01_initial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DataSpark: Illuminating Insights for Global Electronics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "source": [
    "## Skills\n",
    "Data cleaning and processing | EDA | Python | Data management using SQL | Power BI | Tableau"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "source": [
    "### Importing all the required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import mysql.connector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data cleaning process for given data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customers csv file and data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerKey</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Name</th>\n",
       "      <th>City</th>\n",
       "      <th>State Code</th>\n",
       "      <th>State</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Country</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Birthday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>301</td>\n",
       "      <td>Female</td>\n",
       "      <td>Lilly Harding</td>\n",
       "      <td>WANDEARAH EAST</td>\n",
       "      <td>SA</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>5523</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7/3/1939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>325</td>\n",
       "      <td>Female</td>\n",
       "      <td>Madison Hull</td>\n",
       "      <td>MOUNT BUDD</td>\n",
       "      <td>WA</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>6522</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9/27/1979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>554</td>\n",
       "      <td>Female</td>\n",
       "      <td>Claire Ferres</td>\n",
       "      <td>WINJALLOK</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>3380</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5/26/1947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>786</td>\n",
       "      <td>Male</td>\n",
       "      <td>Jai Poltpalingada</td>\n",
       "      <td>MIDDLE RIVER</td>\n",
       "      <td>SA</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>5223</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9/17/1957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1042</td>\n",
       "      <td>Male</td>\n",
       "      <td>Aidan Pankhurst</td>\n",
       "      <td>TAWONGA SOUTH</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>3698</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11/19/1965</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerKey  Gender               Name            City State Code  \\\n",
       "0          301  Female      Lilly Harding  WANDEARAH EAST         SA   \n",
       "1          325  Female       Madison Hull      MOUNT BUDD         WA   \n",
       "2          554  Female      Claire Ferres       WINJALLOK        VIC   \n",
       "3          786    Male  Jai Poltpalingada    MIDDLE RIVER         SA   \n",
       "4         1042    Male    Aidan Pankhurst   TAWONGA SOUTH        VIC   \n",
       "\n",
       "               State Zip Code    Country  Continent    Birthday  \n",
       "0    South Australia     5523  Australia  Australia    7/3/1939  \n",
       "1  Western Australia     6522  Australia  Australia   9/27/1979  \n",
       "2           Victoria     3380  Australia  Australia   5/26/1947  \n",
       "3    South Australia     5223  Australia  Australia   9/17/1957  \n",
       "4           Victoria     3698  Australia  Australia  11/19/1965  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import data from csv\n",
    "\n",
