01-02  IN005010600  AHMADABAD, IN   0.0    NaN   NaN\n",
       "1960-01-03  IN005010600  AHMADABAD, IN   0.0   68.0  57.0\n",
       "1960-01-04  IN005010600  AHMADABAD, IN   0.0   68.0  57.0\n",
       "1960-01-05  IN005010600  AHMADABAD, IN   0.0   68.0  57.0\n",
       "...                 ...            ...   ...    ...   ...\n",
       "2024-05-22  IN005010600  AHMADABAD, IN   0.0   99.0  88.0\n",
       "2024-05-23  IN005010600  AHMADABAD, IN   0.0  100.0  87.0\n",
       "2024-05-24  IN005010600  AHMADABAD, IN   0.0   98.0  86.0\n",
       "2024-05-25  IN005010600  AHMADABAD, IN   0.0   97.0  86.0\n",
       "2024-05-26  IN005010600  AHMADABAD, IN   0.0   96.0  86.0\n",
       "\n",
       "[22472 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "19a040df-9f76-45ec-bdbc-cdf79a924b6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_data.iloc[0:2] = weather_data.iloc[2]\n",
    "#fill the first two rows as same as the third row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "880177f8-8971-4269-9cc5-9ec779a10019",
   "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>PRCP</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>TMIN</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>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-02</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-03</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-04</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-05</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</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>2024-05-22</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-23</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-24</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-25</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-26</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22472 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                STATION           NAME  PRCP   TAVG  TMIN\n",
       "DATE                                                     \n",
       "1960-01-01  IN005010600  AHMADABAD, IN   0.0   68.0  57.0\n",
       "1960-01-02  IN005010600  AHMADABAD, IN   0.0   68.0  57.0\n",
       "1960-01-03  IN005010600  AHMADABAD, IN   0.0   68.0  57.0\n",
       "1960-01-04  IN005010600  AHMADABAD, IN   0.0   68.0  57.0\n",
       "1960-01-05  IN005010600  AHMADABAD, IN   0.0   68.0  57.0\n",
       "...                 ...            ...   ...    ...   ...\n",
       "2024-05-22  IN005010600  AHMADABAD, IN   0.0   99.0  88.0\n",
       "2024-05-23  IN005010600  AHMADABAD, IN   0.0  100.0  87.0\n",
       "2024-05-24  IN005010600  AHMADABAD, IN   0.0   98.0  86.0\n",
       "2024-05-25  IN005010600  AHMADABAD, IN   0.0   97.0  86.0\n",
       "2024-05-26  IN005010600  AHMADABAD, IN   0.0   96.0  86.0\n",
       "\n",
       "[22472 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "25a2c5a3-aadd-4620-8062-3294422fdf30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATION    0\n",
       "NAME       0\n",
       "PRCP       0\n",
       "TAVG       0\n",
       "TMIN       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data.apply(pd.isnull).sum() \n",
    "#check null values in each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a40604de-b016-4c50-8427-01e522ba21a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATION     object\n",
       "NAME        object\n",
       "PRCP       float64\n",
       "TAVG       float64\n",
       "TMIN       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data.dtypes   #to get data types of the columns\n",
    "#object data type usually indicates string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0a8fdac3-93ca-42cf-9486-634f85203d06",
   "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",
       "       '2024-05-17', '2024-05-18', '2024-05-19', '2024-05-20', '2024-05-21',\n",
       "       '2024-05-22', '2024-05-23', '2024-05-24', '2024-05-25', '2024-05-26'],\n",
       "      dtype='object', name='DATE', length=22472)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data.index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21fed3de-0ba8-410f-84bc-fe4c6f7d5ccb",
   "metadata": {},
   "source": [
    "DATE is stored as object. But we will convert it to date to make further processing easier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9d4447b1-4f67-4726-ad66-6931afe2bf31",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_data.index=pd.to_datetime(weather_data.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a32b6239-7aad-4428-b6a1-4afc34b7b044",
   "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",
       "               '2024-05-17', '2024-05-18', '2024-05-19', '2024-05-20',\n",
       "               '2024-05-21', '2024-05-22', '2024-05-23', '2024-05-24',\n",
       "               '2024-05-25', '2024-05-26'],\n",
       "              dtype='datetime64[ns]', name='DATE', length=22472, freq=None)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bc47fe06-832f-48c1-9fb9-aac67f14850e",
   "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=22472)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data.index.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "18a9caf7-519d-4094-8bbd-3c1775b92909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE\n",
       "1960    366\n",
       "1961    365\n",
       "1962    365\n",
       "1963    365\n",
       "1964    366\n",
       "       ... \n",
       "2020    362\n",
       "2021    356\n",
       "2022    358\n",
       "2023    365\n",
       "2024    141\n",
       "Name: count, Length: 63, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data.index.year.value_counts().sort_index()\n",
    "#gives count of how many times a year appears in the data and sorts based on the year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cb3cf549-4e6a-4f8b-9d90-63ef07d43181",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='DATE'>"
      ]
     },
     "execution_count": 21,
     "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": [
    "weather_data[\"TAVG\"].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "948148b6-6092-4995-b41f-981ffc26dd4f",
   "metadata": {},
   "source": [
    "pd.set_option('display.max_rows', None)  # None means unlimited rows\n",
    "pd.set_option('display.max_columns', None)  # None means unlimited columns\n",
    "pd.set_option('display.max_colwidth', None)  # Set the max column width\n",
    "pd.set_option('display.width', None)  # Set the display width\n",
    "#above lines are to display complete data\n",
    "print(weather_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47c67f95-3e6a-430b-8099-be75b3ca2854",
   "metadata": {},
   "source": [
    "#### Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c45490b9-f35f-48cb-af1d-02d2b2955b9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_data[\"target\"]=weather_data.shift(-1)[\"TAVG\"]\n",
    "\n",
    "#creating target for prediction of 'TAVG'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "995e78a3-a901-43ae-94de-402f62298aad",
   "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>PRCP</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>TMIN</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-02</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-03</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-04</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-05</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-22</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-23</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-24</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-25</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-26</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22472 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                STATION           NAME  PRCP   TAVG  TMIN  target\n",
       "DATE                                                             \n",
       "1960-01-01  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0\n",
       "1960-01-02  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0\n",
       "1960-01-03  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0\n",
       "1960-01-04  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0\n",
       "1960-01-05  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0\n",
