time_series_reg .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "## Time Series Regression Analysis Project.\n",
    "\n",
    "##### Introduction\n",
    "\n",
    "- The project is focused on predicting unit sales for products at various stores of Corporation Favorita, a large Ecuadorian-based grocery retailer. The key objective is to build and refine time series forecasting models that can accurately predict product sales based on historical data and supplementary information. Here's a summary of the project description:\n",
    "\n",
    "##### Project Goals\n",
    "- 1. Forecast Product Sales: Develop machine learning models to predict the unit sales of products at different stores based on historical sales data.\n",
    "- 2. Use Time Series Data: Utilize the time series nature of the data, which includes dates, store information, product types, promotional status, and sales numbers.\n",
    "- 3. Incorporate External Factors: Consider external factors such as oil prices, holidays, and special events that may influence sales.\n",
    "\n",
    "\n",
    "##### Data Files\n",
    "- 1. train.csv: Contains historical sales data including store number, product family, promotional status, and sales figures.\n",
    "- 2. test.csv: Contains future dates for which sales predictions need to be made.\n",
    "- 3. transaction.csv: Records daily transactions for each store.\n",
    "- 4. sample_submission.csv: Provides a sample format for submitting predictions.\n",
    "- 5. stores.csv: Includes metadata about stores, such as location and type.\n",
    "- 6. oil.csv: Contains daily oil prices, relevant for understanding economic conditions affecting sales.\n",
    "- 7. holidays_events.csv: Lists holidays and events with details on their impact on sales, including transferred holidays and bridge days.\n",
    "\n",
    "\n",
    "##### Analytical Tasks\n",
    "- 1. Data Completeness: Check if the training dataset includes all required dates.\n",
    "- 2. Sales Trends: Identify the dates with the lowest and highest sales for each year and compare monthly sales across years.\n",
    "- 3. Impact of Events: Assess the impact of the April 2016 earthquake on sales.\n",
    "- 4. Store Performance: Evaluate sales performance across different stores, clusters, cities, states, and store types.\n",
    "- 5. Influence of Promotions and External Factors: Analyze how promotions, oil prices, and holidays affect sales.\n",
    "- 6. Feature Analysis: Investigate the influence of date-related features on sales.\n",
    "- 7.  Effectiveness of Promotions: Determine which product families and stores are most affected by promotions.\n",
    "- 8. Metric Comparisons: Understand the differences between RMSLE, RMSE, MSE, and MAE.\n",
    "- 9. Wage Payments Impact: Examine if public sector wage payments on specific dates affect sales.\n",
    "\n",
    "##### Key Stakeholders\n",
    " - Internal Stakeholders are directly involved in the project’s execution and have a vested interest in the successful implementation and outcomes of the forecasting models.\n",
    "Project Manager,  Data Scientists/Analysts, Data Engineers, Marketing and Sales Team,  Store Managers, IT Support Team, Financial Analysts\n",
    "\n",
    " - External Stakeholders are affected by the outcomes of the project and may influence or be influenced by the predictions and decisions derived from the models.\n",
    "Customers, Vendors/Suppliers, Data Providers, Government and Regulatory Bodies, Investors/Shareholders, External Consultants\n",
    "\n",
    "\n",
    "##### Analysis Objective\n",
    "\n",
    "- 1.1. Assess Data Quality and Completeness                                                                          \n",
    "- 1.2. Explore and Clean Data                                                                                                     \n",
    "- 1.3. Integrate Supplementary Data                                                                                          \n",
    "- 2.1. Analyze Sales Trends and Patterns                                                                                       \n",
    "- 2.2. Examine Impact of Promotions and External Factors2.4. Assess Data Coverage             \n",
    "- 2.3. Investigate Store and Product Performance                                                                \n",
    "- 2.4. Assess Data Coverage                                                                                                         \n",
    "- 3.1. Develop Predictive Features                                                                                             \n",
    "- 3.2. Build and Evaluate Forecasting Models                                                                              \n",
    "- 3.3. Optimize Model Performance                                                                                            \n",
    "- 4.1. Validate Hypotheses                                                                                                          \n",
    "- 4.2. Investigate Special Events and Anomalies                                                                    \n",
    "- 5.1. Derive Business Insights                                                                                                  \n",
    "- 5.2. Prepare for Deployment and Monitoring\n",
    "\n",
    "##### Success Metrics\n",
    "\n",
    "- The predictive modeling project include achieving an RMSLE of 0.3 or lower to ensure high forecasting accuracy,\n",
    "- Providing actionable insights and recommendations to guide business decisions, and delivering comprehensive documentation and clear reporting. \n",
    "- Additionally, successful deployment, ongoing model maintenance, and positive stakeholder feedback are crucial for overall project success.\n",
    "\n",
    "##### Business Effect \n",
    "\n",
    "1. Optimized Inventory Management: Accurate sales forecasts will enable better inventory planning, reducing stockouts and overstock situations, which enhances operational efficiency and customer satisfaction.\n",
    "\n",
    "2. Improved Sales and Marketing Strategies: Insights from the model will help in crafting targeted promotions and marketing campaigns, potentially increasing sales and profitability.\n",
    "\n",
    "3. Enhanced Decision-Making: Reliable sales predictions will support strategic decision-making, including budgeting and resource allocation, ultimately driving better financial performance and competitive advantage.\n",
    "\n",
    "\n",
    "##### Hypothesis\n",
    "\n",
    "- Null Hypothesis (H₀): The sales of products at Corporation Favorita stores are not significantly affected by promotions, oil prices, or holidays.\n",
    "\n",
    "- Alternate Hypothesis (H₁): The sales of products at Corporation Favorita stores are significantly affected by promotions, oil prices, or holidays.\n",
    "\n",
    "\n",
    "##### Key Questions:\n",
    "\n",
    "- 1. Is the train dataset complete (has all the required dates)?\n",
    "- 2. Which dates have the lowest and highest sales for each year (excluding days the store was closed)?\n",
    "- 3. Compare the sales for each month across the years and determine which month of which year had the highest sales.\n",
    "- 4. Did the earthquake impact sales?\n",
    "- 5. Are certain stores or groups of stores selling more products? (Cluster, city, state, type)\n",
    "- 6. Are sales affected by promotions, oil prices, and holidays?\n",
    "- 7. What analysis can we get from the date and its extractable features?\n",
    "- 8. Which product family and stores did the promotions affect?\n",
    "- 9. Does the payment of wages in the public sector on the 15th and last days of the month influence store sales?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Understanding\n",
    "\n",
    "##### 1. Data Exploration\n",
    "- 1.1. Overview of Datasets\n",
    "\n",
    "- Train Data (train.csv): Contains time series data with features like store_nbr, family, onpromotion, and the target variable sales. It provides historical sales data for training predictive models.\n",
    "- Test Data (test.csv): Similar to the train data but used for making predictions. Includes features but lacks the target variable sales.\n",
    "- Transaction Data (transaction.csv): Records of transactions including date, store number, and transaction amounts. Useful for understanding transaction patterns and correlating with sales data.\n",
    "- Store Metadata (stores.csv): Includes information about stores such as city, state, type, and cluster. Helps in categorizing and grouping stores.\n",
    "- Oil Prices (oil.csv): Daily oil prices for the timeframe covered by both training and test datasets. Oil prices can impact consumer spending and sales.\n",
    "- Holidays and Events (holidays_events.csv): Contains information on holidays and special events, including transferred and bridge days. This data is crucial for understanding the impact of holidays on sales.\n",
    "\n",
    "- 1.2. Key Fields and Their Descriptions\n",
    "\n",
    "- store_nbr: Identifies the store location. Useful for aggregating and comparing sales across different stores.\n",
    "- family: Represents the type of product sold. Helps in analyzing sales trends for different product categories.\n",
    "- onpromotion: Indicates the number of items being promoted. Helps in understanding the effect of promotions on sales.\n",
    "- sales: The target variable representing total sales. Essential for training and evaluating forecasting models.\n",
    "- date: Timestamp for transactions, sales, and other events. Crucial for time series analysis and feature engineering.\n",
    "- -transaction: Transaction data provides additional context for sales data.\n",
    "- 1.3. Data Quality Assessment\n",
    "\n",
    "- Missing Values: Check for and address any missing values in the datasets to ensure completeness.\n",
    "- Outliers: Identify and handle outliers that may skew the analysis or model performance.\n",
    "- Consistency: Ensure consistency across datasets, particularly in date formats, store identifiers, and other key fields.\n",
    "\n",
    "\n",
    "- 1.4. Data Integration and Feature Engineering\n",
    "\n",
    "- Integration: Merge datasets such as train.csv, stores.csv, oil.csv, and holidays_events.csv to create a comprehensive dataset for modeling.\n",
    "- Feature Engineering: Create new features such as day of the week, month, oil price lags, and holiday effects to enhance the predictive power of the models.\n",
    "1.5. Initial Insights and Observations\n",
    "\n",
    "- Sales Trends: Analyze historical sales trends and seasonality.\n",
    "- Promotional Impact: Examine how promotions affect sales across different product families and stores.\n",
    "- External Factors: Assess the impact of oil prices and holidays on sales patterns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Import the necessary Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1094,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Manipulation packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import polars as pl\n",
    "\n",
    "\n",
    "\n",
    "# Database connectivity\n",
    "import pyodbc\n",
    "from sqlalchemy import create_engine\n",
    "\n",
    "# Data Visualization:\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "import hvplot.polars\n",
    "import polars\n",
    "import altair as alt\n",
    "import plotly.express as px\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "\n",
    "\n",
    "# Managing environment variables\n",
    "from dotenv import dotenv_values\n",
    "from itertools import product\n",
    "\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.stattools import adfuller\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Import Data Manupilation Packages\n",
    "from dotenv import dotenv_values\n",
    "import pyodbc\n",
    "import pandas as pd\n",
    "import polars as pl\n",
    "import numpy as np\n",
    "from pandas.tseries.offsets import DateOffset\n",
    "\n",
    "# Import Visualization packages\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import hvplot.polars # noqa\n",
    "\n",
    "# Import stats packages\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from scipy.stats import ttest_ind\n",
    "from scipy.stats import mannwhitneyu\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "# Modelling\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import RobustScaler, OneHotEncoder, FunctionTransformer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "\n",
    "from prophet import Prophet\n",
    "from sklearn.metrics import mean_squared_log_error, mean_absolute_error, root_mean_squared_log_error\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import joblib\n",
    "\n",
    "\n",
    "# Utility Packages:\n",
    "from colabcode import ColabCode\n",
    "\n",
    "from itertools import product\n",
    "import os\n",
    "pd.options.display.float_format = '{:.2f}'.format\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Getting Datasets from Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1095,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load environment variable from .env file into a directory\n",
    "environment_variables = dotenv_values('.env')\n",
    "\n",
    "# Get the values for the credentials you set in the '.env' file\n",
    "server = environment_variables.get(\"SERVER\")\n",
    "database = environment_variables.get(\"DATABASE\")\n",
    "username = environment_variables.get(\"UID\")\n",
    "password = environment_variables.get(\"PWD\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1096,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a connection string\n",
    "connection_string = f\"DRIVER={{SQL Server}};SERVER={server};DATABASE={database};UID={username};PWD={password};MARS_Connection=yes;MinProtocolVersion=TLSv1.2;\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1097,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This will connect to the server and might take a few seconds to be complete. \n",
    "connection = pyodbc.connect(connection_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1098,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pc\\AppData\\Local\\Temp\\ipykernel_23676\\2250160719.py:3: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  oil_data1 = pd.read_sql(query1, connection)\n",