    "df_cus = pd.read_csv(\"csv_data/DataSet/Customers.csv\", encoding='ISO-8859-1')\n",
    "df_cus.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15266 entries, 0 to 15265\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   CustomerKey  15266 non-null  int64 \n",
      " 1   Gender       15266 non-null  object\n",
      " 2   Name         15266 non-null  object\n",
      " 3   City         15266 non-null  object\n",
      " 4   State Code   15256 non-null  object\n",
      " 5   State        15266 non-null  object\n",
      " 6   Zip Code     15266 non-null  object\n",
      " 7   Country      15266 non-null  object\n",
      " 8   Continent    15266 non-null  object\n",
      " 9   Birthday     15266 non-null  object\n",
      "dtypes: int64(1), object(9)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df_cus.info() # check data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerKey</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Name</th>\n",
       "      <th>City</th>\n",
       "      <th>State Code</th>\n",
       "      <th>State</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Country</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Birthday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>301</td>\n",
       "      <td>Female</td>\n",
       "      <td>Lilly Harding</td>\n",
       "      <td>WANDEARAH EAST</td>\n",
       "      <td>SA</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>5523</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1939/07/03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>325</td>\n",
       "      <td>Female</td>\n",
       "      <td>Madison Hull</td>\n",
       "      <td>MOUNT BUDD</td>\n",
       "      <td>WA</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>6522</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1979/09/27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>554</td>\n",
       "      <td>Female</td>\n",
       "      <td>Claire Ferres</td>\n",
       "      <td>WINJALLOK</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>3380</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1947/05/26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>786</td>\n",
       "      <td>Male</td>\n",
       "      <td>Jai Poltpalingada</td>\n",
       "      <td>MIDDLE RIVER</td>\n",
       "      <td>SA</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>5223</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1957/09/17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1042</td>\n",
       "      <td>Male</td>\n",
       "      <td>Aidan Pankhurst</td>\n",
       "      <td>TAWONGA SOUTH</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>3698</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1965/11/19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerKey  Gender               Name            City State Code  \\\n",
       "0          301  Female      Lilly Harding  WANDEARAH EAST         SA   \n",
       "1          325  Female       Madison Hull      MOUNT BUDD         WA   \n",
       "2          554  Female      Claire Ferres       WINJALLOK        VIC   \n",
       "3          786    Male  Jai Poltpalingada    MIDDLE RIVER         SA   \n",
       "4         1042    Male    Aidan Pankhurst   TAWONGA SOUTH        VIC   \n",
       "\n",
       "               State Zip Code    Country  Continent    Birthday  \n",
       "0    South Australia     5523  Australia  Australia  1939/07/03  \n",
       "1  Western Australia     6522  Australia  Australia  1979/09/27  \n",
       "2           Victoria     3380  Australia  Australia  1947/05/26  \n",
       "3    South Australia     5223  Australia  Australia  1957/09/17  \n",