       "...                 ...            ...   ...    ...   ...     ...\n",
       "2024-05-22  IN005010600  AHMADABAD, IN   0.0   99.0  88.0   100.0\n",
       "2024-05-23  IN005010600  AHMADABAD, IN   0.0  100.0  87.0    98.0\n",
       "2024-05-24  IN005010600  AHMADABAD, IN   0.0   98.0  86.0    97.0\n",
       "2024-05-25  IN005010600  AHMADABAD, IN   0.0   97.0  86.0    96.0\n",
       "2024-05-26  IN005010600  AHMADABAD, IN   0.0   96.0  86.0     NaN\n",
       "\n",
       "[22472 rows x 6 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2ee0a19-f28f-44cb-a95b-cae413cebf5d",
   "metadata": {},
   "source": [
    "The last block in target column is null as we dont have tomorrow's data here\n",
    "So we can fill the last block using the value of the previous block\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "985038c2-4979-4c12-9b8b-7a06edef619c",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_data.iloc[-1, -1] = weather_data.iloc[-2, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e38fc99b-70f6-49fb-ba54-3ad611607b69",
   "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>PRCP</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>TMIN</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-02</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-03</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-04</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-05</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-22</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-23</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-24</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-25</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-26</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22472 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                STATION           NAME  PRCP   TAVG  TMIN  target\n",
       "DATE                                                             \n",
       "1960-01-01  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0\n",
       "1960-01-02  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0\n",
       "1960-01-03  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0\n",
       "1960-01-04  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0\n",
       "1960-01-05  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0\n",
       "...                 ...            ...   ...    ...   ...     ...\n",
       "2024-05-22  IN005010600  AHMADABAD, IN   0.0   99.0  88.0   100.0\n",
       "2024-05-23  IN005010600  AHMADABAD, IN   0.0  100.0  87.0    98.0\n",
       "2024-05-24  IN005010600  AHMADABAD, IN   0.0   98.0  86.0    97.0\n",
       "2024-05-25  IN005010600  AHMADABAD, IN   0.0   97.0  86.0    96.0\n",
       "2024-05-26  IN005010600  AHMADABAD, IN   0.0   96.0  86.0    96.0\n",
       "\n",
       "[22472 rows x 6 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "891d449e-513d-49eb-8e9c-fd21c7c94bec",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0be3b343-7fcf-41f7-9b42-0d735c4cd945",
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge_reg=Ridge(alpha=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61196b15-d5bd-4150-b2b7-cec670d78dfa",
   "metadata": {},
   "source": [
    "Now we have to create a list of predictor cols. The cols of data for which we are making the prediction. This will include cols except name, station and target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b21e3ac6-d507-4f9c-bb75-c2c017610abb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True,  True,  True, False])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "~weather_data.columns.isin([\"NAME\",\"STATION\",\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4831456a-9153-4d49-9570-e52f21b96d9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor_cols=weather_data.columns[~weather_data.columns.isin([\"NAME\",\"STATION\",\"target\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "075b9a42-db19-4dde-b21e-d3aa797c6d24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PRCP', 'TAVG', 'TMIN'], dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictor_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "071ca6aa-162b-40bd-abdd-1ee216d90f24",
   "metadata": {},
   "outputs": [],
   "source": [
    "def back_test(weather_data,model,predictor_cols,start=3650,step=90):\n",
    "    all_predictions=[]\n",
    "\n",
    "    for i in range(start,weather_data.shape[0],step):\n",
    "        train=weather_data.iloc[:i,:]\n",
    "        test=weather_data.iloc[i:(i+step),:]\n",
    "\n",
    "        model.fit(train[predictor_cols],train[\"target\"])\n",
    "\n",
    "        preds=model.predict(test[predictor_cols])\n",
    "\n",
    "        preds=pd.Series(preds,index=test.index)  #converting numpy array into pandas series data\n",
    "        combined=pd.concat([test[\"target\"],preds],axis=1)  #axis=1 -> treat everything as separate cols\n",
    "\n",
    "        combined.columns=[\"actual\",\"prediction\"]\n",
    "\n",
    "        combined[\"diff\"]=(combined[\"prediction\"]-combined[\"actual\"]).abs()\n",
    "        #difference between the prediction and the actual value\n",
    "\n",
    "        all_predictions.append(combined)\n",
    "\n",
    "    return pd.concat(all_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0e4708a8-fcda-4531-816b-21e0f20a3669",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=back_test(weather_data,ridge_reg,predictor_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e06a20b1-f2f0-4aea-9b88-62715162de47",
   "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>prediction</th>\n",
       "      <th>diff</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>1969-12-30</th>\n",
       "      <td>66.0</td>\n",
       "      <td>66.034907</td>\n",
       "      <td>0.034907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969-12-31</th>\n",
       "      <td>66.0</td>\n",
       "      <td>66.034907</td>\n",
       "      <td>0.034907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01</th>\n",
       "      <td>66.0</td>\n",
       "      <td>66.034907</td>\n",
       "      <td>0.034907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-02</th>\n",
       "      <td>66.0</td>\n",
       "      <td>66.034907</td>\n",
       "      <td>0.034907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-03</th>\n",
       "      <td>66.0</td>\n",
       "      <td>66.034907</td>\n",
       "      <td>0.034907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-22</th>\n",
       "      <td>100.0</td>\n",
       "      <td>98.365150</td>\n",
       "      <td>1.634850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-23</th>\n",
       "      <td>98.0</td>\n",
       "      <td>99.242090</td>\n",
       "      <td>1.242090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-24</th>\n",
       "      <td>97.0</td>\n",
       "      <td>97.353888</td>\n",
       "      <td>0.353888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-25</th>\n",
       "      <td>96.0</td>\n",
       "      <td>96.432173</td>\n",
       "      <td>0.432173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-26</th>\n",
       "      <td>96.0</td>\n",
       "      <td>95.510459</td>\n",
       "      <td>0.489541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18822 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            actual  prediction      diff\n",
       "DATE                                    \n",
       "1969-12-30    66.0   66.034907  0.034907\n",
       "1969-12-31    66.0   66.034907  0.034907\n",
       "1970-01-01    66.0   66.034907  0.034907\n",
       "1970-01-02    66.0   66.034907  0.034907\n",
       "1970-01-03    66.0   66.034907  0.034907\n",
       "...            ...         ...       ...\n",
       "2024-05-22   100.0   98.365150  1.634850\n",
       "2024-05-23    98.0   99.242090  1.242090\n",
       "2024-05-24    97.0   97.353888  0.353888\n",
       "2024-05-25    96.0   96.432173  0.432173\n",
       "2024-05-26    96.0   95.510459  0.489541\n",
       "\n",
       "[18822 rows x 3 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "734d09b5-c05d-42a0-9c7a-b8f59c77689a",
   "metadata": {},
   "source": [