      "C:\\Users\\pc\\AppData\\Local\\Temp\\ipykernel_23676\\2250160719.py:8: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  holiday_data2 =  pd.read_sql(query2, connection)\n",
      "C:\\Users\\pc\\AppData\\Local\\Temp\\ipykernel_23676\\2250160719.py:12: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  store_data3 =  pd.read_sql(query3, connection)\n"
     ]
    }
   ],
   "source": [
    "# sql query to get oil data\n",
    "query1 = \"SELECT * FROM dbo.oil\"\n",
    "oil_data1 = pd.read_sql(query1, connection)\n",
    "\n",
    "\n",
    "# sql query to get holidays_events\n",
    "query2 = \"SELECT * FROM dbo.holidays_events\"\n",
    "holiday_data2 =  pd.read_sql(query2, connection)\n",
    "\n",
    "#sql query to get stores\n",
    "query3 = \"SELECT * FROM dbo.stores\"\n",
    "store_data3 =  pd.read_sql(query3, connection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1099,
   "metadata": {},
   "outputs": [
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       "      <td>2017-08-28</td>\n",
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       "<p>1218 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "            date  dcoilwtico\n",
       "0     2013-01-01         NaN\n",
       "1     2013-01-02   93.139999\n",
       "2     2013-01-03   92.970001\n",
       "3     2013-01-04   93.120003\n",
       "4     2013-01-07   93.199997\n",
       "...          ...         ...\n",
       "1213  2017-08-25   47.650002\n",
       "1214  2017-08-28   46.400002\n",
       "1215  2017-08-29   46.459999\n",
       "1216  2017-08-30   45.959999\n",
       "1217  2017-08-31   47.259998\n",
       "\n",
       "[1218 rows x 2 columns]"
      ]
     },
     "execution_count": 1099,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oil_data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1218 entries, 0 to 1217\n",
      "Data columns (total 2 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   date        1218 non-null   object \n",
      " 1   dcoilwtico  1175 non-null   float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 19.2+ KB\n"
     ]
    }
   ],
   "source": [
    "\n",
    "oil_data1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date           0\n",
       "dcoilwtico    43\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "oil_data1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1102,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "        date  dcoilwtico\n",
       "0 2013-01-01         NaN\n",
       "1 2013-01-02   93.139999\n",
       "2 2013-01-03   92.970001\n",
       "3 2013-01-04   93.120003\n",
       "4 2013-01-07   93.199997"
      ]
     },
     "execution_count": 1102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert 'date' column to datetime\n",
    "\n",
    "oil_data1['date'] = pd.to_datetime(oil_data1['date'])\n",
    "\n",
    "oil_data1.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1103,
   "metadata": {},
   "outputs": [
    {
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       "      <th>date</th>\n",
       "      <th>type</th>\n",
       "      <th>locale</th>\n",
       "      <th>locale_name</th>\n",
       "      <th>description</th>\n",
       "      <th>transferred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-03-02</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>Local</td>\n",
       "      <td>Manta</td>\n",
       "      <td>Fundacion de Manta</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-04-01</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>Regional</td>\n",
       "      <td>Cotopaxi</td>\n",
       "      <td>Provincializacion de Cotopaxi</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-04-12</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>Local</td>\n",
       "      <td>Cuenca</td>\n",
       "      <td>Fundacion de Cuenca</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-14</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>Local</td>\n",
       "      <td>Libertad</td>\n",
       "      <td>Cantonizacion de Libertad</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-04-21</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>Local</td>\n",
       "      <td>Riobamba</td>\n",
       "      <td>Cantonizacion de Riobamba</td>\n",
       "      <td>False</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>345</th>\n",
       "      <td>2017-12-22</td>\n",
       "      <td>Additional</td>\n",
       "      <td>National</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Navidad-3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>2017-12-23</td>\n",
       "      <td>Additional</td>\n",
       "      <td>National</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Navidad-2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>2017-12-24</td>\n",
       "      <td>Additional</td>\n",
       "      <td>National</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Navidad-1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>2017-12-25</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>National</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Navidad</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>2017-12-26</td>\n",
       "      <td>Additional</td>\n",
       "      <td>National</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Navidad+1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>350 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date        type    locale locale_name  \\\n",
       "0    2012-03-02     Holiday     Local       Manta   \n",
       "1    2012-04-01     Holiday  Regional    Cotopaxi   \n",
       "2    2012-04-12     Holiday     Local      Cuenca   \n",
       "3    2012-04-14     Holiday     Local    Libertad   \n",
       "4    2012-04-21     Holiday     Local    Riobamba   \n",
       "..          ...         ...       ...         ...   \n",
       "345  2017-12-22  Additional  National     Ecuador   \n",
       "346  2017-12-23  Additional  National     Ecuador   \n",
       "347  2017-12-24  Additional  National     Ecuador   \n",
       "348  2017-12-25     Holiday  National     Ecuador   \n",
       "349  2017-12-26  Additional  National     Ecuador   \n",
       "\n",
       "                       description  transferred  \n",
       "0               Fundacion de Manta        False  \n",
       "1    Provincializacion de Cotopaxi        False  \n",
       "2              Fundacion de Cuenca        False  \n",
       "3        Cantonizacion de Libertad        False  \n",
       "4        Cantonizacion de Riobamba        False  \n",
       "..                             ...          ...  \n",
       "345                      Navidad-3        False  \n",
       "346                      Navidad-2        False  \n",
       "347                      Navidad-1        False  \n",
       "348                        Navidad        False  \n",
       "349                      Navidad+1        False  \n",
       "\n",
       "[350 rows x 6 columns]"
      ]
     },
     "execution_count": 1103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holiday_data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 350 entries, 0 to 349\n",
      "Data columns (total 6 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   date         350 non-null    object\n",
      " 1   type         350 non-null    object\n",
      " 2   locale       350 non-null    object\n",
      " 3   locale_name  350 non-null    object\n",
      " 4   description  350 non-null    object\n",
      " 5   transferred  350 non-null    bool  \n",
      "dtypes: bool(1), object(5)\n",
      "memory usage: 14.1+ KB\n"
     ]
    }
   ],
   "source": [
    "holiday_data2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date           0\n",
       "type           0\n",
       "locale         0\n",
       "locale_name    0\n",
       "description    0\n",
       "transferred    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holiday_data2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>type</th>\n",
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       "      <th>locale_name</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-03-02</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>Local</td>\n",
       "      <td>Manta</td>\n",
       "      <td>Fundacion de Manta</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-04-01</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>Regional</td>\n",
       "      <td>Cotopaxi</td>\n",
       "      <td>Provincializacion de Cotopaxi</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-04-12</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>Local</td>\n",
       "      <td>Cuenca</td>\n",
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       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-14</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>Local</td>\n",
       "      <td>Libertad</td>\n",
       "      <td>Cantonizacion de Libertad</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-04-21</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>Local</td>\n",
       "      <td>Riobamba</td>\n",
       "      <td>Cantonizacion de Riobamba</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date     type    locale locale_name                    description  \\\n",
       "0 2012-03-02  Holiday     Local       Manta             Fundacion de Manta   \n",
       "1 2012-04-01  Holiday  Regional    Cotopaxi  Provincializacion de Cotopaxi   \n",
       "2 2012-04-12  Holiday     Local      Cuenca            Fundacion de Cuenca   \n",
       "3 2012-04-14  Holiday     Local    Libertad      Cantonizacion de Libertad   \n",
       "4 2012-04-21  Holiday     Local    Riobamba      Cantonizacion de Riobamba   \n",
       "\n",
       "   transferred  \n",
       "0        False  \n",
       "1        False  \n",
       "2        False  \n",
       "3        False  \n",
       "4        False  "
      ]
     },
     "execution_count": 1106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Convert 'date' column to datetime\n",
    "\n",
    "holiday_data2['date'] = pd.to_datetime(holiday_data2['date'])\n",
    "\n",
    "holiday_data2.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1107,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>store_nbr</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
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       "      <td>D</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
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       "      <td>D</td>\n",
       "      <td>13</td>\n",
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       "      <th>6</th>\n",
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       "      <td>D</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Quito</td>\n",
       "      <td>Pichincha</td>\n",
       "      <td>D</td>\n",
       "      <td>8</td>\n",
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       "      <th>8</th>\n",
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       "      <td>Quito</td>\n",
       "      <td>Pichincha</td>\n",
       "      <td>B</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Quito</td>\n",
       "      <td>Pichincha</td>\n",
       "      <td>C</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Cayambe</td>\n",
       "      <td>Pichincha</td>\n",
       "      <td>B</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Latacunga</td>\n",
       "      <td>Cotopaxi</td>\n",
       "      <td>C</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Latacunga</td>\n",
       "      <td>Cotopaxi</td>\n",
       "      <td>C</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Riobamba</td>\n",
       "      <td>Chimborazo</td>\n",
       "      <td>C</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Ibarra</td>\n",
       "      <td>Imbabura</td>\n",
       "      <td>C</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Santo Domingo</td>\n",
       "      <td>Santo Domingo de los Tsachilas</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Quito</td>\n",
       "      <td>Pichincha</td>\n",
       "      <td>C</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>Quito</td>\n",
       "      <td>Pichincha</td>\n",
       "      <td>B</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Guaranda</td>\n",
       "      <td>Bolivar</td>\n",
       "      <td>C</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Quito</td>\n",
       "      <td>Pichincha</td>\n",
       "      <td>B</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>Santo Domingo</td>\n",
       "      <td>Santo Domingo de los Tsachilas</td>\n",
       "      <td>B</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>Puyo</td>\n",
       "      <td>Pastaza</td>\n",
       "      <td>C</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>Ambato</td>\n",
       "      <td>Tungurahua</td>\n",
       "      <td>D</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>Guayaquil</td>\n",
       "      <td>Guayas</td>\n",
       "      <td>D</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>Salinas</td>\n",
       "      <td>Santa Elena</td>\n",
       "      <td>D</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>Guayaquil</td>\n",
       "      <td>Guayas</td>\n",
       "      <td>D</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>Daule</td>\n",
       "      <td>Guayas</td>\n",
       "      <td>D</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>Guayaquil</td>\n",
       "      <td>Guayas</td>\n",
       "      <td>E</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>Guayaquil</td>\n",
       "      <td>Guayas</td>\n",
       "      <td>E</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>Guayaquil</td>\n",
       "      <td>Guayas</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>Babahoyo</td>\n",
       "      <td>Los Rios</td>\n",
       "      <td>B</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>Guayaquil</td>\n",
       "      <td>Guayas</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>Quevedo</td>\n",
       "      <td>Los Rios</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>Guayaquil</td>\n",
       "      <td>Guayas</td>\n",
       "      <td>B</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>Playas</td>\n",
       "      <td>Guayas</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>Libertad</td>\n",
       "      <td>Guayas</td>\n",