       "4           Victoria     3698  Australia  Australia  1965/11/19  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# datetime format adjustment for 'Birthday' column as YYYY/MM/DD\n",
    "\n",
    "df_cus['Birthday'] = df_cus['Birthday'].apply(lambda x: pd.to_datetime(x).strftime('%Y/%m/%d'))\n",
    "df_cus.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerKey</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Name</th>\n",
       "      <th>City</th>\n",
       "      <th>State Code</th>\n",
       "      <th>State</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Country</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Birthday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>301</td>\n",
       "      <td>Female</td>\n",
       "      <td>Lilly Harding</td>\n",
       "      <td>WANDEARAH EAST</td>\n",
       "      <td>SA</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>5523</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1939/07/03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>325</td>\n",
       "      <td>Female</td>\n",
       "      <td>Madison Hull</td>\n",
       "      <td>MOUNT BUDD</td>\n",
       "      <td>WA</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>6522</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1979/09/27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>554</td>\n",
       "      <td>Female</td>\n",
       "      <td>Claire Ferres</td>\n",
       "      <td>WINJALLOK</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>3380</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1947/05/26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>786</td>\n",
       "      <td>Male</td>\n",
       "      <td>Jai Poltpalingada</td>\n",
       "      <td>MIDDLE RIVER</td>\n",
       "      <td>SA</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>5223</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1957/09/17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1042</td>\n",
       "      <td>Male</td>\n",
       "      <td>Aidan Pankhurst</td>\n",
       "      <td>TAWONGA SOUTH</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>3698</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1965/11/19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15261</th>\n",
       "      <td>2099600</td>\n",
       "      <td>Female</td>\n",
       "      <td>Denisa Duková</td>\n",
       "      <td>Houston</td>\n",
       "      <td>TX</td>\n",
       "      <td>Texas</td>\n",
       "      <td>77017</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>1936/03/25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15262</th>\n",
       "      <td>2099618</td>\n",
       "      <td>Male</td>\n",
       "      <td>Justin Solórzano</td>\n",
       "      <td>Mclean</td>\n",
       "      <td>VA</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>22101</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>1992/02/16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15263</th>\n",
       "      <td>2099758</td>\n",
       "      <td>Male</td>\n",
       "      <td>Svend Petrussen</td>\n",
       "      <td>Wilmington</td>\n",
       "      <td>NC</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>28405</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>1937/11/09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15264</th>\n",
       "      <td>2099862</td>\n",
       "      <td>Female</td>\n",
       "      <td>Lorenza Rush</td>\n",
       "      <td>Riverside</td>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "      <td>92501</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>1937/10/12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15265</th>\n",