    "Analysing error in the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bf5839e2-97f7-4029-b3b1-f315e010af42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8295480813685618"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[\"diff\"].mean()\n",
    "\n",
    "#mean absolute error in the predicted values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "839d0f16-05a0-4696-8e6b-378b7ecdaa49",
   "metadata": {},
   "outputs": [],
   "source": [
    "def percent_err(val1,val2):\n",
    "    return (val2-val1)/val1\n",
    "\n",
    "#percentage error function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "32dd2ff3-aa57-44e9-b5c1-113e4bf5686b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rolling_avg(weather,days,col):                   #computing the rolling mean\n",
    "    label=f\"rolling_avg_{days}-{col}\"\n",
    "\n",
    "    weather[label]=weather[col].rolling(days).mean()\n",
    "    weather[f\"{label}_percent\"]= percent_err(weather[label],weather[col])\n",
    "\n",
    "    return weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2eec5c5f-e8ec-4610-9f5c-d86fd81fde9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "rolling_days=[3,14]\n",
    "\n",
    "for day in rolling_days:\n",
    "    for col in [\"TAVG\",\"TMIN\",\"PRCP\"]:\n",
    "        weather_data=rolling_avg(weather_data,day,col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "59c8a46f-259e-4594-995e-300fb739a6d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>PRCP</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>TMIN</th>\n",
       "      <th>target</th>\n",
       "      <th>rolling_avg_3-TAVG</th>\n",
       "      <th>rolling_avg_3-TAVG_percent</th>\n",
       "      <th>rolling_avg_3-TMIN</th>\n",
       "      <th>rolling_avg_3-TMIN_percent</th>\n",
       "      <th>rolling_avg_3-PRCP</th>\n",
       "      <th>rolling_avg_3-PRCP_percent</th>\n",
       "      <th>rolling_avg_14-TAVG</th>\n",
       "      <th>rolling_avg_14-TAVG_percent</th>\n",
       "      <th>rolling_avg_14-TMIN</th>\n",
       "      <th>rolling_avg_14-TMIN_percent</th>\n",
       "      <th>rolling_avg_14-PRCP</th>\n",
       "      <th>rolling_avg_14-PRCP_percent</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-02</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-03</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</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>1960-01-04</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</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>1960-01-05</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-22</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>98.666667</td>\n",
       "      <td>0.003378</td>\n",
       "      <td>87.333333</td>\n",
       "      <td>0.007634</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.857143</td>\n",
       "      <td>0.043675</td>\n",
       "      <td>83.785714</td>\n",
       "      <td>0.050298</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-23</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>87.333333</td>\n",
       "      <td>-0.003817</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.357143</td>\n",
       "      <td>0.048689</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-24</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>-0.010101</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>-0.011494</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.714286</td>\n",
       "      <td>0.023881</td>\n",
       "      <td>84.142857</td>\n",
       "      <td>0.022071</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-25</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>98.333333</td>\n",
       "      <td>-0.013559</td>\n",
       "      <td>86.333333</td>\n",
       "      <td>-0.003861</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.928571</td>\n",
       "      <td>0.011169</td>\n",
       "      <td>84.285714</td>\n",
       "      <td>0.020339</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-26</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.071429</td>\n",
       "      <td>-0.000743</td>\n",
       "      <td>84.428571</td>\n",
       "      <td>0.018613</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22472 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                STATION           NAME  PRCP   TAVG  TMIN  target  \\\n",
       "DATE                                                                \n",
       "1960-01-01  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0   \n",
       "1960-01-02  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0   \n",
       "1960-01-03  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0   \n",
       "1960-01-04  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0   \n",
       "1960-01-05  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0   \n",
       "...                 ...            ...   ...    ...   ...     ...   \n",
       "2024-05-22  IN005010600  AHMADABAD, IN   0.0   99.0  88.0   100.0   \n",
       "2024-05-23  IN005010600  AHMADABAD, IN   0.0  100.0  87.0    98.0   \n",
       "2024-05-24  IN005010600  AHMADABAD, IN   0.0   98.0  86.0    97.0   \n",
       "2024-05-25  IN005010600  AHMADABAD, IN   0.0   97.0  86.0    96.0   \n",
       "2024-05-26  IN005010600  AHMADABAD, IN   0.0   96.0  86.0    96.0   \n",
       "\n",
       "            rolling_avg_3-TAVG  rolling_avg_3-TAVG_percent  \\\n",
       "DATE                                                         \n",
       "1960-01-01                 NaN                         NaN   \n",
       "1960-01-02                 NaN                         NaN   \n",
       "1960-01-03           68.000000                    0.000000   \n",
       "1960-01-04           68.000000                    0.000000   \n",
       "1960-01-05           68.000000                    0.000000   \n",
       "...                        ...                         ...   \n",
       "2024-05-22           98.666667                    0.003378   \n",
       "2024-05-23           99.000000                    0.010101   \n",
       "2024-05-24           99.000000                   -0.010101   \n",
       "2024-05-25           98.333333                   -0.013559   \n",
       "2024-05-26           97.000000                   -0.010309   \n",
       "\n",
       "            rolling_avg_3-TMIN  rolling_avg_3-TMIN_percent  \\\n",
       "DATE                                                         \n",
       "1960-01-01                 NaN                         NaN   \n",
       "1960-01-02                 NaN                         NaN   \n",
       "1960-01-03           57.000000                    0.000000   \n",
       "1960-01-04           57.000000                    0.000000   \n",
       "1960-01-05           57.000000                    0.000000   \n",
       "...                        ...                         ...   \n",
       "2024-05-22           87.333333                    0.007634   \n",
       "2024-05-23           87.333333                   -0.003817   \n",
       "2024-05-24           87.000000                   -0.011494   \n",
       "2024-05-25           86.333333                   -0.003861   \n",
       "2024-05-26           86.000000                    0.000000   \n",
       "\n",
       "            rolling_avg_3-PRCP  rolling_avg_3-PRCP_percent  \\\n",
       "DATE                                                         \n",
       "1960-01-01                 NaN                         NaN   \n",
       "1960-01-02                 NaN                         NaN   \n",
       "1960-01-03                 0.0                         NaN   \n",
       "1960-01-04                 0.0                         NaN   \n",
       "1960-01-05                 0.0                         NaN   \n",
       "...                        ...                         ...   \n",
       "2024-05-22                 0.0                         NaN   \n",
       "2024-05-23                 0.0                         NaN   \n",
       "2024-05-24                 0.0                         NaN   \n",
       "2024-05-25                 0.0                         NaN   \n",
       "2024-05-26                 0.0                         NaN   \n",
       "\n",
       "            rolling_avg_14-TAVG  rolling_avg_14-TAVG_percent  \\\n",
       "DATE                                                           \n",
