1. Time Series Forecasting Process description(familiarize,day-number column).ipynb

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      "C:\\Users\\agboola_yusuf\\AppData\\Local\\Temp\\ipykernel_936\\865340377.py:9: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.\n",
      "  plt.style.use(\"seaborn-whitegrid\")\n",
      "C:\\Users\\agboola_yusuf\\AppData\\Local\\Temp\\ipykernel_936\\865340377.py:19: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.\n",
      "  plt.style.use(\"seaborn-whitegrid\")\n"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day of the Week</th>\n",
       "      <th>Temperature (°F)</th>\n",
       "      <th>Special Event</th>\n",
       "      <th>Sales ($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-06-01</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>55.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-06-02</td>\n",
       "      <td>Friday</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>47.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-06-03</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>50.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-06-04</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>61.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-06-05</td>\n",
       "      <td>Monday</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>39.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2023-08-25</td>\n",
       "      <td>Friday</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>43.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>2023-08-26</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>59.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2023-08-27</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>59.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2023-08-28</td>\n",
       "      <td>Monday</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>38.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2023-08-29</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>44.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>90 rows × 5 columns</p>\n",
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      "text/plain": [
       "         Date Day of the Week  Temperature (°F)  Special Event  Sales ($)\n",
       "0  2023-06-01        Thursday                80              0      55.33\n",
       "1  2023-06-02          Friday                74              0      47.17\n",
       "2  2023-06-03        Saturday                81              0      50.24\n",
       "3  2023-06-04          Sunday                90              0      61.28\n",
       "4  2023-06-05          Monday                73              0      39.73\n",
       "..        ...             ...               ...            ...        ...\n",
       "85 2023-08-25          Friday                70              0      43.43\n",
       "86 2023-08-26        Saturday                84              0      59.32\n",
       "87 2023-08-27          Sunday                78              0      59.93\n",
       "88 2023-08-28          Monday                70              0      38.00\n",
       "89 2023-08-29         Tuesday                80              0      44.33\n",
       "\n",
       "[90 rows x 5 columns]"
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     "execution_count": 1,
     "metadata": {},
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   "source": [
    "import datetime\n",
    "# import plotly packages \n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"seaborn-whitegrid\")\n",
    "%matplotlib inline\n",
    "from sklearn.metrics import mean_squared_error,r2_score,mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split, KFold\n",
    "from lightgbm import LGBMRegressor\n",
    "from catboost import CatBoostRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.preprocessing import StandardScaler,LabelEncoder\n",
    "le=LabelEncoder()\n",
    "#pd.options.display.max_rows=None\n",
    "plt.style.use(\"seaborn-whitegrid\")\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from tqdm.notebook import tqdm_notebook\n",
    "ls=pd.read_excel('Lemonade_sales_data_SET.xlsx')\n",
    "ls"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ece75a58",
   "metadata": {},
   "source": [
    "# Creating a new column \"Day Number\" to numerically represent each day sequentially.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "31a9ae39",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day of the Week</th>\n",
       "      <th>Temperature (°F)</th>\n",
       "      <th>Special Event</th>\n",
       "      <th>Sales ($)</th>\n",
       "      <th>Day Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-06-01</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>55.33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-06-02</td>\n",
       "      <td>Friday</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>47.17</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-06-03</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>50.24</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-06-04</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>61.28</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-06-05</td>\n",
       "      <td>Monday</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>39.73</td>\n",
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       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2023-08-25</td>\n",
       "      <td>Friday</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>43.43</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>2023-08-26</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>59.32</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2023-08-27</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>59.93</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2023-08-28</td>\n",
       "      <td>Monday</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>38.00</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2023-08-29</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>44.33</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Day of the Week  Temperature (°F)  Special Event  Sales ($)  \\\n",
       "0  2023-06-01        Thursday                80              0      55.33   \n",
       "1  2023-06-02          Friday                74              0      47.17   \n",
       "2  2023-06-03        Saturday                81              0      50.24   \n",
       "3  2023-06-04          Sunday                90              0      61.28   \n",
       "4  2023-06-05          Monday                73              0      39.73   \n",
       "..        ...             ...               ...            ...        ...   \n",
       "85 2023-08-25          Friday                70              0      43.43   \n",
       "86 2023-08-26        Saturday                84              0      59.32   \n",
       "87 2023-08-27          Sunday                78              0      59.93   \n",