       "      <td>2099937</td>\n",
       "      <td>Male</td>\n",
       "      <td>Zygmunt Kaminski</td>\n",
       "      <td>Bloomfield Township</td>\n",
       "      <td>MI</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>48302</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>1965/08/18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15256 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CustomerKey  Gender               Name                 City State Code  \\\n",
       "0              301  Female      Lilly Harding       WANDEARAH EAST         SA   \n",
       "1              325  Female       Madison Hull           MOUNT BUDD         WA   \n",
       "2              554  Female      Claire Ferres            WINJALLOK        VIC   \n",
       "3              786    Male  Jai Poltpalingada         MIDDLE RIVER         SA   \n",
       "4             1042    Male    Aidan Pankhurst        TAWONGA SOUTH        VIC   \n",
       "...            ...     ...                ...                  ...        ...   \n",
       "15261      2099600  Female     Denisa Duková              Houston         TX   \n",
       "15262      2099618    Male   Justin Solórzano               Mclean         VA   \n",
       "15263      2099758    Male    Svend Petrussen           Wilmington         NC   \n",
       "15264      2099862  Female       Lorenza Rush            Riverside         CA   \n",
       "15265      2099937    Male   Zygmunt Kaminski  Bloomfield Township         MI   \n",
       "\n",
       "                   State Zip Code        Country      Continent    Birthday  \n",
       "0        South Australia     5523      Australia      Australia  1939/07/03  \n",
       "1      Western Australia     6522      Australia      Australia  1979/09/27  \n",
       "2               Victoria     3380      Australia      Australia  1947/05/26  \n",
       "3        South Australia     5223      Australia      Australia  1957/09/17  \n",
       "4               Victoria     3698      Australia      Australia  1965/11/19  \n",
       "...                  ...      ...            ...            ...         ...  \n",
       "15261              Texas    77017  United States  North America  1936/03/25  \n",
       "15262           Virginia    22101  United States  North America  1992/02/16  \n",
       "15263     North Carolina    28405  United States  North America  1937/11/09  \n",
       "15264         California    92501  United States  North America  1937/10/12  \n",
       "15265           Michigan    48302  United States  North America  1965/08/18  \n",
       "\n",
       "[15256 rows x 10 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter out missing values\n",
    "df_cus = df_cus.dropna()\n",
    "df_cus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerKey    0\n",
       "Gender         0\n",
       "Name           0\n",
       "City           0\n",
       "State Code     0\n",
       "State          0\n",
       "Zip Code       0\n",
       "Country        0\n",
       "Continent      0\n",
       "Birthday       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cus.isnull().sum() # check for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Counts as per Country')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# country count visualization\n",
    "plt.figure(figsize=(12,5))\n",
    "sns.countplot(x=df_cus[\"Country\"], data=df_cus, hue ='Country')\n",
    "plt.xlabel(\"Country\")\n",
    "plt.ylabel(\"Counts\")\n",