       "      <td>E</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>Cuenca</td>\n",
       "      <td>Azuay</td>\n",
       "      <td>D</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>Loja</td>\n",
       "      <td>Loja</td>\n",
       "      <td>D</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>Cuenca</td>\n",
       "      <td>Azuay</td>\n",
       "      <td>B</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>Machala</td>\n",
       "      <td>El Oro</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>Machala</td>\n",
       "      <td>El Oro</td>\n",
       "      <td>D</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>Cuenca</td>\n",
       "      <td>Azuay</td>\n",
       "      <td>D</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>Esmeraldas</td>\n",
       "      <td>Esmeraldas</td>\n",
       "      <td>E</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>Quito</td>\n",
       "      <td>Pichincha</td>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>Quito</td>\n",
       "      <td>Pichincha</td>\n",
       "      <td>A</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>Quito</td>\n",
       "      <td>Pichincha</td>\n",
       "      <td>A</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>Quito</td>\n",
       "      <td>Pichincha</td>\n",
       "      <td>A</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>Quito</td>\n",
       "      <td>Pichincha</td>\n",
       "      <td>A</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>Quito</td>\n",
       "      <td>Pichincha</td>\n",
       "      <td>A</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>Ambato</td>\n",
       "      <td>Tungurahua</td>\n",
       "      <td>A</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>Guayaquil</td>\n",
       "      <td>Guayas</td>\n",
       "      <td>A</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52</td>\n",
       "      <td>Manta</td>\n",
       "      <td>Manabi</td>\n",
       "      <td>A</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53</td>\n",
       "      <td>Manta</td>\n",
       "      <td>Manabi</td>\n",
       "      <td>D</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54</td>\n",
       "      <td>El Carmen</td>\n",
       "      <td>Manabi</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    store_nbr           city                           state type  cluster\n",
       "0           1          Quito                       Pichincha    D       13\n",
       "1           2          Quito                       Pichincha    D       13\n",
       "2           3          Quito                       Pichincha    D        8\n",
       "3           4          Quito                       Pichincha    D        9\n",
       "4           5  Santo Domingo  Santo Domingo de los Tsachilas    D        4\n",
       "5           6          Quito                       Pichincha    D       13\n",
       "6           7          Quito                       Pichincha    D        8\n",
       "7           8          Quito                       Pichincha    D        8\n",
       "8           9          Quito                       Pichincha    B        6\n",
       "9          10          Quito                       Pichincha    C       15\n",
       "10         11        Cayambe                       Pichincha    B        6\n",
       "11         12      Latacunga                        Cotopaxi    C       15\n",
       "12         13      Latacunga                        Cotopaxi    C       15\n",
       "13         14       Riobamba                      Chimborazo    C        7\n",
       "14         15         Ibarra                        Imbabura    C       15\n",
       "15         16  Santo Domingo  Santo Domingo de los Tsachilas    C        3\n",
       "16         17          Quito                       Pichincha    C       12\n",
       "17         18          Quito                       Pichincha    B       16\n",
       "18         19       Guaranda                         Bolivar    C       15\n",
       "19         20          Quito                       Pichincha    B        6\n",
       "20         21  Santo Domingo  Santo Domingo de los Tsachilas    B        6\n",
       "21         22           Puyo                         Pastaza    C        7\n",
       "22         23         Ambato                      Tungurahua    D        9\n",
       "23         24      Guayaquil                          Guayas    D        1\n",
       "24         25        Salinas                     Santa Elena    D        1\n",
       "25         26      Guayaquil                          Guayas    D       10\n",
       "26         27          Daule                          Guayas    D        1\n",
       "27         28      Guayaquil                          Guayas    E       10\n",
       "28         29      Guayaquil                          Guayas    E       10\n",
       "29         30      Guayaquil                          Guayas    C        3\n",
       "30         31       Babahoyo                        Los Rios    B       10\n",
       "31         32      Guayaquil                          Guayas    C        3\n",
       "32         33        Quevedo                        Los Rios    C        3\n",
       "33         34      Guayaquil                          Guayas    B        6\n",
       "34         35         Playas                          Guayas    C        3\n",
       "35         36       Libertad                          Guayas    E       10\n",
       "36         37         Cuenca                           Azuay    D        2\n",
       "37         38           Loja                            Loja    D        4\n",
       "38         39         Cuenca                           Azuay    B        6\n",
       "39         40        Machala                          El Oro    C        3\n",
       "40         41        Machala                          El Oro    D        4\n",
       "41         42         Cuenca                           Azuay    D        2\n",
       "42         43     Esmeraldas                      Esmeraldas    E       10\n",
       "43         44          Quito                       Pichincha    A        5\n",
       "44         45          Quito                       Pichincha    A       11\n",
       "45         46          Quito                       Pichincha    A       14\n",
       "46         47          Quito                       Pichincha    A       14\n",
       "47         48          Quito                       Pichincha    A       14\n",
       "48         49          Quito                       Pichincha    A       11\n",
       "49         50         Ambato                      Tungurahua    A       14\n",
       "50         51      Guayaquil                          Guayas    A       17\n",
       "51         52          Manta                          Manabi    A       11\n",
       "52         53          Manta                          Manabi    D       13\n",
       "53         54      El Carmen                          Manabi    C        3"
      ]
     },
     "execution_count": 1107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 54 entries, 0 to 53\n",
      "Data columns (total 5 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   store_nbr  54 non-null     int64 \n",
      " 1   city       54 non-null     object\n",
      " 2   state      54 non-null     object\n",
      " 3   type       54 non-null     object\n",
      " 4   cluster    54 non-null     int64 \n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 2.2+ KB\n"
     ]
    }
   ],
   "source": [
    "store_data3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "store_nbr    0\n",
       "city         0\n",
       "state        0\n",
       "type         0\n",
       "cluster      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_data3.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accessing Data from One drive\n",
    "\n",
    "sample_data4 = pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1111,
   "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>id</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3000888</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000889</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3000890</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3000891</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3000892</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28507</th>\n",
       "      <td>3029395</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28508</th>\n",
       "      <td>3029396</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28509</th>\n",
       "      <td>3029397</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28510</th>\n",
       "      <td>3029398</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28511</th>\n",
       "      <td>3029399</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28512 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  sales\n",
       "0      3000888    0.0\n",
       "1      3000889    0.0\n",
       "2      3000890    0.0\n",
       "3      3000891    0.0\n",
       "4      3000892    0.0\n",
       "...        ...    ...\n",
       "28507  3029395    0.0\n",
       "28508  3029396    0.0\n",
       "28509  3029397    0.0\n",
       "28510  3029398    0.0\n",
       "28511  3029399    0.0\n",
       "\n",
       "[28512 rows x 2 columns]"
      ]
     },
     "execution_count": 1111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accessing sapmple data in Onedrive\n",
    "\n",
    "sample_data4 = pd.read_csv('../data/sample_submission.csv')\n",
    "sample_data4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id       0\n",
       "sales    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data4.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accessing Data from One drive\n",
    "test_data5 = pd.read_csv('../data/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 28512 entries, 0 to 28511\n",
      "Data columns (total 5 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   id           28512 non-null  int64 \n",
      " 1   date         28512 non-null  object\n",
      " 2   store_nbr    28512 non-null  int64 \n",
      " 3   family       28512 non-null  object\n",
      " 4   onpromotion  28512 non-null  int64 \n",
      "dtypes: int64(3), object(2)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "test_data5.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1115,
   "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>id</th>\n",
       "      <th>date</th>\n",
       "      <th>store_nbr</th>\n",
       "      <th>family</th>\n",
       "      <th>onpromotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>28507</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28508</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28509</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28510</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28511</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28512 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id   date  store_nbr  family  onpromotion\n",
       "0      False  False      False   False        False\n",
       "1      False  False      False   False        False\n",
       "2      False  False      False   False        False\n",
       "3      False  False      False   False        False\n",
       "4      False  False      False   False        False\n",
       "...      ...    ...        ...     ...          ...\n",
       "28507  False  False      False   False        False\n",
       "28508  False  False      False   False        False\n",
       "28509  False  False      False   False        False\n",
       "28510  False  False      False   False        False\n",
       "28511  False  False      False   False        False\n",
       "\n",
       "[28512 rows x 5 columns]"
      ]
     },
     "execution_count": 1115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data5.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Getting the rest of the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1116,
   "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>id</th>\n",
       "      <th>date</th>\n",
       "      <th>store_nbr</th>\n",
       "      <th>family</th>\n",
       "      <th>sales</th>\n",
       "      <th>onpromotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>AUTOMOTIVE</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>BABY CARE</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>BEAUTY</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>BEVERAGES</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>BOOKS</td>\n",
       "      <td>0.000</td>\n",
       "      <td>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>3000883</th>\n",
       "      <td>3000883</td>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>9</td>\n",
       "      <td>POULTRY</td>\n",
       "      <td>438.133</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000884</th>\n",
       "      <td>3000884</td>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>9</td>\n",
       "      <td>PREPARED FOODS</td>\n",
       "      <td>154.553</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000885</th>\n",
       "      <td>3000885</td>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>9</td>\n",
       "      <td>PRODUCE</td>\n",
       "      <td>2419.729</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000886</th>\n",
       "      <td>3000886</td>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>9</td>\n",
       "      <td>SCHOOL AND OFFICE SUPPLIES</td>\n",
       "      <td>121.000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000887</th>\n",
       "      <td>3000887</td>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>9</td>\n",
       "      <td>SEAFOOD</td>\n",
       "      <td>16.000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000888 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id        date  store_nbr                      family     sales  \\\n",
       "0              0  2013-01-01          1                  AUTOMOTIVE     0.000   \n",
       "1              1  2013-01-01          1                   BABY CARE     0.000   \n",
       "2              2  2013-01-01          1                      BEAUTY     0.000   \n",
       "3              3  2013-01-01          1                   BEVERAGES     0.000   \n",