       "1960-01-01                  NaN                          NaN   \n",
       "1960-01-02                  NaN                          NaN   \n",
       "1960-01-03                  NaN                          NaN   \n",
       "1960-01-04                  NaN                          NaN   \n",
       "1960-01-05                  NaN                          NaN   \n",
       "...                         ...                          ...   \n",
       "2024-05-22            94.857143                     0.043675   \n",
       "2024-05-23            95.357143                     0.048689   \n",
       "2024-05-24            95.714286                     0.023881   \n",
       "2024-05-25            95.928571                     0.011169   \n",
       "2024-05-26            96.071429                    -0.000743   \n",
       "\n",
       "            rolling_avg_14-TMIN  rolling_avg_14-TMIN_percent  \\\n",
       "DATE                                                           \n",
       "1960-01-01                  NaN                          NaN   \n",
       "1960-01-02                  NaN                          NaN   \n",
       "1960-01-03                  NaN                          NaN   \n",
       "1960-01-04                  NaN                          NaN   \n",
       "1960-01-05                  NaN                          NaN   \n",
       "...                         ...                          ...   \n",
       "2024-05-22            83.785714                     0.050298   \n",
       "2024-05-23            84.000000                     0.035714   \n",
       "2024-05-24            84.142857                     0.022071   \n",
       "2024-05-25            84.285714                     0.020339   \n",
       "2024-05-26            84.428571                     0.018613   \n",
       "\n",
       "            rolling_avg_14-PRCP  rolling_avg_14-PRCP_percent  \n",
       "DATE                                                          \n",
       "1960-01-01                  NaN                          NaN  \n",
       "1960-01-02                  NaN                          NaN  \n",
       "1960-01-03                  NaN                          NaN  \n",
       "1960-01-04                  NaN                          NaN  \n",
       "1960-01-05                  NaN                          NaN  \n",
       "...                         ...                          ...  \n",
       "2024-05-22                  0.0                          NaN  \n",
       "2024-05-23                  0.0                          NaN  \n",
       "2024-05-24                  0.0                          NaN  \n",
       "2024-05-25                  0.0                          NaN  \n",
       "2024-05-26                  0.0                          NaN  \n",
       "\n",
       "[22472 rows x 18 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbfc413b-9d3a-4c92-b556-d56a7ef7865e",
   "metadata": {},
   "source": [
    "Now, as we can see, we have some missing values in this data. The first 14 rows contain NULL values. We need to fix this!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "33723a0c-00f7-4fbe-a3d9-70c6880f475a",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_data=weather_data.iloc[14:,:]    #cut out the first 14 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "085c391c-f4e7-461e-aa95-d08887d7ca91",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATION</th>\n",
       "      <th>NAME</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>TMIN</th>\n",
       "      <th>target</th>\n",
       "      <th>rolling_avg_3-TAVG</th>\n",
       "      <th>rolling_avg_3-TAVG_percent</th>\n",
       "      <th>rolling_avg_3-TMIN</th>\n",
       "      <th>rolling_avg_3-TMIN_percent</th>\n",
       "      <th>rolling_avg_3-PRCP</th>\n",
       "      <th>rolling_avg_3-PRCP_percent</th>\n",
       "      <th>rolling_avg_14-TAVG</th>\n",
       "      <th>rolling_avg_14-TAVG_percent</th>\n",
       "      <th>rolling_avg_14-TMIN</th>\n",
       "      <th>rolling_avg_14-TMIN_percent</th>\n",
       "      <th>rolling_avg_14-PRCP</th>\n",
       "      <th>rolling_avg_14-PRCP_percent</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>1960-01-15</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017857</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-16</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017857</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-17</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017857</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-18</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017857</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-19</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017857</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>2024-05-22</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>98.666667</td>\n",
       "      <td>0.003378</td>\n",
       "      <td>87.333333</td>\n",
       "      <td>0.007634</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.857143</td>\n",
       "      <td>0.043675</td>\n",
       "      <td>83.785714</td>\n",
       "      <td>0.050298</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-23</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>87.333333</td>\n",
       "      <td>-0.003817</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.357143</td>\n",
       "      <td>0.048689</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-24</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>-0.010101</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>-0.011494</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.714286</td>\n",
       "      <td>0.023881</td>\n",
       "      <td>84.142857</td>\n",
       "      <td>0.022071</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-25</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>98.333333</td>\n",
       "      <td>-0.013559</td>\n",
       "      <td>86.333333</td>\n",
       "      <td>-0.003861</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.928571</td>\n",
       "      <td>0.011169</td>\n",
       "      <td>84.285714</td>\n",
       "      <td>0.020339</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-26</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.071429</td>\n",
       "      <td>-0.000743</td>\n",
       "      <td>84.428571</td>\n",
       "      <td>0.018613</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22458 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                STATION           NAME  PRCP   TAVG  TMIN  target  \\\n",
       "DATE                                                                \n",
       "1960-01-15  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0   \n",
       "1960-01-16  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0   \n",
       "1960-01-17  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0   \n",
       "1960-01-18  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0   \n",
       "1960-01-19  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0   \n",
       "...                 ...            ...   ...    ...   ...     ...   \n",
       "2024-05-22  IN005010600  AHMADABAD, IN   0.0   99.0  88.0   100.0   \n",
       "2024-05-23  IN005010600  AHMADABAD, IN   0.0  100.0  87.0    98.0   \n",
       "2024-05-24  IN005010600  AHMADABAD, IN   0.0   98.0  86.0    97.0   \n",
       "2024-05-25  IN005010600  AHMADABAD, IN   0.0   97.0  86.0    96.0   \n",
       "2024-05-26  IN005010600  AHMADABAD, IN   0.0   96.0  86.0    96.0   \n",
       "\n",
       "            rolling_avg_3-TAVG  rolling_avg_3-TAVG_percent  \\\n",
       "DATE                                                         \n",
       "1960-01-15           68.000000                    0.000000   \n",
       "1960-01-16           68.000000                    0.000000   \n",
       "1960-01-17           68.000000                    0.000000   \n",
       "1960-01-18           68.000000                    0.000000   \n",
       "1960-01-19           68.000000                    0.000000   \n",
       "...                        ...                         ...   \n",
       "2024-05-22           98.666667                    0.003378   \n",
       "2024-05-23           99.000000                    0.010101   \n",
       "2024-05-24           99.000000                   -0.010101   \n",