       "88 2023-08-28          Monday                70              0      38.00   \n",
       "89 2023-08-29         Tuesday                80              0      44.33   \n",
       "\n",
       "    Day Number  \n",
       "0            1  \n",
       "1            2  \n",
       "2            3  \n",
       "3            4  \n",
       "4            5  \n",
       "..         ...  \n",
       "85          86  \n",
       "86          87  \n",
       "87          88  \n",
       "88          89  \n",
       "89          90  \n",
       "\n",
       "[90 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls['Day Number'] = range(1, len(ls) + 1)\n",
    "ls"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03a6f468",
   "metadata": {},
   "source": [
    "# preparing data  for forecasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ebfc8019",
   "metadata": {},
   "outputs": [
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       "      <th>year</th>\n",
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       "      <th>2</th>\n",
       "      <td>2023-06-03</td>\n",
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       "      <td>50.24</td>\n",
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       "      <td>3</td>\n",
       "      <td>90</td>\n",
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       "      <td>4</td>\n",
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       "      <td>2023-08-27</td>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>59.93</td>\n",
       "      <td>88</td>\n",
       "      <td>2023</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.118273</td>\n",
       "      <td>0.992981</td>\n",
       "      <td>0.464723</td>\n",
       "      <td>0.885456</td>\n",
       "      <td>0.19509</td>\n",
       "      <td>0.980785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2023-08-28</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>38.00</td>\n",
       "      <td>89</td>\n",
       "      <td>2023</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.118273</td>\n",
       "      <td>0.992981</td>\n",
       "      <td>0.464723</td>\n",
       "      <td>0.885456</td>\n",
       "      <td>0.19509</td>\n",
       "      <td>0.980785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2023-08-29</td>\n",
       "      <td>5</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>44.33</td>\n",
       "      <td>90</td>\n",
       "      <td>2023</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.118273</td>\n",
       "      <td>0.992981</td>\n",
       "      <td>0.464723</td>\n",
       "      <td>0.885456</td>\n",
       "      <td>0.19509</td>\n",
       "      <td>0.980785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Day of the Week  Temperature (°F)  Special Event  Sales ($)  \\\n",
       "0  2023-06-01                4                80              0      55.33   \n",
       "1  2023-06-02                0                74              0      47.17   \n",
       "2  2023-06-03                2                81              0      50.24   \n",
       "3  2023-06-04                3                90              0      61.28   \n",
       "4  2023-06-05                1                73              0      39.73   \n",
       "..        ...              ...               ...            ...        ...   \n",
       "85 2023-08-25                0                70              0      43.43   \n",
       "86 2023-08-26                2                84              0      59.32   \n",
       "87 2023-08-27                3                78              0      59.93   \n",
       "88 2023-08-28                1                70              0      38.00   \n",
       "89 2023-08-29                5                80              0      44.33   \n",
       "\n",
       "    Day Number  year  month  weekofyear  day  weekofyear_sin  weekofyear_cos  \\\n",
       "0            1  2023      1           1    1        0.118273        0.992981   \n",
       "1            2  2023      1           1    1        0.118273        0.992981   \n",
       "2            3  2023      1           1    1        0.118273        0.992981   \n",
       "3            4  2023      1           1    1        0.118273        0.992981   \n",
       "4            5  2023      1           1    1        0.118273        0.992981   \n",
       "..         ...   ...    ...         ...  ...             ...             ...   \n",
       "85          86  2023      1           1    1        0.118273        0.992981   \n",
       "86          87  2023      1           1    1        0.118273        0.992981   \n",
       "87          88  2023      1           1    1        0.118273        0.992981   \n",
       "88          89  2023      1           1    1        0.118273        0.992981   \n",
       "89          90  2023      1           1    1        0.118273        0.992981   \n",
       "\n",
       "    month_sin  month_cos  day_sin   day_cos  \n",
       "0    0.464723   0.885456  0.19509  0.980785  \n",
       "1    0.464723   0.885456  0.19509  0.980785  \n",
       "2    0.464723   0.885456  0.19509  0.980785  \n",
       "3    0.464723   0.885456  0.19509  0.980785  \n",
       "4    0.464723   0.885456  0.19509  0.980785  \n",
       "..        ...        ...      ...       ...  \n",
       "85   0.464723   0.885456  0.19509  0.980785  \n",
       "86   0.464723   0.885456  0.19509  0.980785  \n",
       "87   0.464723   0.885456  0.19509  0.980785  \n",
       "88   0.464723   0.885456  0.19509  0.980785  \n",
       "89   0.464723   0.885456  0.19509  0.980785  \n",
       "\n",
       "[90 rows x 16 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#label encoding for day of week\n",
    "ls['Day of the Week'] = le.fit_transform(ls['Day of the Week'])\n",
    "# conversion to acceptable format and extracting :day,year,month,day,and so on\n",
    "ls['Date']=pd.to_datetime(ls['Date'])\n",
    "ls['year'] = pd.to_datetime(ls['Date']).dt.year\n",
    "ls['month'] = pd.to_datetime(ls['Day of the Week']).dt.month\n",
    "ls['weekofyear'] = pd.to_datetime(ls['Day of the Week']).dt.weekofyear\n",
    "ls['day'] = pd.to_datetime(ls['Day of the Week']).dt.day\n",
    "# calculating of sine and cosine for time series to capture trend and enhance prediction\n",
    "def encode_date(df, col, max_val):\n",
    "    df[col + '_sin'] = np.sin(2 * np.pi * df[col]/max_val)\n",
    "    df[col + '_cos'] = np.cos(2 * np.pi * df[col]/max_val)\n",
    "    return df\n",
    "\n",
    "encode_date(ls, 'weekofyear', 53)\n",
    "encode_date(ls, 'month', 13)\n",
    "encode_date(ls, 'day', 32)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9eb2528",
   "metadata": {},
   "source": [
    "# every column is in numerial data types ready for forcast modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e80d4851",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
    "version": 3
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