       "4              4  2013-01-01          1                       BOOKS     0.000   \n",
       "...          ...         ...        ...                         ...       ...   \n",
       "3000883  3000883  2017-08-15          9                     POULTRY   438.133   \n",
       "3000884  3000884  2017-08-15          9              PREPARED FOODS   154.553   \n",
       "3000885  3000885  2017-08-15          9                     PRODUCE  2419.729   \n",
       "3000886  3000886  2017-08-15          9  SCHOOL AND OFFICE SUPPLIES   121.000   \n",
       "3000887  3000887  2017-08-15          9                     SEAFOOD    16.000   \n",
       "\n",
       "         onpromotion  \n",
       "0                  0  \n",
       "1                  0  \n",
       "2                  0  \n",
       "3                  0  \n",
       "4                  0  \n",
       "...              ...  \n",
       "3000883            0  \n",
       "3000884            1  \n",
       "3000885          148  \n",
       "3000886            8  \n",
       "3000887            0  \n",
       "\n",
       "[3000888 rows x 6 columns]"
      ]
     },
     "execution_count": 1116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the train dataset\n",
    "\n",
    "\n",
    "train_data6 = pd.read_csv('../data/train.csv')\n",
    "train_data6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3000888, 6)"
      ]
     },
     "execution_count": 1117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data6.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3000888 entries, 0 to 3000887\n",
      "Data columns (total 6 columns):\n",
      " #   Column       Dtype  \n",
      "---  ------       -----  \n",
      " 0   id           int64  \n",
      " 1   date         object \n",
      " 2   store_nbr    int64  \n",
      " 3   family       object \n",
      " 4   sales        float64\n",
      " 5   onpromotion  int64  \n",
      "dtypes: float64(1), int64(3), object(2)\n",
      "memory usage: 137.4+ MB\n"
     ]
    }
   ],
   "source": [
    "train_data6.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id             0\n",
       "date           0\n",
       "store_nbr      0\n",
       "family         0\n",
       "sales          0\n",
       "onpromotion    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data6.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>store_nbr</th>\n",
       "      <th>family</th>\n",
       "      <th>sales</th>\n",
       "      <th>onpromotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>AUTOMOTIVE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>BABY CARE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>BEAUTY</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>BEVERAGES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>BOOKS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id       date  store_nbr      family  sales  onpromotion\n",
       "0   0 2013-01-01          1  AUTOMOTIVE    0.0            0\n",
       "1   1 2013-01-01          1   BABY CARE    0.0            0\n",
       "2   2 2013-01-01          1      BEAUTY    0.0            0\n",
       "3   3 2013-01-01          1   BEVERAGES    0.0            0\n",
       "4   4 2013-01-01          1       BOOKS    0.0            0"
      ]
     },
     "execution_count": 1120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert 'date' column to datetime\n",
    "\n",
    "train_data6['date'] = pd.to_datetime(train_data6['date'])\n",
    "\n",
    "train_data6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>store_nbr</th>\n",
       "      <th>transactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>25</td>\n",
       "      <td>770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>2111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>2</td>\n",
       "      <td>2358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>3</td>\n",
       "      <td>3487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>4</td>\n",
       "      <td>1922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83483</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>50</td>\n",
       "      <td>2804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83484</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>51</td>\n",
       "      <td>1573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83485</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>52</td>\n",
       "      <td>2255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83486</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>53</td>\n",
       "      <td>932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83487</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>54</td>\n",
       "      <td>802</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83488 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date  store_nbr  transactions\n",
       "0      2013-01-01         25           770\n",
       "1      2013-01-02          1          2111\n",
       "2      2013-01-02          2          2358\n",
       "3      2013-01-02          3          3487\n",
       "4      2013-01-02          4          1922\n",
       "...           ...        ...           ...\n",
       "83483  2017-08-15         50          2804\n",
       "83484  2017-08-15         51          1573\n",
       "83485  2017-08-15         52          2255\n",
       "83486  2017-08-15         53           932\n",
       "83487  2017-08-15         54           802\n",
       "\n",
       "[83488 rows x 3 columns]"
      ]
     },
     "execution_count": 1121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the transaction dataset\n",
    "\n",
    "transaction_data7 = pd.read_csv('../data/transactions.csv')\n",
    "transaction_data7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>store_nbr</th>\n",
       "      <th>transactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>25</td>\n",
       "      <td>770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>2111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>2</td>\n",
       "      <td>2358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>3</td>\n",
       "      <td>3487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>4</td>\n",
       "      <td>1922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  store_nbr  transactions\n",
       "0 2013-01-01         25           770\n",
       "1 2013-01-02          1          2111\n",
       "2 2013-01-02          2          2358\n",
       "3 2013-01-02          3          3487\n",
       "4 2013-01-02          4          1922"
      ]
     },
     "execution_count": 1122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert 'date' column to datetime\n",
    "\n",
    "transaction_data7['date'] = pd.to_datetime(transaction_data7['date'])\n",
    "\n",
    "transaction_data7.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 83488 entries, 0 to 83487\n",
      "Data columns (total 3 columns):\n",
      " #   Column        Non-Null Count  Dtype         \n",
      "---  ------        --------------  -----         \n",
      " 0   date          83488 non-null  datetime64[ns]\n",
      " 1   store_nbr     83488 non-null  int64         \n",
      " 2   transactions  83488 non-null  int64         \n",
      "dtypes: datetime64[ns](1), int64(2)\n",
      "memory usage: 1.9 MB\n"
     ]
    }
   ],
   "source": [
    "transaction_data7.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date            0\n",
       "store_nbr       0\n",
       "transactions    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction_data7.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### EDA on the train Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
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       "      <th>75%</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>3000888.0</td>\n",
       "      <td>1500443.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>750221.75</td>\n",
       "      <td>1500443.5</td>\n",
       "      <td>2250665.25</td>\n",
       "      <td>3000887.0</td>\n",
       "      <td>866281.891642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <td>3000888</td>\n",
       "      <td>2015-04-24 08:27:04.703088384</td>\n",
       "      <td>2013-01-01 00:00:00</td>\n",
       "      <td>2014-02-26 18:00:00</td>\n",
       "      <td>2015-04-24 12:00:00</td>\n",
       "      <td>2016-06-19 06:00:00</td>\n",
       "      <td>2017-08-15 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>store_nbr</th>\n",
       "      <td>3000888.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>41.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>15.585787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <td>3000888.0</td>\n",
       "      <td>357.775749</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>195.84725</td>\n",
       "      <td>124717.0</td>\n",
       "      <td>1101.997721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>onpromotion</th>\n",
       "      <td>3000888.0</td>\n",
       "      <td>2.60277</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>12.218882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count                           mean                  min  \\\n",
       "id           3000888.0                      1500443.5                  0.0   \n",
       "date           3000888  2015-04-24 08:27:04.703088384  2013-01-01 00:00:00   \n",
       "store_nbr    3000888.0                           27.5                  1.0   \n",
       "sales        3000888.0                     357.775749                  0.0   \n",
       "onpromotion  3000888.0                        2.60277                  0.0   \n",
       "\n",
       "                             25%                  50%                  75%  \\\n",
       "id                     750221.75            1500443.5           2250665.25   \n",
       "date         2014-02-26 18:00:00  2015-04-24 12:00:00  2016-06-19 06:00:00   \n",
       "store_nbr                   14.0                 27.5                 41.0   \n",
       "sales                        0.0                 11.0            195.84725   \n",
       "onpromotion                  0.0                  0.0                  0.0   \n",
       "\n",
       "                             max            std  \n",
       "id                     3000887.0  866281.891642  \n",
       "date         2017-08-15 00:00:00            NaN  \n",
       "store_nbr                   54.0      15.585787  \n",
       "sales                   124717.0    1101.997721  \n",
       "onpromotion                741.0      12.218882  "
      ]
     },
     "execution_count": 1125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the descriptive statistics of the validation data set\n",
    "\n",
    "train_data6.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id             3000888\n",
       "date              1684\n",
       "store_nbr           54\n",
       "family              33\n",
       "sales           379610\n",
       "onpromotion        362\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for the unique values of the validation\n",
    "\n",
    "train_data6.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 1127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data6.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- checking the data completeness and Mising dates in the Data column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1128,
   "metadata": {},
   "outputs": [],
   "source": [
    "def start_end_date(df):\n",
    "    # Start of the data using the date\n",
    "    start_date = df['date'].min()\n",
    " \n",
    "    # End of the data using the date\n",
    "    end_date = df['date'].max()\n",
    " \n",
    "    # Print the start and end date of the data\n",
    "    print(f'Start date is {start_date}')\n",
    "    print(f'End date is {end_date}')\n",
    "   \n",
    "    return start_date, end_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start date is 2013-01-01 00:00:00\n",
      "End date is 2017-08-31 00:00:00\n"
     ]
    }
   ],
   "source": [
    "start_date, end_date = start_end_date(oil_data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start date is 2013-01-01 00:00:00\n",
      "End date is 2017-08-15 00:00:00\n"
     ]
    }
   ],
   "source": [
    "start_date, end_date = start_end_date(train_data6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1131,
   "metadata": {},
   "outputs": [],
   "source": [
    "def missing_dates(df):\n",
    "    # Assuming 'date' is the column with dates\n",
    "    existing_dates = df['date']\n",
    "\n",
    "    # Create a complete date range from min to max date in your data\n",
    "    date_range = pd.date_range(start=min(existing_dates), end=max(existing_dates))\n",
    "\n",
    "    # Find the dates that are in 'date_range' but not in 'existing_dates'\n",
    "    missing_dates = date_range[~date_range.isin(existing_dates)]\n",
    "\n",
    "    return missing_dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The Missing Date functions used to find  & retrieve the missing dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2013-12-25', '2014-12-25', '2015-12-25', '2016-12-25'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 1132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check missing dates\n",
    "all_missing_dates = missing_dates(train_data6)\n",
    "all_missing_dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping the Id column since it will not be relevant for the visualizations\n",
    "\n",
    "train_data6 = train_data6.drop(columns= ['id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3000888, 5)"
      ]
     },
     "execution_count": 1134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data6.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['store_nbr', 'family', 'sales', 'onpromotion']"
      ]
     },
     "execution_count": 1135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the columns to fill\n",
    "# Create a list of all column names in the DataFrame 'train_data6', excluding the 'date' column\n",
    "\n",
    "columns = [column for column in train_data6.columns if column != 'date']\n",
    "columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1782"
      ]
     },
     "execution_count": 1136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine the number of entries for the earliest date in the 'train_dataset'\n",