       "2024-05-25           98.333333                   -0.013559   \n",
       "2024-05-26           97.000000                   -0.010309   \n",
       "\n",
       "            rolling_avg_3-TMIN  rolling_avg_3-TMIN_percent  \\\n",
       "DATE                                                         \n",
       "1960-01-15           57.000000                    0.000000   \n",
       "1960-01-16           57.000000                    0.000000   \n",
       "1960-01-17           57.000000                    0.000000   \n",
       "1960-01-18           57.000000                    0.000000   \n",
       "1960-01-19           57.000000                    0.000000   \n",
       "...                        ...                         ...   \n",
       "2024-05-22           87.333333                    0.007634   \n",
       "2024-05-23           87.333333                   -0.003817   \n",
       "2024-05-24           87.000000                   -0.011494   \n",
       "2024-05-25           86.333333                   -0.003861   \n",
       "2024-05-26           86.000000                    0.000000   \n",
       "\n",
       "            rolling_avg_3-PRCP  rolling_avg_3-PRCP_percent  \\\n",
       "DATE                                                         \n",
       "1960-01-15                 0.0                         NaN   \n",
       "1960-01-16                 0.0                         NaN   \n",
       "1960-01-17                 0.0                         NaN   \n",
       "1960-01-18                 0.0                         NaN   \n",
       "1960-01-19                 0.0                         NaN   \n",
       "...                        ...                         ...   \n",
       "2024-05-22                 0.0                         NaN   \n",
       "2024-05-23                 0.0                         NaN   \n",
       "2024-05-24                 0.0                         NaN   \n",
       "2024-05-25                 0.0                         NaN   \n",
       "2024-05-26                 0.0                         NaN   \n",
       "\n",
       "            rolling_avg_14-TAVG  rolling_avg_14-TAVG_percent  \\\n",
       "DATE                                                           \n",
       "1960-01-15            68.000000                     0.000000   \n",
       "1960-01-16            68.000000                     0.000000   \n",
       "1960-01-17            68.000000                     0.000000   \n",
       "1960-01-18            68.000000                     0.000000   \n",
       "1960-01-19            68.000000                     0.000000   \n",
       "...                         ...                          ...   \n",
       "2024-05-22            94.857143                     0.043675   \n",
       "2024-05-23            95.357143                     0.048689   \n",
       "2024-05-24            95.714286                     0.023881   \n",
       "2024-05-25            95.928571                     0.011169   \n",
       "2024-05-26            96.071429                    -0.000743   \n",
       "\n",
       "            rolling_avg_14-TMIN  rolling_avg_14-TMIN_percent  \\\n",
       "DATE                                                           \n",
       "1960-01-15            57.000000                     0.000000   \n",
       "1960-01-16            57.000000                     0.000000   \n",
       "1960-01-17            57.000000                     0.000000   \n",
       "1960-01-18            57.000000                     0.000000   \n",
       "1960-01-19            57.000000                     0.000000   \n",
       "...                         ...                          ...   \n",
       "2024-05-22            83.785714                     0.050298   \n",
       "2024-05-23            84.000000                     0.035714   \n",
       "2024-05-24            84.142857                     0.022071   \n",
       "2024-05-25            84.285714                     0.020339   \n",
       "2024-05-26            84.428571                     0.018613   \n",
       "\n",
       "            rolling_avg_14-PRCP  rolling_avg_14-PRCP_percent  \n",
       "DATE                                                          \n",
       "1960-01-15             0.017857                         -1.0  \n",
       "1960-01-16             0.017857                         -1.0  \n",
       "1960-01-17             0.017857                         -1.0  \n",
       "1960-01-18             0.017857                         -1.0  \n",
       "1960-01-19             0.017857                         -1.0  \n",
       "...                         ...                          ...  \n",
       "2024-05-22             0.000000                          NaN  \n",
       "2024-05-23             0.000000                          NaN  \n",
       "2024-05-24             0.000000                          NaN  \n",
       "2024-05-25             0.000000                          NaN  \n",
       "2024-05-26             0.000000                          NaN  \n",
       "\n",
       "[22458 rows x 18 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f9f21eb-d379-4579-84fa-e75f16669428",
   "metadata": {},
   "source": [
    "There are still some remaining NULL values. We shall fix this too!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c704bbc6-643d-4e2e-bcab-87d811b8591a",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_data=weather_data.fillna(0)  \n",
    "#filling the null values with zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "213b5f5d-9dcb-4eee-b09c-3b3488a6bc7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATION</th>\n",
       "      <th>NAME</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>TMIN</th>\n",
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       "      <th>rolling_avg_3-TAVG_percent</th>\n",
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       "      <th>rolling_avg_14-TAVG_percent</th>\n",
       "      <th>rolling_avg_14-TMIN</th>\n",
       "      <th>rolling_avg_14-TMIN_percent</th>\n",
       "      <th>rolling_avg_14-PRCP</th>\n",
       "      <th>rolling_avg_14-PRCP_percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-01-15</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017857</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-16</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017857</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-17</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017857</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-18</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017857</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-19</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2024-05-22</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>100.0</td>\n",
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       "      <td>0.003378</td>\n",
       "      <td>87.333333</td>\n",
       "      <td>0.007634</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>94.857143</td>\n",
       "      <td>0.043675</td>\n",
       "      <td>83.785714</td>\n",
       "      <td>0.050298</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-23</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>87.333333</td>\n",
       "      <td>-0.003817</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95.357143</td>\n",
       "      <td>0.048689</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-24</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>-0.010101</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>-0.011494</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95.714286</td>\n",
       "      <td>0.023881</td>\n",
       "      <td>84.142857</td>\n",
       "      <td>0.022071</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-25</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>98.333333</td>\n",
       "      <td>-0.013559</td>\n",
       "      <td>86.333333</td>\n",
       "      <td>-0.003861</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95.928571</td>\n",
       "      <td>0.011169</td>\n",
       "      <td>84.285714</td>\n",
       "      <td>0.020339</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-26</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.071429</td>\n",
       "      <td>-0.000743</td>\n",
       "      <td>84.428571</td>\n",
       "      <td>0.018613</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22458 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                STATION           NAME  PRCP   TAVG  TMIN  target  \\\n",
       "DATE                                                                \n",
       "1960-01-15  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0   \n",
       "1960-01-16  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0   \n",
       "1960-01-17  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0   \n",
       "1960-01-18  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0   \n",
       "1960-01-19  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0   \n",
       "...                 ...            ...   ...    ...   ...     ...   \n",
       "2024-05-22  IN005010600  AHMADABAD, IN   0.0   99.0  88.0   100.0   \n",
       "2024-05-23  IN005010600  AHMADABAD, IN   0.0  100.0  87.0    98.0   \n",
       "2024-05-24  IN005010600  AHMADABAD, IN   0.0   98.0  86.0    97.0   \n",
       "2024-05-25  IN005010600  AHMADABAD, IN   0.0   97.0  86.0    96.0   \n",
       "2024-05-26  IN005010600  AHMADABAD, IN   0.0   96.0  86.0    96.0   \n",
       "\n",
       "            rolling_avg_3-TAVG  rolling_avg_3-TAVG_percent  \\\n",
       "DATE                                                         \n",
       "1960-01-15           68.000000                    0.000000   \n",
       "1960-01-16           68.000000                    0.000000   \n",
       "1960-01-17           68.000000                    0.000000   \n",
       "1960-01-18           68.000000                    0.000000   \n",
       "1960-01-19           68.000000                    0.000000   \n",
       "...                        ...                         ...   \n",
       "2024-05-22           98.666667                    0.003378   \n",
       "2024-05-23           99.000000                    0.010101   \n",
       "2024-05-24           99.000000                   -0.010101   \n",
       "2024-05-25           98.333333                   -0.013559   \n",
       "2024-05-26           97.000000                   -0.010309   \n",
       "\n",
       "            rolling_avg_3-TMIN  rolling_avg_3-TMIN_percent  \\\n",
       "DATE                                                         \n",
       "1960-01-15           57.000000                    0.000000   \n",
       "1960-01-16           57.000000                    0.000000   \n",
       "1960-01-17           57.000000                    0.000000   \n",
       "1960-01-18           57.000000                    0.000000   \n",
       "1960-01-19           57.000000                    0.000000   \n",
       "...                        ...                         ...   \n",
       "2024-05-22           87.333333                    0.007634   \n",
       "2024-05-23           87.333333                   -0.003817   \n",
       "2024-05-24           87.000000                   -0.011494   \n",
       "2024-05-25           86.333333                   -0.003861   \n",
       "2024-05-26           86.000000                    0.000000   \n",
       "\n",
       "            rolling_avg_3-PRCP  rolling_avg_3-PRCP_percent  \\\n",
       "DATE                                                         \n",
       "1960-01-15                 0.0                         0.0   \n",
       "1960-01-16                 0.0                         0.0   \n",
       "1960-01-17                 0.0                         0.0   \n",
       "1960-01-18                 0.0                         0.0   \n",
       "1960-01-19                 0.0                         0.0   \n",
       "...                        ...                         ...   \n",
       "2024-05-22                 0.0                         0.0   \n",
       "2024-05-23                 0.0                         0.0   \n",
       "2024-05-24                 0.0                         0.0   \n",
       "2024-05-25                 0.0                         0.0   \n",
       "2024-05-26                 0.0                         0.0   \n",
       "\n",
       "            rolling_avg_14-TAVG  rolling_avg_14-TAVG_percent  \\\n",
       "DATE                                                           \n",
       "1960-01-15            68.000000                     0.000000   \n",
       "1960-01-16            68.000000                     0.000000   \n",
       "1960-01-17            68.000000                     0.000000   \n",
       "1960-01-18            68.000000                     0.000000   \n",
       "1960-01-19            68.000000                     0.000000   \n",
       "...                         ...                          ...   \n",
       "2024-05-22            94.857143                     0.043675   \n",
       "2024-05-23            95.357143                     0.048689   \n",
       "2024-05-24            95.714286                     0.023881   \n",
       "2024-05-25            95.928571                     0.011169   \n",
       "2024-05-26            96.071429                    -0.000743   \n",
       "\n",
       "            rolling_avg_14-TMIN  rolling_avg_14-TMIN_percent  \\\n",
       "DATE                                                           \n",
       "1960-01-15            57.000000                     0.000000   \n",
       "1960-01-16            57.000000                     0.000000   \n",
       "1960-01-17            57.000000                     0.000000   \n",
       "1960-01-18            57.000000                     0.000000   \n",
       "1960-01-19            57.000000                     0.000000   \n",
       "...                         ...                          ...   \n",
       "2024-05-22            83.785714                     0.050298   \n",
       "2024-05-23            84.000000                     0.035714   \n",
       "2024-05-24            84.142857                     0.022071   \n",
       "2024-05-25            84.285714                     0.020339   \n",
       "2024-05-26            84.428571                     0.018613   \n",
       "\n",
       "            rolling_avg_14-PRCP  rolling_avg_14-PRCP_percent  \n",
       "DATE                                                          \n",
       "1960-01-15             0.017857                         -1.0  \n",
       "1960-01-16             0.017857                         -1.0  \n",
       "1960-01-17             0.017857                         -1.0  \n",
       "1960-01-18             0.017857                         -1.0  \n",
       "1960-01-19             0.017857                         -1.0  \n",
       "...                         ...                          ...  \n",
       "2024-05-22             0.000000                          0.0  \n",
       "2024-05-23             0.000000                          0.0  \n",
       "2024-05-24             0.000000                          0.0  \n",
       "2024-05-25             0.000000                          0.0  \n",
       "2024-05-26             0.000000                          0.0  \n",
       "\n",
       "[22458 rows x 18 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c6176d47-c5fe-4450-bb41-39453846174f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def exp_mean(df):\n",
    "    return df.expanding(1).mean()\n",
    "#calculate the expanding mean of each col in a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ea0c0822-441a-426f-96d3-bca2a5cc1a0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in [\"TAVG\",\"TMIN\",\"PRCP\"]:\n",
    "    weather_data[f\"month_avg_{col}\"]=weather_data[col].groupby(weather_data.index.month,group_keys=False).apply(exp_mean)\n",
    "    #finding the averages in a given month\n",
    "\n",
    "    weather_data[f\"day_avg_{col}\"]=weather_data[col].groupby(weather_data.index.day_of_year,group_keys=False).apply(exp_mean)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8c84d23c-a6fd-40f4-a147-dc78dd4dd649",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATION</th>\n",
       "      <th>NAME</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>TMIN</th>\n",
       "      <th>target</th>\n",
       "      <th>rolling_avg_3-TAVG</th>\n",
       "      <th>rolling_avg_3-TAVG_percent</th>\n",
       "      <th>rolling_avg_3-TMIN</th>\n",