    "# The code filters the DataFrame to select rows where the 'date' column matches the earliest date (minimum date),\n",
    "\n",
    "earliest_date = train_data6['date'].min()\n",
    "no_entries_per_day = train_data6[train_data6['date'] == earliest_date].shape[0]\n",
    "no_entries_per_day\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7128"
      ]
     },
     "execution_count": 1137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the total number of entries by multiplying the number of entries per day (for the earliest date)\n",
    "# by the length of the 'all_missing_dates' list. This assumes that the number of entries per day is consistent\n",
    "# across all missing dates.\n",
    "\n",
    "no_entries_per_day * len(all_missing_dates)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Creating missing dataframe using all missing dates, unique store_nbr and unique family category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in train_data6: Index(['date', 'store_nbr', 'family', 'sales', 'onpromotion'], dtype='object')\n",
      "(7128, 3)\n",
      "        date  store_nbr      family\n",
      "0 2013-12-25          1  AUTOMOTIVE\n",
      "1 2013-12-25          1   BABY CARE\n",
      "2 2013-12-25          1      BEAUTY\n",
      "3 2013-12-25          1   BEVERAGES\n",
      "4 2013-12-25          1       BOOKS\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def missing_dates(df, date_col):\n",
    "    # Ensure the date column is of datetime type\n",
    "    if date_col not in df.columns:\n",
    "        raise KeyError(f\"The column '{date_col}' does not exist in the DataFrame.\")\n",
    "    \n",
    "    df[date_col] = pd.to_datetime(df[date_col])\n",
    "    \n",
    "    # Drop duplicate dates\n",
    "    df = df.drop_duplicates(subset=[date_col])\n",
    "    \n",
    "    # Set the date column as the index\n",
    "    df.set_index(date_col, inplace=True)\n",
    "    \n",
    "    # Generate a complete date range from the start_date to the end_date\n",
    "    start_date, end_date = df.index.min(), df.index.max()\n",
    "    all_dates = pd.date_range(start=start_date, end=end_date)\n",
    "    \n",
    "    # Reindex the DataFrame to include all dates\n",
    "    df_reindexed = df.reindex(all_dates)\n",
    "    \n",
    "    # Find missing dates\n",
    "    missing_dates = df_reindexed[df_reindexed.isnull().any(axis=1)].index\n",
    "    \n",
    "    return pd.DataFrame(missing_dates, columns=[date_col])\n",
    "\n",
    "# Assuming train_data6 is already defined and contains the 'date' column\n",
    "try:\n",
    "    print(\"Columns in train_data6:\", train_data6.columns)\n",
    "    missing_dates_df = missing_dates(train_data6, 'date')\n",
    "    all_missing_dates = missing_dates_df['date'].tolist()\n",
    "    \n",
    "    # Create df with unique values for store_nbr\n",
    "    store_nbr_unique = train_data6['store_nbr'].unique()\n",
    "\n",
    "    # Create df with unique values for family category\n",
    "    family_unique = train_data6['family'].unique()\n",
    "\n",
    "    # Create dataframe with missing dates and unique store_nbr and family category\n",
    "    missing_df = pd.DataFrame(\n",
    "        list(product(all_missing_dates, store_nbr_unique, family_unique)), \n",
    "        columns=['date', 'store_nbr', 'family']\n",
    "    )\n",
    "\n",
    "    # Display the shape of the missing_df DataFrame\n",
    "    print(missing_df.shape)\n",
    "\n",
    "    # Optionally display the first few rows to verify\n",
    "    print(missing_df.head())\n",
    "except KeyError as e:\n",
    "    print(f\"Error: {e}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7128, 3)"
      ]
     },
     "execution_count": 1139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Create df with unique values for store_nbr\n",
    "store_nbr_unique = train_data6['store_nbr'].unique()\n",
    "\n",
    "# Create df with unique values for family category\n",
    "family_unique = train_data6['family'].unique()\n",
    "\n",
    "# Create dataframe with missing dates and unique store_nbr and family category\n",
    "missing_df = pd.DataFrame(\n",
    "    list(product(all_missing_dates, store_nbr_unique, family_unique)), \n",
    "    columns=['date', 'store_nbr', 'family']\n",
    ")\n",
    "\n",
    "missing_df.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Add sales and onpromotion columns to the missing_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in missing_df: Index(['date', 'store_nbr', 'family'], dtype='object')\n",
      "DataFrame content:\n",
      "            date  store_nbr                      family\n",
      "0    2013-12-25          1                  AUTOMOTIVE\n",
      "1    2013-12-25          1                   BABY CARE\n",
      "2    2013-12-25          1                      BEAUTY\n",
      "3    2013-12-25          1                   BEVERAGES\n",
      "4    2013-12-25          1                       BOOKS\n",
      "...         ...        ...                         ...\n",
      "7123 2016-12-25          9                     POULTRY\n",
      "7124 2016-12-25          9              PREPARED FOODS\n",
      "7125 2016-12-25          9                     PRODUCE\n",
      "7126 2016-12-25          9  SCHOOL AND OFFICE SUPPLIES\n",
      "7127 2016-12-25          9                     SEAFOOD\n",
      "\n",
      "[7128 rows x 3 columns]\n",
      "Error: \"['sales', 'onpromotion'] not found in axis\"\n",
      "Adding missing columns instead.\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7128 entries, 0 to 7127\n",
      "Data columns (total 5 columns):\n",
      " #   Column       Non-Null Count  Dtype         \n",
      "---  ------       --------------  -----         \n",
      " 0   date         7128 non-null   datetime64[ns]\n",
      " 1   store_nbr    7128 non-null   int64         \n",
      " 2   family       7128 non-null   object        \n",
      " 3   sales        0 non-null      object        \n",
      " 4   onpromotion  0 non-null      object        \n",
      "dtypes: datetime64[ns](1), int64(1), object(3)\n",
      "memory usage: 278.6+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Print column names and DataFrame content\n",
    "print(\"Columns in missing_df:\", missing_df.columns)\n",
    "print(\"DataFrame content:\\n\", missing_df)\n",
    "\n",
    "# Attempt to drop columns and handle case if columns do not exist\n",
    "try:\n",
    "    missing_df = missing_df.drop(columns=['sales', 'onpromotion'])\n",
    "except KeyError as e:\n",
    "    print(f\"Error: {e}\")\n",
    "    print(\"Adding missing columns instead.\")\n",
    "    # Add missing columns with None values\n",
    "    missing_df['sales'] = None\n",
    "    missing_df['onpromotion'] = None\n",
    "\n",
    "# Display the updated DataFrame\n",
    "print(missing_df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "missing_df = missing_df.with_columns(\n",
    "    sales=None,\n",
    "    onpromotion=None,\n",
    ")\n",
    "\n",
    "missing_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add new columns with default None values\n",
    "missing_df = missing_df.assign(sales=None, onpromotion=None)\n",
    "\n",
    "\n",
    "# Print the DataFrame structure\n",
    "print(missing_df.head())\n",
    "print(missing_df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Join missing_df to the train_data6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1141,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pc\\AppData\\Local\\Temp\\ipykernel_23676\\4085676085.py:3: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  combined_train_data6 = pd.concat([train_data6, missing_df], ignore_index=True)\n"
     ]
    }
   ],
   "source": [
    "# Join missing_df to the train_data6 using date\n",
    "\n",
    "combined_train_data6 = pd.concat([train_data6, missing_df], ignore_index=True)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date           0\n",
       "store_nbr      0\n",
       "family         0\n",
       "sales          0\n",
       "onpromotion    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the null values in each column\n",
    "\n",
    "train_data6.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Re-checking for mising date "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-12-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-12-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-12-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-12-25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date\n",
       "0 2013-12-25\n",
       "1 2014-12-25\n",
       "2 2015-12-25\n",
       "3 2016-12-25"
      ]
     },
     "execution_count": 1143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Check all missing dates\n",
    "\n",
    "all_missing_dates = missing_dates(train_data6, 'date')\n",
    "all_missing_dates\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3000888 entries, 0 to 3000887\n",
      "Data columns (total 5 columns):\n",
      " #   Column       Dtype         \n",
      "---  ------       -----         \n",
      " 0   date         datetime64[ns]\n",
      " 1   store_nbr    int64         \n",
      " 2   family       object        \n",
      " 3   sales        float64       \n",
      " 4   onpromotion  int64         \n",
      "dtypes: datetime64[ns](1), float64(1), int64(2), object(1)\n",
      "memory usage: 114.5+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Get a concise summary of the DataFrame\n",
    "print(train_data6.info())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                date     store_nbr         sales   onpromotion\n",
      "count                        3000888  3.000888e+06  3.000888e+06  3.000888e+06\n",
      "mean   2015-04-24 08:27:04.703088384  2.750000e+01  3.577757e+02  2.602770e+00\n",
      "min              2013-01-01 00:00:00  1.000000e+00  0.000000e+00  0.000000e+00\n",
      "25%              2014-02-26 18:00:00  1.400000e+01  0.000000e+00  0.000000e+00\n",
      "50%              2015-04-24 12:00:00  2.750000e+01  1.100000e+01  0.000000e+00\n",
      "75%              2016-06-19 06:00:00  4.100000e+01  1.958473e+02  0.000000e+00\n",
      "max              2017-08-15 00:00:00  5.400000e+01  1.247170e+05  7.410000e+02\n",
      "std                              NaN  1.558579e+01  1.101998e+03  1.221888e+01\n"
     ]
    }
   ],
   "source": [
    "# Get basic statistics for numerical columns\n",
    "print(train_data6.describe())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Filling missing values in sales with 0.0 and onpromotion with 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>store_nbr</th>\n",
       "      <th>family</th>\n",
       "      <th>sales</th>\n",
       "      <th>onpromotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>AUTOMOTIVE</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>BABY CARE</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>BEAUTY</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>BEVERAGES</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>BOOKS</td>\n",
       "      <td>0.000</td>\n",
       "      <td>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>3000883</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>9</td>\n",
       "      <td>POULTRY</td>\n",
       "      <td>438.133</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000884</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>9</td>\n",
       "      <td>PREPARED FOODS</td>\n",
       "      <td>154.553</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000885</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>9</td>\n",
       "      <td>PRODUCE</td>\n",
       "      <td>2419.729</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000886</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>9</td>\n",
       "      <td>SCHOOL AND OFFICE SUPPLIES</td>\n",
       "      <td>121.000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000887</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>9</td>\n",
       "      <td>SEAFOOD</td>\n",
       "      <td>16.000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000888 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              date  store_nbr                      family     sales  \\\n",
       "0       2013-01-01          1                  AUTOMOTIVE     0.000   \n",
       "1       2013-01-01          1                   BABY CARE     0.000   \n",
       "2       2013-01-01          1                      BEAUTY     0.000   \n",
       "3       2013-01-01          1                   BEVERAGES     0.000   \n",
       "4       2013-01-01          1                       BOOKS     0.000   \n",
       "...            ...        ...                         ...       ...   \n",
       "3000883 2017-08-15          9                     POULTRY   438.133   \n",
       "3000884 2017-08-15          9              PREPARED FOODS   154.553   \n",
       "3000885 2017-08-15          9                     PRODUCE  2419.729   \n",
       "3000886 2017-08-15          9  SCHOOL AND OFFICE SUPPLIES   121.000   \n",
       "3000887 2017-08-15          9                     SEAFOOD    16.000   \n",
       "\n",
       "         onpromotion  \n",
       "0                  0  \n",
       "1                  0  \n",
       "2                  0  \n",
       "3                  0  \n",
       "4                  0  \n",
       "...              ...  \n",
       "3000883            0  \n",
       "3000884            1  \n",
       "3000885          148  \n",
       "3000886            8  \n",
       "3000887            0  \n",
       "\n",
       "[3000888 rows x 5 columns]"
      ]
     },
     "execution_count": 1146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filling missing values in sales with 0.0 and onpromotion with 0\n",
    "\n",
    "train_data6['sales'] = train_data6['sales'].fillna(0.0)\n",
    "train_data6['onpromotion'] = train_data6['onpromotion'].fillna(0)\n",
    "\n",
    "train_data6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date           0\n",
       "store_nbr      0\n",
       "family         0\n",
       "sales          0\n",
       "onpromotion    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Re-check for missing values\n",
    "train_data6.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store the original train columns\n",
    "\n",
    "train_col_org = train_data6.columns\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>store_nbr</th>\n",
       "      <th>family</th>\n",
       "      <th>sales</th>\n",
       "      <th>onpromotion</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01</td>\n",
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       "      <td>AUTOMOTIVE</td>\n",
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       "      <td>0</td>\n",
       "      <td>2013</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>BABY CARE</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>BEAUTY</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>BEVERAGES</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>BOOKS</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000883</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>9</td>\n",
       "      <td>POULTRY</td>\n",
       "      <td>438.133</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000884</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>9</td>\n",
       "      <td>PREPARED FOODS</td>\n",
       "      <td>154.553</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000885</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>9</td>\n",
       "      <td>PRODUCE</td>\n",
       "      <td>2419.729</td>\n",
       "      <td>148</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000886</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>9</td>\n",
       "      <td>SCHOOL AND OFFICE SUPPLIES</td>\n",
       "      <td>121.000</td>\n",
       "      <td>8</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000887</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>9</td>\n",
       "      <td>SEAFOOD</td>\n",
       "      <td>16.000</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000888 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              date  store_nbr                      family     sales  \\\n",
       "0       2013-01-01          1                  AUTOMOTIVE     0.000   \n",
       "1       2013-01-01          1                   BABY CARE     0.000   \n",
       "2       2013-01-01          1                      BEAUTY     0.000   \n",
       "3       2013-01-01          1                   BEVERAGES     0.000   \n",
       "4       2013-01-01          1                       BOOKS     0.000   \n",
       "...            ...        ...                         ...       ...   \n",
       "3000883 2017-08-15          9                     POULTRY   438.133   \n",
       "3000884 2017-08-15          9              PREPARED FOODS   154.553   \n",
       "3000885 2017-08-15          9                     PRODUCE  2419.729   \n",
       "3000886 2017-08-15          9  SCHOOL AND OFFICE SUPPLIES   121.000   \n",
       "3000887 2017-08-15          9                     SEAFOOD    16.000   \n",
       "\n",
       "         onpromotion  year  month  day  weekday  quarter  \n",
       "0                  0  2013      1    1        1        1  \n",
       "1                  0  2013      1    1        1        1  \n",
       "2                  0  2013      1    1        1        1  \n",
       "3                  0  2013      1    1        1        1  \n",
       "4                  0  2013      1    1        1        1  \n",
       "...              ...   ...    ...  ...      ...      ...  \n",
       "3000883            0  2017      8   15        1        3  \n",
       "3000884            1  2017      8   15        1        3  \n",
       "3000885          148  2017      8   15        1        3  \n",
       "3000886            8  2017      8   15        1        3  \n",
       "3000887            0  2017      8   15        1        3  \n",
       "\n",
       "[3000888 rows x 10 columns]"
      ]
     },
     "execution_count": 1149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def date_features(df):\n",
    "    # Ensure 'date' column is in datetime format\n",
    "    df['date'] = pd.to_datetime(df['date'])\n",
    "    \n",
    "    # Extracting the year from the date and adding it to the df as a new column\n",
    "    df['year'] = df['date'].dt.year\n",
    "\n",
    "    # Extracting the month from the date and adding it to df as new column\n",
    "    df['month'] = df['date'].dt.month\n",
    "\n",
    "    # Extracting the day from the date and adding it to df as new column\n",
    "    df['day'] = df['date'].dt.day\n",
    "\n",
    "    # Extracting the weekday from the date and adding it to df as new column\n",
    "    df['weekday'] = df['date'].dt.weekday\n",
    "\n",
    "    # Extracting the quarter from the date and adding it to df as new column\n",
    "    df['quarter'] = df['date'].dt.quarter\n",
    "\n",
    "    return df\n",
    "\n",
    "# Applying the function to the DataFrame\n",
    "train_data6 = date_features(train_data6)\n",
    "\n",
    "# Preview of the train_data6 DataFrame\n",
    "train_data6\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Select columns to keep for the boxplot\n",
    "columns_to_keep = [col for col in train_data6.columns if col not in ['year', 'sales', 'onpromotion', 'family']]\n",
    "df_for_boxplot = train_data6[columns_to_keep]\n",
    "\n",
    "# Plotting the boxplot\n",
    "plt.figure(figsize=(12, 5))\n",
    "sns.boxplot(data=df_for_boxplot, orient='h')\n",
    "plt.title('Distribution of Features (Excluding Sales and Onpromotion)')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/KbyRP+yww6JPnz5x/vnnx+zZs2O33XaLiRMnxj333BNnnnlmfPGLX4yIiIsvvjimTZsWf/3rX6NZs2bRtWvXuOCCC+KnP/1pDBw4sMqb+IYNG8aDDz4Yxx13XPTs2TMmTJgQ999/f5x33nnrvVj64osvLvxOh6FDh0a9evXixhtvjOXLl8fPf/7zwnrdunWLkpKSuOKKK2LhwoVRWlpa+F0T1bnyyivj4IMPjr333juGDBlSuNVueXl5XHjhhZ91mqt1+eWXx+TJk6Nnz55x0kknRefOnWPBggXx7LPPxiOPPFJ4U33ooYfG3XffHUcccUQccsghMWvWrLjhhhuic+fOhU+nPotBgwbFj3/844iIDf7K1emnnx4fffRRHHHEEdGxY8dYsWJFPPnkk3HHHXfEjjvuWLhu5aCDDooGDRrEYYcdFqecckosWbIkRo8eHS1btix8OrIu3/ve9+LOO++MU089NSZPnhy9evWKioqKeOmll+LOO++Mhx56KPbcc88YMWJEPPbYY3HIIYdEu3btYv78+XHdddfF9ttvH/vss89nmxyA2lKLd9oC2OSqu9Vuw4YNU7du3dL1119fuB3sGosXL05nnXVWatOmTapfv3760pe+lK688srCes8880yqV69eldvnprT6NrE9evRIbdq0Se+//35KafWtdps0aZJmzpyZDjrooNS4cePUqlWrNHz48Cq3fU1p7VvtprT6drD9+/dPTZs2TY0bN059+vRJTz755FrHOHr06NS+fftUUlKyQbfdfeSRR1KvXr1So0aNUllZWTrssMPSjBkzqqyzMbfa/d9j/rh58+alYcOGpbZt26b69eunbbfdNvXt2zf95je/KaxTWVmZLr300tSuXbtUWlqaunfvnu6777503HHHpXbt2hXWW3M73SuvvHKt/VR3q9015s6dm0pKSlKHDh02+HgmTJiQTjjhhNSxY8fUtGnT1KBBg7Tzzjun008/Pc2bN6/KuuPHj09du3ZNDRs2TDvuuGO64oor0s0335wiIs2aNauw3sdvtZtSSitWrEhXXHFF6tKlSyotLU1f+MIX0h577JEuuuiitHDhwpRSSn/961/TN77xjdSmTZvUoEGD1KZNm3T00UenV155ZYOPB6CuKUppA37tLgCfaPDgwfGnP/1pk/ytPZ/du+++G61bt44LLrggfvazn9X2cAAI13wAsIUaO3ZsVFRUxPe+973aHgoA/59rPgDYokyaNClmzJgRl1xySQwYMGCddxkDID/xAcAWZcSIEfHkk09Gr1694tprr63t4QDwP1zzAQAAZOGaDwAAIAvxAQAAZLHR13xUVlbGnDlzolmzZlFUVLQpxwQAAGxGUkqxePHiaNOmTRQXr/vzjY2Ojzlz5kTbtm03dnMAAGAL89Zbb8X222+/zsc3Oj6aNWtW2EFZWdnGPg0AALCZW7RoUbRt27bQCOuy0fGx5qtWZWVl4gMAAPjEyzFccA4AAGQhPgAAgCzEBwAAkIX4AAAAshAfAABAFuIDAADIQnwAAABZiA8AACAL8QEAAGQhPgAAgCzEBwAAkIX4AAAAshAfAABAFuIDAADIQnwAAABZiA8AACAL8QEAAGQhPgAAgCzEBwAAkIX4AAAAshAfAABAFuIDAADIQnwAAABZiA8AACAL8QEAAGQhPgAAgCzEBwAAkIX4AAAAshAfAABAFuIDAADIQnwAAABZiA8AACAL8QEAAGQhPgAAgCzq1fYAgIh58+bFwoULa3sYAJ875eXl0apVq9oeBnxuiA+oZfPmzYvvfm9QrFyxvLaHAvC5U79Badz2u1sFCGQiPqCWLVy4MFauWB5L2/eOyobltT0c+FSKl34QjWY9Fkt32i8qGzWv7eHAp1K8bGHE64/GwoULxQdkIj6gjqhsWB6VTbap7WHARqls1Nz5C8AncsE5AACQhfgAAACyEB8AAEAW4gMAAMhCfAAAAFmIDwAAIAvxAQAAZCE+AACALMQHAACQhfgAAACyEB8AAEAW4gMAAMhCfAAAAFmIDwAAIAvxAQAAZCE+AACALMQHAACQhfgAAACyEB8AAEAW4gMAAMhCfAAAAFmIDwAAIAvxAQAAZCE+AACALMQHAACQhfgAAACyEB8AAEAW4gMAAMhCfAAAAFmIDwAAIAvxAQAAZCE+AACALMQHAACQhfgAAACyEB8AAEAW4gMAAMhCfAAAAFmIDwAAIAvxAQAAZCE+AACALMQHAACQhfgAAACyEB8AAEAW4gMAAMhCfAAAAFmIDwAAIAvxAQAAZCE+AACALMQHAACQhfgAAACyEB8AAEAW4gMAAMhCfAAAAFmIDwAAIAvxAQAAZCE+AACALMQHAACQhfgAAACyEB8AAEAW4gMAAMhCfAAAAFmIDwAAIAvxAQAAZCE+AACALMQHAACQhfgAAACyEB8AAEAW4gMAAMhCfAAAAFmIDwAAIAvxAQAAZCE+AACALMQHAACQxRYRH8uWLYtXXnklli1bVttDAQCALDbH98BbRHy8+eabcfLJJ8ebb75Z20MBAIAsNsf3wFtEfAAAAHWf+AAAALIQHwAAQBbiAwAAyEJ8AAAAWYgPAAAgC/EBAABkIT4AAIAsxAcAAJCF+AAAALIQHwAAQBbiAwAAyEJ8AAAAWYgPAAAgC/EBAABkIT4AAIAsxAcAAJCF+AAAALIQHwAAQBbiAwAAyEJ8AAAAWYgPAAAgC/EBAABkIT4AAIAsxAcAAJCF+AAAALIQHwAAQBbiAwAAyEJ8AAAAWYgPAAAgC/EBAABkIT4AAIAsxAcAAJCF+AAAALIQHwAAQBbiAwAAyEJ8AAAAWYgPAAAgC/EBAABkIT4AAIAsxAcAAJCF+AAAALIQHwAAQBbiAwAAyEJ8AAAAWYgPAAAgC/EBAABkIT4AAIAsxAcAAJCF+AAAALIQHwAAQBbiAwAAyEJ8AAAAWYgPAAAgC/EBAABkIT4AAIAsxAcAAJCF+AAAALIQHwAAQBbiAwAAyEJ8AAAAWYgPAAAgC/EBAABkIT4AAIAsxAcAAJCF+AAAALIQHwAAQBbiAwAAyEJ8AAAAWYgPAAAgC/EBAABkIT4AAIAsxAcAAJCF+AAAALIQHwAAQBb1NnTF5cuXx/Llywt/XrRoUY0M6LN44403ansI8Kk5bwFql9dhNleb47m7wfFx2WWXxUUXXVSTY/nMLrnkktoeAgCwmfH+AfLZ4Pg499xz44c//GHhz4sWLYq2bdvWyKA21vnnnx/t2rWr7WHAp/LGG2/4Dx9ALfL+gc3V5vgeYoPjo7S0NEpLS2tyLJ9Zu3btokOHDrU9DABgM+L9A+TjgnMAACAL8QEAAGQhPgAAgCzEBwAAkIX4AAAAshAfAABAFuIDAADIQnwAAABZiA8AACAL8QEAAGQhPgAAgCzEBwAAkIX4AAAAshAfAABAFuIDAADIQnwAAABZiA8AACAL8QEAAGQhPgAAgCzEBwAAkIX4AAAAshAfAABAFuIDAADIQnwAAABZiA8AACAL8QEAAGQhPgAAgCzEBwAAkIX4AAAAshAfAABAFuIDAADIQnwAAABZiA8AACAL8QEAAGQhPgAAgCzEBwAAkIX4AAAAshAfAABAFuIDAADIQnwAAABZiA8AACAL8QEAAGQhPgAAgCzEBwAAkIX4AAAAshAfAABAFuIDAADIQnwAAABZiA8AACAL8QEAAGQhPgAAgCzEBwAAkIX4AAAAshAfAABAFuIDAADIQnwAAABZiA8AACAL8QEAAGQhPgAAgCzEBwAAkIX4AAAAshAfAABAFuIDAADIQnwAAABZiA8AACAL8QEAAGQhPgAAgCzEBwAAkIX4AAAAshAfAABAFuIDAADIQnwAAABZiA8AACAL8QEAAGQhPgAAgCy2iPjYYYcd4je/+U3ssMMOtT0UAADIYnN8D1yvtgewKTRs2DA6dOhQ28MAAIBsNsf3wFvEJx8AAEDdJz4AAIAsxAcAAJCF+AAAALIQHwAAQBbiAwAAyEJ8AAAAWYgPAAAgC/EBAABkIT4AAIAsxAcAAJCF+AAAALIQHwAAQBbiAwAAyEJ8AAAAWYgPAAAgC/EBAABkIT4AAIAsxAcAAJCF+AAAALIQHwAAQBbiAwAAyEJ8AAAAWYgPAAAgC/EBAABkIT4AAIAsxAcAAJCF+AAAALIQHwAAQBbiAwAAyEJ8AAAAWYgPAAAgC/EBAABkIT4AAIAsxAcAAJCF+AAAALIQHwAAQBbiAwAAyEJ8AAAAWYgPAAAgC/EBAABkIT4AAIAsxAcAAJCF+AAAALIQHwAAQBbiAwAAyEJ8AAAAWYgPAAAgC/EBAABkIT4AAIAsxAcAAJCF+AAAALIQHwAAQBbiAwAAyEJ8AAAAWYgPAAAgC/EBAABkIT4AAIAsxAcAAJCF+AAAALIQHwAAQBbiAwAAyEJ8AAAAWYgPAAAgC/EBAABkIT4AAIAsxAcAAJCF+AAAALIQHwAAQBbiAwAAyEJ8AAAAWYgPAAAgC/EBAABkUa+2BwCsVrxsYW0PAT614qUfVPlf2Jx43YX8xAfUsvLy8qjfoDTi9Udreyiw0RrNeqy2hwAbpX6D0igvL6/tYcDnhviAWtaqVau47Xe3xsKF/gYOILfy8vJo1apVbQ8DPjfEB9QBrVq18h8/AGCL54JzAAAgC/EBAABkIT4AAIAsxAcAAJCF+AAAALIQHwAAQBbiAwAAyEJ8AAAAWYgPAAAgC/EBAABkIT4AAIAsxAcAAJCF+AAAALIQHwAAQBbiAwAAyEJ8AAAAWYgPAAAgC/EBAABkIT4AAIAsxAcAAJCF+AAAALIQHwAAQBbiAwAAyEJ8AAAAWYgPAAAgC/EBAABkIT4AAIAsxAcAAJCF+AAAALIQHwAAQBbiAwAAyEJ8AAAAWYgPAAAgC/EBAABkIT4AAIAs6m3shimliIhYtGjRJhsMAACw+VnTBGsaYV02Oj4WL14cERFt27bd2KcAAAC2IIsXL47y8vJ1Pl6UPilP1qGysjLmzJkTzZo1i6Kioo0e4KawaNGiaNu2bbz11ltRVlZWq2PZEpnfmmV+a5b5rVnmt2aZ35plfmueOa5ZdWl+U0qxePHiaNOmTRQXr/vKjo3+5KO4uDi23377jd28RpSVldX6xG/JzG/NMr81y/zWLPNbs8xvzTK/Nc8c16y6Mr/r+8RjDRecAwAAWYgPAAAgiy0iPkpLS2P48OFRWlpa20PZIpnfmmV+a5b5rVnmt2aZ35plfmueOa5Zm+P8bvQF5wAAAJ/GFvHJBwAAUPeJDwAAIAvxAQAAZCE+AACALLLGx2WXXRY9evSIZs2aRcuWLWPAgAHx8ssvV1ln2bJlMWzYsNh6662jadOmceSRR8a8efOqrHPGGWfEHnvsEaWlpdGtW7e19vPyyy9Hnz59olWrVtGwYcNo3759/PSnP42VK1eud3xvvvlmHHLIIdG4ceNo2bJlnH322bFq1arPfNy51PX5LSoqWuvnj3/842c+7lxyze//eu2116JZs2bRvHnzTxyf83e1mppf5+9qnzS/s2fPrnaunnrqqfWOz/m7Wk3Nr/N3tQ15fUgpxciRI6NDhw5RWloa2223XVxyySXrHd+CBQvi2GOPjbKysmjevHkMGTIklixZ8pmOOae6Pr877rjjWufv5Zdf/pmOObdcc3zhhRdW+//3Jk2arHd8OV+Ds8bHo48+GsOGDYunnnoqHn744Vi5cmUcdNBB8eGHHxbWOeuss+Lee++NcePGxaOPPhpz5syJb37zm2s91wknnBDf/va3q91P/fr1Y9CgQTFx4sR4+eWX4+qrr47Ro0fH8OHD1zm2ioqKOOSQQ2LFihXx5JNPxi233BJjx46NCy644LMfeCZ1eX7XGDNmTMydO7fwM2DAgI0+3txyze8aK1eujKOPPjr23XffTxyb87eqTT2/azh/V9uQ+X3kkUeqzNUee+yxznWdv1Vt6vldw/m72ifN7w9+8IO46aabYuTIkfHSSy/F+PHjY6+99lrv+I499tiYPn16PPzww3HffffFY489FieffPLGH3BmdX1+IyJGjBhR5fw9/fTTN+5ga0muOf7xj39cZZ7mzp0bnTt3jqOOOmqdY8v+Gpxq0fz581NEpEcffTSllNIHH3yQ6tevn8aNG1dY58UXX0wRkaZMmbLW9sOHD0+77bbbBu3rrLPOSvvss886H3/ggQdScXFxeueddwrLrr/++lRWVpaWL1++gUdUt9Sl+U0ppYhIf/7znzd4/HVdTc/vOeeck7773e+mMWPGpPLy8vWOxflbs/ObkvP349Y1v7NmzUoRkaZOnbrBY3H+1uz8puT8/bh1ze+MGTNSvXr10ksvvbTBY5kxY0aKiPTPf/6zsGzChAmpqKgovf3225/iqOqOujS/KaXUrl27NGrUqE+1TV2X6z3atGnTUkSkxx57bJ3r5H4NrtVrPhYuXBgREVtttVVERDzzzDOxcuXK6NevX2Gdjh07xg477BBTpkzZ6P289tpr8eCDD0bv3r3Xuc6UKVPiy1/+crRq1aqwrH///rFo0aKYPn36Ru+7NtWl+V1j2LBhsc0228Ree+0VN998c6TN+NfM1OT8Tpo0KcaNGxe//vWvN2h952/Nzu8azt8Nd/jhh0fLli1jn332ifHjx693Xedvzc7vGs7fT3bvvfdG+/bt47777ouddtopdtxxxzjxxBNjwYIF69xmypQp0bx589hzzz0Ly/r16xfFxcXx9NNPf9pDqxPq0vyucfnll8fWW28d3bt3jyuvvHKz+lpmdXK9R7vpppuiQ4cO6/2UP/drcL1N/owbqLKyMs4888zo1atX7LrrrhER8c4770SDBg3W+v51q1at4p133vnU+/jqV78azz77bCxfvjxOPvnkGDFixDrXfeedd6pM+pr9rnlsc1PX5jdi9UemBxxwQDRu3DgmTpwYQ4cOjSVLlsQZZ5zxqfdd22pyft97770YPHhw3HbbbVFWVrZB2zh/a3Z+I5y/G6pp06bxi1/8Inr16hXFxcVx1113xYABA+Ivf/lLHH744dVu4/yt2fmNcP5uqNdffz3eeOONGDduXNx6661RUVERZ511VgwcODAmTZpU7TbvvPNOtGzZssqyevXqxVZbbeX8/ZiNmd+I1dc57L777rHVVlvFk08+Geeee27MnTs3rrrqqo06xtqW4z1axOprSG6//fb4yU9+st71cr8G11p8DBs2LF544YV4/PHHa2wfd9xxRyxevDiee+65OPvss2PkyJFxzjnn1Nj+6pK6OL8/+9nPCv/cvXv3+PDDD+PKK6/cLP/jV5Pze9JJJ8UxxxwT++233yZ/7s1FXZxf5++G2WabbeKHP/xh4c89evSIOXPmxJVXXrneN8dbkro4v87fDVNZWRnLly+PW2+9NTp06BAREb/97W9jjz32iJdffjl22WWXTb7PuqYuzu//nvNdu3aNBg0axCmnnBKXXXZZlJaWbvJx1rQc79EiIv785z/H4sWL47jjjqvR/XxatfK1q9NOOy3uu+++mDx5cmy//faF5dtuu22sWLEiPvjggyrrz5s3L7bddttPvZ+2bdtG586d4+ijj47LL788LrzwwqioqKh23W233XatOwqs+fPG7Ls21cX5rU7Pnj3j3//+dyxfvvxT77s21fT8Tpo0KUaOHBn16tWLevXqxZAhQ2LhwoVRr169uPnmm6vdxvlbs/NbHefvhuvZs2e89tpr63zc+Vuz87uubZy/a2vdunXUq1ev8MY4IqJTp04RsfpuQNXZdtttY/78+VWWrVq1KhYsWOD8/ZiNmd/q9OzZM1atWhWzZ8/e4G3qipyvETfddFMceuiha32q8XG5X4OzxkdKKU477bT485//HJMmTYqddtqpyuN77LFH1K9fP/76178Wlr388svx5ptvxt577/2Z9l1ZWRkrV66MysrKah/fe++94/nnn6/yAvLwww9HWVlZdO7c+TPtO5e6PL/VmTZtWnzhC1/YbP7WItf8TpkyJaZNm1b4GTFiRDRr1iymTZsWRxxxRLXbOH9rdn6r4/zdcNOmTYvWrVuv83Hnb83O77q2cf6urVevXrFq1aqYOXNmYdkrr7wSERHt2rWrdpu99947Pvjgg3jmmWcKyyZNmhSVlZXRs2fPDd53barL81udadOmRXFx8Vpfd6vLcr9GzJo1KyZPnhxDhgz5xHWzvwZv8kvY1+P73/9+Ki8vT3/729/S3LlzCz8fffRRYZ1TTz017bDDDmnSpEnpX//6V9p7773T3nvvXeV5Xn311TR16tR0yimnpA4dOqSpU6emqVOnFq7Iv+2229Idd9yRZsyYkWbOnJnuuOOO1KZNm3TssccWnuPuu+9Ou+yyS+HPq1atSrvuums66KCD0rRp09KDDz6YWrRokc4999wanpVNpy7P7/jx49Po0aPT888/n1599dV03XXXpcaNG6cLLrighmdl08k1vx9X3d2YnL9559f5+1+fNL9jx45Nv//979OLL76YXnzxxXTJJZek4uLidPPNNxeew/mbd36dv//1SfNbUVGRdt9997TffvulZ599Nv3rX/9KPXv2TAceeGDhOZ5++um0yy67pH//+9+FZV/72tdS9+7d09NPP50ef/zx9KUvfSkdffTRNTwrm05dnt8nn3wyjRo1Kk2bNi3NnDkz3XbbbalFixZp0KBBGWZm08n937if/vSnqU2bNmnVqlVrjaW2X4OzxkdEVPszZsyYwjpLly5NQ4cOTV/4whdS48aN0xFHHJHmzp1b5Xl69+5d7fPMmjUrpZTSH//4x7T77runpk2bpiZNmqTOnTunSy+9NC1durTwHGPGjEkfb6/Zs2engw8+ODVq1Chts8026Uc/+lFauXJljc3HplaX53fChAmpW7duhW122223dMMNN6SKiooanZNNKdf8flx1b46dv3nn1/n7X580v2PHjk2dOnVKjRs3TmVlZWmvvfaqcuvIlJy/uefX+ftfG/L68Pbbb6dvfvObqWnTpqlVq1Zp8ODB6b333is8Pnny5LW2ee+999LRRx+dmjZtmsrKytLxxx+fFi9eXFPTscnV5fl95plnUs+ePVN5eXlq2LBh6tSpU7r00kvTsmXLanJKNrmcc1xRUZG23377dN5551U7ltp+DS5KaTO+1x4AALDZqNXf8wEAAHx+iA8AACAL8QEAAGQhPgAAgCzEBwAAkIX4AAAAshAfAABAFuIDAADIQnwAAABZiA8AACAL8QFAraioqIjKysraHgYAGYkPAOLWW2+NrbfeOpYvX15l+YABA+J73/teRETcc889sfvuu0fDhg2jffv2cdFFF8WqVasK61511VXx5S9/OZo0aRJt27aNoUOHxpIlSwqPjx07Npo3bx7jx4+Pzp07R2lpabz55pt5DhCAOkF8ABBHHXVUVFRUxPjx4wvL5s+fH/fff3+ccMIJ8fe//z0GDRoUP/jBD2LGjBlx4403xtixY+OSSy4prF9cXBy//OUvY/r06XHLLbfEpEmT4pxzzqmyn48++iiuuOKKuOmmm2L69OnRsmXLbMcIQO0rSiml2h4EALVv6NChMXv27HjggQciYvUnGb/+9a/jtddeiwMPPDD69u0b5557bmH92267Lc4555yYM2dOtc/3pz/9KU499dR49913I2L1Jx/HH398TJs2LXbbbbeaPyAA6hzxAUBEREydOjV69OgRb7zxRmy33XbRtWvXOOqoo+JnP/tZtGjRIpYsWRIlJSWF9SsqKmLZsmXx4YcfRuPGjeORRx6Jyy67LF566aVYtGhRrFq1qsrjY8eOjVNOOSWWLVsWRUVFtXikANSWerU9AADqhu7du8duu+0Wt956axx00EExffr0uP/++yMiYsmSJXHRRRfFN7/5zbW2a9iwYcyePTsOPfTQ+P73vx+XXHJJbLXVVvH444/HkCFDYsWKFdG4ceOIiGjUqJHwAPgcEx8AFJx44olx9dVXx9tvvx39+vWLtm3bRkTE7rvvHi+//HLsvPPO1W73zDPPRGVlZfziF7+I4uLVlxPeeeed2cYNwOZBfABQcMwxx8SPf/zjGD16dNx6662F5RdccEEceuihscMOO8TAgQOjuLg4nnvuuXjhhRfi4osvjp133jlWrlwZ1157bRx22GHxxBNPxA033FCLRwJAXeRuVwAUlJeXx5FHHhlNmzaNAQMGFJb3798/7rvvvpg4cWL06NEjvvKVr8SoUaOiXbt2ERGx2267xVVXXRVXXHFF7LrrrnH77bfHZZddVktHAUBd5YJzAKro27dvdOnSJX75y1/W9lAA2MKIDwAiIuL999+Pv/3tbzFw4MCYMWNG7LLLLrU9JAC2MK75ACAiVt/t6v33348rrrhCeABQI3zyAQAAZOGCcwAAIAvxAQAAZCE+AACALMQHAACQhfgAAACyEB8AAEAW4gMAAMhCfAAAAFn8P46JGZ4sfbDnAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "sns.boxplot(train_data6['year'], orient = 'h')\n",