       "      <th>rolling_avg_3-TMIN_percent</th>\n",
       "      <th>...</th>\n",
       "      <th>rolling_avg_14-TMIN</th>\n",
       "      <th>rolling_avg_14-TMIN_percent</th>\n",
       "      <th>rolling_avg_14-PRCP</th>\n",
       "      <th>rolling_avg_14-PRCP_percent</th>\n",
       "      <th>month_avg_TAVG</th>\n",
       "      <th>day_avg_TAVG</th>\n",
       "      <th>month_avg_TMIN</th>\n",
       "      <th>day_avg_TMIN</th>\n",
       "      <th>month_avg_PRCP</th>\n",
       "      <th>day_avg_PRCP</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>1960-01-15</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017857</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-16</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017857</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-17</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017857</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-18</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017857</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-19</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017857</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>2024-05-22</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>98.666667</td>\n",
       "      <td>0.003378</td>\n",
       "      <td>87.333333</td>\n",
       "      <td>0.007634</td>\n",
       "      <td>...</td>\n",
       "      <td>83.785714</td>\n",
       "      <td>0.050298</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89.325157</td>\n",
       "      <td>89.730159</td>\n",
       "      <td>76.553758</td>\n",
       "      <td>77.412698</td>\n",
       "      <td>0.009849</td>\n",
       "      <td>0.001270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-23</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>87.333333</td>\n",
       "      <td>-0.003817</td>\n",
       "      <td>...</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89.330725</td>\n",
       "      <td>90.158730</td>\n",
       "      <td>76.559207</td>\n",
       "      <td>77.714286</td>\n",
       "      <td>0.009844</td>\n",
       "      <td>0.000635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-24</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>-0.010101</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>-0.011494</td>\n",
       "      <td>...</td>\n",
       "      <td>84.142857</td>\n",
       "      <td>0.022071</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89.335245</td>\n",
       "      <td>89.619048</td>\n",
       "      <td>76.564129</td>\n",
       "      <td>77.857143</td>\n",
       "      <td>0.009838</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-25</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>98.333333</td>\n",
       "      <td>-0.013559</td>\n",
       "      <td>86.333333</td>\n",
       "      <td>-0.003861</td>\n",
       "      <td>...</td>\n",
       "      <td>84.285714</td>\n",
       "      <td>0.020339</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89.339239</td>\n",
       "      <td>89.467742</td>\n",
       "      <td>76.569046</td>\n",
       "      <td>77.661290</td>\n",
       "      <td>0.009833</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-26</th>\n",
       "      <td>IN005010600</td>\n",
       "      <td>AHMADABAD, IN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>84.428571</td>\n",
       "      <td>0.018613</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89.342708</td>\n",
       "      <td>89.758065</td>\n",
       "      <td>76.573958</td>\n",
       "      <td>77.725806</td>\n",
       "      <td>0.009828</td>\n",
       "      <td>0.009516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22458 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                STATION           NAME  PRCP   TAVG  TMIN  target  \\\n",
       "DATE                                                                \n",
       "1960-01-15  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0   \n",
       "1960-01-16  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0   \n",
       "1960-01-17  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0   \n",
       "1960-01-18  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0   \n",
       "1960-01-19  IN005010600  AHMADABAD, IN   0.0   68.0  57.0    68.0   \n",
       "...                 ...            ...   ...    ...   ...     ...   \n",
       "2024-05-22  IN005010600  AHMADABAD, IN   0.0   99.0  88.0   100.0   \n",
       "2024-05-23  IN005010600  AHMADABAD, IN   0.0  100.0  87.0    98.0   \n",
       "2024-05-24  IN005010600  AHMADABAD, IN   0.0   98.0  86.0    97.0   \n",
       "2024-05-25  IN005010600  AHMADABAD, IN   0.0   97.0  86.0    96.0   \n",
       "2024-05-26  IN005010600  AHMADABAD, IN   0.0   96.0  86.0    96.0   \n",
       "\n",
       "            rolling_avg_3-TAVG  rolling_avg_3-TAVG_percent  \\\n",
       "DATE                                                         \n",
       "1960-01-15           68.000000                    0.000000   \n",
       "1960-01-16           68.000000                    0.000000   \n",
       "1960-01-17           68.000000                    0.000000   \n",
       "1960-01-18           68.000000                    0.000000   \n",
       "1960-01-19           68.000000                    0.000000   \n",
       "...                        ...                         ...   \n",
       "2024-05-22           98.666667                    0.003378   \n",
       "2024-05-23           99.000000                    0.010101   \n",
       "2024-05-24           99.000000                   -0.010101   \n",
       "2024-05-25           98.333333                   -0.013559   \n",
       "2024-05-26           97.000000                   -0.010309   \n",
       "\n",
       "            rolling_avg_3-TMIN  rolling_avg_3-TMIN_percent  ...  \\\n",
       "DATE                                                        ...   \n",
       "1960-01-15           57.000000                    0.000000  ...   \n",
       "1960-01-16           57.000000                    0.000000  ...   \n",
       "1960-01-17           57.000000                    0.000000  ...   \n",
       "1960-01-18           57.000000                    0.000000  ...   \n",
       "1960-01-19           57.000000                    0.000000  ...   \n",
       "...                        ...                         ...  ...   \n",
       "2024-05-22           87.333333                    0.007634  ...   \n",
       "2024-05-23           87.333333                   -0.003817  ...   \n",
       "2024-05-24           87.000000                   -0.011494  ...   \n",
       "2024-05-25           86.333333                   -0.003861  ...   \n",
       "2024-05-26           86.000000                    0.000000  ...   \n",
       "\n",
       "            rolling_avg_14-TMIN  rolling_avg_14-TMIN_percent  \\\n",
       "DATE                                                           \n",
       "1960-01-15            57.000000                     0.000000   \n",
       "1960-01-16            57.000000                     0.000000   \n",
       "1960-01-17            57.000000                     0.000000   \n",
       "1960-01-18            57.000000                     0.000000   \n",
       "1960-01-19            57.000000                     0.000000   \n",
       "...                         ...                          ...   \n",
       "2024-05-22            83.785714                     0.050298   \n",
       "2024-05-23            84.000000                     0.035714   \n",
       "2024-05-24            84.142857                     0.022071   \n",
       "2024-05-25            84.285714                     0.020339   \n",
       "2024-05-26            84.428571                     0.018613   \n",
       "\n",
       "            rolling_avg_14-PRCP  rolling_avg_14-PRCP_percent  month_avg_TAVG  \\\n",
       "DATE                                                                           \n",
       "1960-01-15             0.017857                         -1.0       68.000000   \n",
       "1960-01-16             0.017857                         -1.0       68.000000   \n",
       "1960-01-17             0.017857                         -1.0       68.000000   \n",