    "plt.title('Boxplot of Yearly Sales')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the figure size and create a horizontal boxplot for 'sales' column\n",
    "plt.figure(figsize = (15,5))\n",
    "sns.boxplot(train_data6['onpromotion'], orient = 'h')\n",
    "\n",
    "# Add title to the plot\n",
    "plt.title('Distribution of Sales')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the figure size and create a horizontal boxplot for 'onpromotion' column\n",
    "plt.figure(figsize = (15,5))\n",
    "sns.boxplot(train_data6['sales'], orient = 'h')\n",
    "\n",
    "# Add title to the plot\n",
    "plt.title('Distribution of Onpromotion')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 5))\n",
    "sns.kdeplot(train_data6.drop(columns=['year', 'sales']), alpha=0.3, fill=True)\n",
    "plt.title('Density Plot for Features')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 5))\n",
    "sns.kdeplot(train_data6['sales'], alpha=0.3, fill=True)\n",
    "plt.title('Density Plot for Sales')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sales skewness = 7.358757818882655\n",
      "onpromotion skewness = 11.166561930715057\n",
      "Store skewness = 0.0\n"
     ]
    }
   ],
   "source": [
    "# Calculate skewness for 'sales' column\n",
    "sales_skew = train_data6['sales'].skew()\n",
    "\n",
    "# Calculate skewness for numerical columns only\n",
    "onpromotion_skew = train_data6['onpromotion'].skew()\n",
    "store_skew = train_data6['store_nbr'].skew()\n",
    "\n",
    "# Print the skewness for each column\n",
    "print(f'Sales skewness = {sales_skew}')\n",
    "print(f'onpromotion skewness = {onpromotion_skew}')\n",
    "print(f'Store skewness = {store_skew}')\n",
    "\n",
    "# Skip the date skewness calculation if not applicable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 4)) \n",
    "sns.lineplot(train_data6, x=train_data6['date'], y=train_data6['sales'])\n",
    "plt.title(f'Daily sales over time')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bi-Variate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Select the columns of interest\n",
    "columns_of_interest = ['store_nbr', 'sales', 'onpromotion', 'year', 'month', 'day', 'weekday', 'quarter']\n",
    "\n",
    "# Ensure columns are numeric\n",
    "train_data6['store_nbr'] = pd.to_numeric(train_data6['store_nbr'], errors='coerce')\n",
    "train_data6['sales'] = pd.to_numeric(train_data6['sales'], errors='coerce')\n",
    "train_data6['onpromotion'] = pd.to_numeric(train_data6['onpromotion'], errors='coerce')\n",
    "\n",
    "# Create a DataFrame with only the columns of interest\n",
    "df_for_correlation = train_data6[columns_of_interest]\n",
    "\n",
    "# Compute the correlation matrix\n",
    "correlation_matrix = df_for_correlation.corr()\n",
    "\n",
    "# Plot the heatmap\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', vmin=-1, vmax=1)\n",
    "plt.title('Correlation Heatmap ')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize relationships between sales and promotion\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.scatter(train_data6['onpromotion'], train_data6['sales'])\n",
    "plt.xlabel('On Promotion')\n",
    "plt.ylabel('Sales')\n",
    "plt.title('Sales vs OnPromotion')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Decomposition "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#  train_data6 is a Pandas DataFrame\n",
    "seasonal_df = train_data6[['date', 'sales']]\n",
    "\n",
    "# Set the date column as the index\n",
    "seasonal_df.set_index('date', inplace=True)\n",
    "\n",
    "# Perform seasonal decomposition\n",
    "additive_results = seasonal_decompose(seasonal_df['sales'], model='additive', period=365)\n",
    "\n",
    "# Update rcParams to set the figure size for the plot\n",
    "plt.rcParams.update({'figure.figsize': (15, 5)})\n",
    "\n",
    "# Plot the decomposed components (trend, seasonal, residual)\n",
    "additive_results.plot()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1161,
   "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>Trend</th>\n",
       "      <th>Seasonality</th>\n",
       "      <th>Residual</th>\n",
       "      <th>Observed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.961304</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.206308</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.169477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15.778771</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.451767</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.967582</td>\n",
       "      <td>NaN</td>\n",
       "      <td>438.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.658920</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154.553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.628191</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2419.729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.289799</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.142098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000888 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Trend  Seasonality  Residual  Observed\n",
       "date                                              \n",
       "2013-01-01    NaN     2.961304       NaN     0.000\n",
       "2013-01-01    NaN     2.206308       NaN     0.000\n",
       "2013-01-01    NaN    -0.169477       NaN     0.000\n",
       "2013-01-01    NaN    15.778771       NaN     0.000\n",
       "2013-01-01    NaN    -0.451767       NaN     0.000\n",
       "...           ...          ...       ...       ...\n",
       "2017-08-15    NaN    -5.967582       NaN   438.133\n",
       "2017-08-15    NaN     6.658920       NaN   154.553\n",
       "2017-08-15    NaN     2.628191       NaN  2419.729\n",
       "2017-08-15    NaN     5.289799       NaN   121.000\n",
       "2017-08-15    NaN    -2.142098       NaN    16.000\n",
       "\n",
       "[3000888 rows x 4 columns]"
      ]
     },
     "execution_count": 1161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract components and create a new DataFrame\n",
    "decomposition_df = pd.DataFrame({\n",
    "        'Trend': additive_results.trend,\n",
    "        'Seasonality': additive_results.seasonal,\n",
    "        'Residual': additive_results.resid,\n",
    "        'Observed': additive_results.observed,\n",
    "            \n",
    "            }) \n",
    "decomposition_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Stationarity Test using Augmented Dickey -Fuller(ADF) test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['date', 'store_nbr', 'family', 'sales', 'onpromotion', 'year', 'month',\n",
      "       'day', 'weekday', 'quarter'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pc\\AppData\\Local\\Temp\\ipykernel_23676\\514904604.py:8: FutureWarning: 'w' is deprecated and will be removed in a future version, please use 'W' instead.\n",
      "  resampled_df = train_data6.groupby(pd.Grouper(freq='1w'))['sales'].mean()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "date\n",
       "2013-01-06    206.843478\n",
       "2013-01-13    190.285220\n",
       "2013-01-20    189.835452\n",
       "2013-01-27    182.152050\n",
       "2013-02-03    198.564267\n",
       "                 ...    \n",
       "2017-07-23    460.504663\n",
       "2017-07-30    474.732392\n",
       "2017-08-06    513.884368\n",
       "2017-08-13    431.730145\n",
       "2017-08-20    427.492801\n",
       "Freq: W-SUN, Name: sales, Length: 242, dtype: float64"
      ]
     },
     "execution_count": 1162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ensure 'date' is in the DataFrame columns\n",
    "print(train_data6.columns)\n",
    "\n",
    "# If 'date' is a column, set it as the index\n",
    "train_data6.set_index('date', inplace=True)\n",
    "\n",
    "# Now group by the index 'date' and calculate the mean of 'sales'\n",
    "resampled_df = train_data6.groupby(pd.Grouper(freq='1w'))['sales'].mean()\n",
    "\n",
    "# Check the shape of the resampled DataFrame\n",
    "resampled_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['store_nbr', 'family', 'sales', 'onpromotion', 'year', 'month', 'day',\n",
      "       'weekday', 'quarter'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(train_data6.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -1.590375\n",
      "p-value: 0.488427\n",
      "Critical Values:\n",
      "\t1%: -3.459\n",
      "\t5%: -2.874\n",
      "\t10%: -2.573\n",
      "The Data is not stationary and may have a unit root.\n"
     ]
    }
   ],
   "source": [
    "# Assuming 'timeseries' is your time series data\n",
    "adf_result = adfuller(resampled_df)\n",
    "\n",
    "print('ADF Statistic: %f' % adf_result[0])\n",
    "print('p-value: %f' % adf_result[1])\n",
    "print('Critical Values:')\n",
    "for key, value in adf_result[4].items():\n",
    "    print('\\t%s: %.3f' % (key, value))\n",
    "\n",
    "# Interpretation\n",
    "if adf_result[1] < 0.05:\n",
    "    print(\"The Data is stationary.\")\n",
    "else:\n",
    "    print(\"The Data is not stationary and may have a unit root.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['store_nbr', 'family', 'sales', 'onpromotion', 'year', 'month', 'day',\n",
      "       'weekday', 'quarter'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(train_data6.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Visualization of the stationearity using rolling Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1166,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the rolling Average\n",
    "rolling_average = train_data6['sales'].rolling(window=7).mean().rename('sales_average')\n",
    "\n",
    "# Getting the rolling Standard Deviation\n",
    "rolling_std = train_data6['sales'].rolling(window=7).std().rename('sales_std')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1167,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create both figure and axis objects\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "\n",
    "# Make plot\n",
    "ax.plot(train_data6['sales'], label='Original')\n",
    "ax.plot(rolling_average, label='Rolling Mean')\n",
    "ax.plot(rolling_std, label='Rolling Std Dev')\n",
    "\n",
    "# Add legend and title\n",
    "ax.legend(loc='best')\n",
    "ax.set_title(\"Rolling Statistics\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Key Findings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- a. We have detected gaps in the date column, requiring us to fill in the missing dates.\n",
    "- b. The summary statistics review reveals that our data lacks consistent scaling.\n",
    "- c. The train dataset exhibits positive skewness.\n",
    "- d. There are observable seasonal patterns in the sales data, particularly during holidays and events.\n",
    "- e. We have identified outliers in the sales data, especially concerning promotions, which may need further investigation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Hypothesis Testing**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Null Hypothesis (Ho):** Holidays do not have a significant effect on the sales\n",
    "\n",
    "**Alternate Hypothesis (Ha):** Holidays have a significant effect on the sales\n",
    "\n",
    "- We will use Mann-Whiteny to test to the Hypothesis because our sales has a high positive skweness and the distribution is not normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start date is 2013-01-01 00:00:00\n",
      "End date is 2017-08-31 00:00:00\n"
     ]
    }
   ],
   "source": [
    "# Calling my start_end_date function on my oil_data1\n",
    "\n",
    "start_date, end_date = start_end_date(oil_data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date\n",
       "0 2013-01-05\n",
       "1 2013-01-06"
      ]
     },
     "execution_count": 1169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Function to find missing dates in a DataFrame\n",
    "def missing_dates(df, date_column):\n",
    "    # Ensure the date column is in datetime format\n",
    "    df[date_column] = pd.to_datetime(df[date_column])\n",
    "    \n",
    "    # Set the date column as the DataFrame index\n",
    "    df.set_index(date_column, inplace=True)\n",
    "    \n",
    "    # Create a complete date range from the start to the end of the DataFrame\n",
    "    full_range = pd.date_range(start=df.index.min(), end=df.index.max())\n",
    "    \n",
    "    # Identify missing dates by finding the difference\n",
    "    missing = full_range.difference(df.index)\n",
    "    \n",
    "    # Return the missing dates as a DataFrame\n",
    "    return pd.DataFrame(missing, columns=[date_column])\n",
    "\n",
    "# Assuming 'date' is the name of your date column in oil_df\n",
    "all_missing_dates = missing_dates(oil_data1, 'date')\n",
    "\n",
    "# Display the first 2 missing dates\n",