       "1960-01-18             0.017857                         -1.0       68.000000   \n",
       "1960-01-19             0.017857                         -1.0       68.000000   \n",
       "...                         ...                          ...             ...   \n",
       "2024-05-22             0.000000                          0.0       89.325157   \n",
       "2024-05-23             0.000000                          0.0       89.330725   \n",
       "2024-05-24             0.000000                          0.0       89.335245   \n",
       "2024-05-25             0.000000                          0.0       89.339239   \n",
       "2024-05-26             0.000000                          0.0       89.342708   \n",
       "\n",
       "            day_avg_TAVG  month_avg_TMIN  day_avg_TMIN  month_avg_PRCP  \\\n",
       "DATE                                                                     \n",
       "1960-01-15     68.000000       57.000000     57.000000        0.000000   \n",
       "1960-01-16     68.000000       57.000000     57.000000        0.000000   \n",
       "1960-01-17     68.000000       57.000000     57.000000        0.000000   \n",
       "1960-01-18     68.000000       57.000000     57.000000        0.000000   \n",
       "1960-01-19     68.000000       57.000000     57.000000        0.000000   \n",
       "...                  ...             ...           ...             ...   \n",
       "2024-05-22     89.730159       76.553758     77.412698        0.009849   \n",
       "2024-05-23     90.158730       76.559207     77.714286        0.009844   \n",
       "2024-05-24     89.619048       76.564129     77.857143        0.009838   \n",
       "2024-05-25     89.467742       76.569046     77.661290        0.009833   \n",
       "2024-05-26     89.758065       76.573958     77.725806        0.009828   \n",
       "\n",
       "            day_avg_PRCP  \n",
       "DATE                      \n",
       "1960-01-15      0.000000  \n",
       "1960-01-16      0.000000  \n",
       "1960-01-17      0.000000  \n",
       "1960-01-18      0.000000  \n",
       "1960-01-19      0.000000  \n",
       "...                  ...  \n",
       "2024-05-22      0.001270  \n",
       "2024-05-23      0.000635  \n",
       "2024-05-24      0.000000  \n",
       "2024-05-25      0.000000  \n",
       "2024-05-26      0.009516  \n",
       "\n",
       "[22458 rows x 24 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2ad17714-1067-491e-8e57-372c92bda476",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor_cols=weather_data.columns[~weather_data.columns.isin([\"NAME\",\"STATION\",\"target\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9883982a-8267-4533-965f-843f0c03bb41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PRCP', 'TAVG', 'TMIN', 'rolling_avg_3-TAVG',\n",
       "       'rolling_avg_3-TAVG_percent', 'rolling_avg_3-TMIN',\n",
       "       'rolling_avg_3-TMIN_percent', 'rolling_avg_3-PRCP',\n",
       "       'rolling_avg_3-PRCP_percent', 'rolling_avg_14-TAVG',\n",
       "       'rolling_avg_14-TAVG_percent', 'rolling_avg_14-TMIN',\n",
       "       'rolling_avg_14-TMIN_percent', 'rolling_avg_14-PRCP',\n",
       "       'rolling_avg_14-PRCP_percent', 'month_avg_TAVG', 'day_avg_TAVG',\n",
       "       'month_avg_TMIN', 'day_avg_TMIN', 'month_avg_PRCP', 'day_avg_PRCP'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictor_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "88946ed0-b69d-4371-9409-cd05901c46d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=back_test(weather_data,ridge_reg,predictor_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "8ae6fd51-3f73-41f7-b7d6-cedfc27382ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7797756073102218"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[\"diff\"].mean()    #mean absolute error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "bb7d1d10-d7ab-4934-9003-eecf04a7d62d",
   "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>prediction</th>\n",
       "      <th>diff</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>1976-06-01</th>\n",
       "      <td>76.0</td>\n",
       "      <td>92.854170</td>\n",
       "      <td>16.854170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-09-29</th>\n",
       "      <td>72.0</td>\n",
       "      <td>85.155570</td>\n",
       "      <td>13.155570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-06-08</th>\n",
       "      <td>80.0</td>\n",
       "      <td>92.598971</td>\n",
       "      <td>12.598971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-19</th>\n",
       "      <td>80.0</td>\n",
       "      <td>92.199832</td>\n",
       "      <td>12.199832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-15</th>\n",
       "      <td>81.0</td>\n",
       "      <td>93.170596</td>\n",
       "      <td>12.170596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-10-18</th>\n",
       "      <td>66.0</td>\n",
       "      <td>65.999560</td>\n",
       "      <td>0.000440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-22</th>\n",
       "      <td>92.0</td>\n",
       "      <td>91.999573</td>\n",
       "      <td>0.000427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-06-08</th>\n",
       "      <td>66.0</td>\n",
       "      <td>66.000330</td>\n",
       "      <td>0.000330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-15</th>\n",
       "      <td>93.0</td>\n",
       "      <td>93.000271</td>\n",
       "      <td>0.000271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-29</th>\n",
       "      <td>83.0</td>\n",
       "      <td>83.000209</td>\n",
       "      <td>0.000209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18808 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            actual  prediction       diff\n",
       "DATE                                     \n",
       "1976-06-01    76.0   92.854170  16.854170\n",
       "1980-09-29    72.0   85.155570  13.155570\n",
       "1998-06-08    80.0   92.598971  12.598971\n",
       "2012-04-19    80.0   92.199832  12.199832\n",
       "2019-04-15    81.0   93.170596  12.170596\n",
       "...            ...         ...        ...\n",
       "1970-10-18    66.0   65.999560   0.000440\n",
       "2010-06-22    92.0   91.999573   0.000427\n",
       "1970-06-08    66.0   66.000330   0.000330\n",
       "2011-06-15    93.0   93.000271   0.000271\n",
       "2017-07-29    83.0   83.000209   0.000209\n",
       "\n",
       "[18808 rows x 3 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.sort_values(\"diff\",ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3d18408-21d7-4220-9df1-3b7a98414a6c",
   "metadata": {},
   "source": [
    "Checking error ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f6a1efcf-322b-49fc-a317-101fb99fe0cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diff\n",
       "1.0     6051\n",
       "2.0     4168\n",
       "0.0     3909\n",
       "3.0     2320\n",
       "4.0     1182\n",
       "5.0      573\n",
       "6.0      306\n",
       "7.0      133\n",
       "8.0       83\n",
       "9.0       41\n",
       "10.0      22\n",
       "11.0      10\n",
       "12.0       7\n",
       "13.0       2\n",
       "17.0       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[\"diff\"].round().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "55c8b344-23dc-457f-8a15-a3b9f2679972",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diff\n",
       "0.0     3909\n",
       "1.0     6051\n",
       "2.0     4168\n",
       "3.0     2320\n",
       "4.0     1182\n",
       "5.0      573\n",
       "6.0      306\n",
       "7.0      133\n",
       "8.0       83\n",
       "9.0       41\n",
       "10.0      22\n",
       "11.0      10\n",
       "12.0       7\n",
       "13.0       2\n",
       "17.0       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[\"diff\"].round().value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "392dffb0-a169-440f-8037-c17b75404832",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='diff'>"
      ]
     },
     "execution_count": 70,
     "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": [
    "predictions[\"diff\"].round().value_counts().sort_index().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6dc2f291-9937-49c9-93cd-f21f56109ceb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}