    "all_missing_dates.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>dcoilwtico</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-05</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-06</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-12</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-13</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-19</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date dcoilwtico\n",
       "0 2013-01-05       None\n",
       "1 2013-01-06       None\n",
       "2 2013-01-12       None\n",
       "3 2013-01-13       None\n",
       "4 2013-01-19       None"
      ]
     },
     "execution_count": 1170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the name of the date column\n",
    "date_column = 'date'\n",
    "\n",
    "# Creating a dataframe to store my missing values using pandas\n",
    "missing_df = pd.DataFrame({\n",
    "    'date': all_missing_dates[date_column].values,  # Extracting date values\n",
    "    'dcoilwtico': [None] * len(all_missing_dates)  # Creating a column with None values\n",
    "})\n",
    "\n",
    "# Display the first few rows of the DataFrame\n",
    "missing_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1171,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pc\\AppData\\Local\\Temp\\ipykernel_23676\\3126707453.py:2: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  oil_data1 = pd.concat([oil_data1, missing_df])\n"
     ]
    },
    {
     "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>dcoilwtico</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-01-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-01-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-01-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-01-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-01-19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dcoilwtico       date\n",
       "0         NaN 2013-01-05\n",
       "1         NaN 2013-01-06\n",
       "2         NaN 2013-01-12\n",
       "3         NaN 2013-01-13\n",
       "4         NaN 2013-01-19"
      ]
     },
     "execution_count": 1171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenating the original oil_df with missing_df using pandas\n",
    "oil_data1 = pd.concat([oil_data1, missing_df])\n",
    "\n",
    "# Sorting the DataFrame by 'date'\n",
    "oil_data1 = oil_data1.sort_values('date')\n",
    "\n",
    "# Displaying the first few rows of the DataFrame\n",
    "oil_data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1205</th>\n",
       "      <td>2017-08-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1206</th>\n",
       "      <td>2017-08-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1207</th>\n",
       "      <td>2017-08-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1208</th>\n",
       "      <td>2017-08-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1209</th>\n",
       "      <td>2017-08-25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1210 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date\n",
       "0    2013-01-07\n",
       "1    2013-01-08\n",
       "2    2013-01-09\n",
       "3    2013-01-10\n",
       "4    2013-01-11\n",
       "...         ...\n",
       "1205 2017-08-21\n",
       "1206 2017-08-22\n",
       "1207 2017-08-23\n",
       "1208 2017-08-24\n",
       "1209 2017-08-25\n",
       "\n",
       "[1210 rows x 1 columns]"
      ]
     },
     "execution_count": 1172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Function to find missing dates in a DataFrame\n",
    "def missing_dates(df, date_column='date'):\n",
    "    # Ensure the date column is in datetime format\n",
    "    df[date_column] = pd.to_datetime(df[date_column])\n",
    "    \n",
    "    # Create a complete date range from the start to the end of the DataFrame\n",
    "    full_range = pd.date_range(start=df[date_column].min(), end=df[date_column].max())\n",
    "    \n",
    "    # Identify missing dates by finding the difference\n",
    "    missing = full_range.difference(df.set_index(date_column).index)\n",
    "    \n",
    "    # Return the missing dates as a DataFrame\n",
    "    return pd.DataFrame(missing, columns=[date_column])\n",
    "\n",
    "# Checking again to confirm there are no more missing dates in the oil df\n",
    "all_missing_dates = missing_dates(oil_data1)\n",
    "\n",
    "# Display the missing dates\n",
    "all_missing_dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1173,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pc\\AppData\\Local\\Temp\\ipykernel_23676\\4051352787.py:2: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  oil_data1 = oil_data1.fillna(method='ffill')\n"
     ]
    }
   ],
   "source": [
    "# Forward fill missing values in the 'dcoilwtico' column\n",
    "oil_data1 = oil_data1.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1174,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pc\\AppData\\Local\\Temp\\ipykernel_23676\\158071750.py:2: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  oil_data1= oil_data1.fillna(method='bfill')\n"
     ]
    }
   ],
   "source": [
    "# Backward fill missing values in the 'dcoilwtico' column\n",
    "oil_data1= oil_data1.fillna(method='bfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dcoilwtico    0\n",
      "date          0\n",
      "dtype: int64\n",
      "Total number of null values in the DataFrame: 0\n"
     ]
    }
   ],
   "source": [
    "# Count the number of null values in each column\n",
    "null_counts = oil_data1.isnull().sum()\n",
    "\n",
    "# If you want to count the total number of null values in the entire DataFrame\n",
    "total_null_counts = oil_data1.isnull().sum().sum()\n",
    "\n",
    "# Display the counts\n",
    "print(null_counts)\n",
    "print(\"Total number of null values in the DataFrame:\", total_null_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['store_nbr', 'family', 'sales', 'onpromotion', 'year', 'month', 'day',\n",
      "       'weekday', 'quarter'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(train_data6.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1184,
   "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>store_nbr</th>\n",
       "      <th>family</th>\n",
       "      <th>sales</th>\n",
       "      <th>onpromotion</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>quarter</th>\n",
       "      <th>type</th>\n",
       "      <th>locale</th>\n",
       "      <th>locale_name</th>\n",
       "      <th>description</th>\n",
       "      <th>transferred</th>\n",
       "      <th>dcoilwtico</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>1</td>\n",
       "      <td>AUTOMOTIVE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>National</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Primer dia del ano</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>1</td>\n",
       "      <td>BABY CARE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>National</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Primer dia del ano</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            store_nbr      family  sales  onpromotion  year  month  day  \\\n",
       "date                                                                      \n",
       "2013-01-01          1  AUTOMOTIVE    0.0            0  2013      1    1   \n",
       "2013-01-01          1   BABY CARE    0.0            0  2013      1    1   \n",
       "\n",
       "            weekday  quarter     type    locale locale_name  \\\n",
       "date                                                          \n",
       "2013-01-01        1        1  Holiday  National     Ecuador   \n",
       "2013-01-01        1        1  Holiday  National     Ecuador   \n",
       "\n",
       "                   description transferred  dcoilwtico  \n",
       "date                                                    \n",
       "2013-01-01  Primer dia del ano       False         NaN  \n",
       "2013-01-01  Primer dia del ano       False         NaN  "
      ]
     },
     "execution_count": 1184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Reset the index if necessary\n",
    "train_data6.reset_index(drop=True)\n",
    "holiday_data2 = holiday_data2.reset_index(drop=True)\n",
    "oil_data1 = oil_data1.reset_index(drop=True)\n",
    "\n",
    "# Join DataFrames\n",
    "train_holiday_df = train_data6.join(holiday_data2.set_index('date'), on='date', how='left')\n",
    "train_holiday_oil_df = train_holiday_df.join(oil_data1.set_index('date'), on='date', how='left')\n",
    "\n",
    "# Display the first few rows of the merged DataFrame\n",
    "train_holiday_oil_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "train_holiday_oil_df = train_holiday_oil_df.drop_duplicates()\n",
    "train_holiday_oil_df = train_holiday_oil_df.reset_index(drop=True)\n",
    "print(train_holiday_oil_df.index.is_unique) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1194,
   "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>store_nbr</th>\n",
       "      <th>family</th>\n",
       "      <th>sales</th>\n",
       "      <th>onpromotion</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>quarter</th>\n",
       "      <th>type</th>\n",
       "      <th>locale</th>\n",
       "      <th>locale_name</th>\n",
       "      <th>description</th>\n",
       "      <th>transferred</th>\n",
       "      <th>dcoilwtico</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>AUTOMOTIVE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>National</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Primer dia del ano</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>BABY CARE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>National</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Primer dia del ano</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>BEAUTY</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>National</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Primer dia del ano</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>BEVERAGES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>National</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Primer dia del ano</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   store_nbr      family  sales  onpromotion  year  month  day  weekday  \\\n",
       "0          1  AUTOMOTIVE    0.0            0  2013      1    1        1   \n",
       "1          1   BABY CARE    0.0            0  2013      1    1        1   \n",
       "2          1      BEAUTY    0.0            0  2013      1    1        1   \n",
       "3          1   BEVERAGES    0.0            0  2013      1    1        1   \n",
       "\n",
       "   quarter     type    locale locale_name         description transferred  \\\n",
       "0        1  Holiday  National     Ecuador  Primer dia del ano       False   \n",
       "1        1  Holiday  National     Ecuador  Primer dia del ano       False   \n",
       "2        1  Holiday  National     Ecuador  Primer dia del ano       False   \n",
       "3        1  Holiday  National     Ecuador  Primer dia del ano       False   \n",
       "\n",
       "   dcoilwtico  \n",
       "0         NaN  \n",
       "1         NaN  \n",
       "2         NaN  \n",
       "3         NaN  "
      ]
     },
     "execution_count": 1194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_holiday_oil_df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1195,
   "metadata": {},
   "outputs": [],
   "source": [
    "holiday_types = ['Holiday', 'Bridge', 'Additional', 'Event']\n",
    "\n",
    "# Create a mask to filter rows where 'type' is in holiday_types\n",
    "mask = train_holiday_oil_df['type'].isin(holiday_types)\n",
    "\n",
    "# Filter data for holidays (excluding transfers)\n",
    "with_holidays = train_holiday_oil_df[mask]['sales']\n",
    "without_holidays = train_holiday_oil_df[~mask]['sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney U Test Results:\n",
      "U-statistic: 625810868438.5000\n",
      "p-value: 0.0000\n",
      "Significance level (α): 0.0500\n",
      "\n",
      "Conclusion:\n",
      "Reject the Null Hypothesis. The data suggests that holidays do not have a significant effect on the sales.\n"
     ]
    }
   ],
   "source": [
    "# Perform Mann-Whitney U test to compare sales before and after the earthquake\n",
    "u_stat, p_value = mannwhitneyu(with_holidays, without_holidays, alternative='two-sided')\n",
    "\n",
    "# Significance level\n",
    "alpha = 0.05\n",
    "\n",
    "print(\"Mann-Whitney U Test Results:\")\n",
    "print(f\"U-statistic: {u_stat:.4f}\")\n",
    "print(f\"p-value: {p_value:.4f}\")\n",
    "print(f\"Significance level (α): {alpha:.4f}\")\n",
    "\n",
    "if p_value < alpha:\n",
    "    print(\"\\nConclusion:\")\n",
    "    print(\"Reject the Null Hypothesis. The data suggests that holidays do not have a significant effect on the sales.\")\n",
    "else:\n",
    "    print(\"\\nConclusion:\")\n",
    "    print(\"Fail to reject the Null Hypothesis. The data suggests that holidays have a significant effect on the sales.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answering Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 1. Is the train dataset complete (has all the required dates)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 2. Which dates have the lowest and highest sales for each year (excluding days the store was closed)?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 3. Compare the sales for each month across the years and determine which month of which year had the highest sales.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 4. Did the earthquake impact sales?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 5. Are certain stores or groups of stores selling more products? (Cluster, city, state, type)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 6. Are sales affected by promotions, oil prices, and holidays?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 7. What analysis can we get from the date and its extractable features?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 8. Which product family and stores did the promotions affect?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 9. Does the payment of wages in the public sector on the 15th and last days of the month influence store sales?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Analysis of High and Low Stocks of Apple from Year "
   ]
  }
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