    "plt.title(\"Counts as per Country\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rPl9SUhK++uorxMfHQ6fjfwMiV+BGefIIWq0W6enpSE9Ph1ardbovPDwckZGReOedd3D06FH88ssveOKJJ675fNOnT0dhYSHuu+8+bNu2DceOHcPatWsxefJkWK3W+nwrRKrFQiGPERISgpCQkEq3azQarFixAmlpaejUqRMef/xxvPLKK9d8rri4OGzatAlWqxVDhgxBp06d8NhjjyE0NBQaDf9bENWGIoQQskMQEZHn41cxIiJyCRYKERG5BAuFiIhcgoVCREQuwUIhIiKXYKEQEZFLsFCIiMglWChEROQSLBQiInIJjopHdBUmiw1Wm4DFZoPNBmi1Cny0Gvjo+D2MqCosFFI1IQTOXTTidFE5cooMKCwzoajUhPNlZhSVmVBUbsb5MhOKyswoMVpgMFthtNhgttpwtUGJFAXQa+zF4qPTwEergZ9eg9AAH4QH6BER4IOwAB9EBOov/fRBTIgvmoYHIDrYF4qiNOyHQNRAOJYXeTyL1Ybj+aXIyCtBVkEpTp8vx+nzZThzvhxnisphtFR9GmAZfHQaNA3zR5NwfzSLCEDTcH80jwhA2+hgtG4UCJ2WSz/kuVgo5FGyi8pxOPciDuVexOHcCzicV4LMcyUwuVFp1JaPVoNWjQKRGBuChMbBaN84GO0bh6BxqJ/saETVwkIht2W0WLH/TDHSTpxH2onz2HmyCOcuGmXHanBRQb5Iah6G5PhwdG8Rgc5NQrkdh9wSC4XcRqnRgs2ZBdh2vABpJ85jf/YFVSx5uJqvToPOTULRPT4cPVpE4MbWkQjy5eZQko+FQtIIIXAw5wJ+zTiH3zLOIe3EeZit/HOsKb1WQVLzcPRPaIT+7RqhY1yo7EjkpVgo1KBKjBb8cugsNhw+i41H8r1yFVZ9iw72xc3t7OXSP6ERQvz0siORl2ChUL0rMVrw88E8fL8vB79lnHOrva7Uzkenwc1tozC8SxwGd4jhqjGqVywUqhclRgvWpedh1V6WiLvw1WnQv10jDL8hDoMToxHgw3Ih12KhkMsIIbA5swArtp/C2gO5LBE35qfXYHBiDO5OboZ+baKg0fBgS6o7FgrVWd4FA77YcQqf7ziNk4VlsuNQDcWF+mF096YY06MZmoYHyI5DHoyFQrVitQmsS8/DZ9tPYUPGOVht/DPydBoFuLldI9zfszkGJcZAy6UWqiEWCtVIcbkZy7edxLItJ3CmqFx2HKoncaF+mNgnHvf1bI5g7iVG1cRCoWo5WVCG934/hi/STqPMZJUdhxpIsK8O9/Zshsl9WyI21F92HHJzLBS6pr2ni7Dk12P48UAuV2t5Mb1WQUrnWPzl5lY8cJKuioVCVdp18jxe/ykDG4/ky45CbqZf2yg8fms7JDUPlx2F3AwLhZzsP1OM13/KwC+HzsqOQm7uloRGmHlbAjo14RIL2bFQCABwKPcCXl+bgbUH82RHIQ+iKMBtHWIw87YEtIsJlh2HJGOheLms/FK8svYwVu/LueoZComuR6MAw7vE4Ylb2yE+KlB2HJKEheKlLhrMePOXo1i6KQsmK49oJ9fw0WowsU88/jqwDXc39kIsFC9jswl8vuMUXl2bgfwSjvRL9SMqyBdPDmmHu7s347AuXoSF4kW2HS/EvO8O4ED2BdlRyEt0bhKK51M7IDk+QnYUagAsFC9w7qIR8747gFV7c2RHIS91xw1xmJOSiOgQP9lRqB6xUFTu8x2n8ML36SguN8uOQl4uxE+HZ1IScU+P5rKjUD1hoajUyYIyzP56LzYdLZAdhchJnzaReHFkFzSP5MjGasNCURmrTeD934/j9Z8yUG7mmFvknvz1Wsy8rR0m92nJjfYqwkJRkSN5FzHziz3Ye7pYdhSiaunaLAyvjO6CtjwoUhVYKCqxbEsWXlidDoOZx5SQZ/HVafBMSiIm9I6XHYXqiIXi4QpLTXjqyz34OZ1jb5FnG5wYjZdH34CIQB/ZUaiWWCge7LeMc5j5xR6cu8gDFEkdooN98fqYrujbNkp2FKoFFooHMllsWPjjIby/6TjH3yLVURTgoX6tMGtIAvRajew4VAMsFA+TU1yOhz/eiT2nimRHIapXNzQNxdvjuiMujGeK9BQsFA/yx7ECTP90J/JLTLKjEDWIqCAfvHl/Em5sFSk7ClUDC8VDvP/7cSxYnQ4LT8NLXkanUfBMSiIm9WkpOwpdBwvFzRnMVjz91V58sztbdhQiqUYlNcGCkZ3hp9fKjkJXwUJxY6fPl2HqsjSODkx0SecmoVg8vjuacLuKW2KhuKn9Z4ox6YPt3CWY6ApRQb54f2IyujQNkx2FrsBCcUPrD5/F9E92oszEsbiIqhLgo8WisUm4JSFadhS6DAvFzSzfdhLPfrOfG9+JrkOnUTB/RCfc25PD4bsLFoobeXXNYby5/qjsGEQe5dGBbfDEbQmyYxBYKG7BbLXhf77ai5U7z8iOQuSRRndvipdGdYaOR9ZLxUKRzGSxYdonO/Fzep7sKEQebWD7aLw9Lgm+Ou5WLAsLRSKD2Yqpy9Lwa8Y52VGIVKFvmyi8OyEZ/j4sFRlYKJKUm6x48KPtPEUvkYv1iA/H0kk9EeSrkx3F67BQJCgxWjB56XZsyyqUHYVIlZKah+HDyT0R7KeXHcWrsFAa2AWDGQ+8vw27ThbJjkKkal2bheGjKT0RwlJpMNwlogGVGC0Y/x7LhKgh7D5VhPHvbUOJ0SI7itdgoTQQg9mKKR9s53lMiBrQnlNFeOijHTBaOOpEQ2ChNACz1YZHPk7DH8e5zYSooW3OLMBfP90FK0efqHcslHpmswk88fkerD/MXYOJZFl7MA9Pf7UX3GRcv1go9Wzudwfw3R6ey4RIti/STuOF79Nlx1A1Fko9+r+fj+CjLSdkxyCiS/7z+3Es4nh59YaFUk9W7jyNf/6cITsGEV3hlTWH8WXaadkxVImFUg+2ZxXi6a/2yY5BRFfx95X7sJ0HFrscC8XFThXaT9trstpkRyGiqzBZbZi6LA2nCstkR1EVFooLXTSYMfmD7SgsNcmOQkTXUVhqwpQPt+OiwSw7impw6BUXsdoEJn2wHb9x5GC3VPT7JyjetNzpNk1gGJrN+BgAUHZ4My7u/gGmvEzYyi8gduK/4BPTymn6wnXvonT/Oih6f4QPmIjADv0d95Wmb0TpgV8QPfr5+n8z5FIDEhrhvQd6QKtRZEfxeByO00X+seogy8TN6aOaI+aeF/68QfPnArrNbIBv0w4IaN8XhT/+u9Jjy47+gdL0XxE95h+wnM9GwQ//B7+W3aD1D4HNUIKijR8h5t4XKj2O3N+Gw+fwwvfpeC61g+woHo+F4gLf7j6DDzZnyY5B16PRQhsUXuVdQZ0GAgAsxVWf6MxccAp+zTrDN7YtfGPbonDdu7AU5ULrH4LzG5YiuFsKdCHR9Rad6tf7m46jS9NQjOjWRHYUj8ZCqaOjZ0vw95Xco8sTWM5n4/SiCYBWD9/Ydgjr/wD0YY2r9VifRi1RsnsNrIYSWIpyISxG6MLjYDh9AKa8TETcNq2e01N9+/vX+9CpSQjaRAfLjuKxuA2lDspNVty56Hdk5JXIjkLXUZ65AzaLEfqIJrCWFqF48wqYC08jbspb0PqHOKazFOfhzOIpVW5DKfr9E5Qe2ABF54OwfmPh37oHcj74GyJTHofxTDou7lwFrX8IIobMgE+jFg39FskF2sUE4dvpfXnGx1riXl518MzX+1gmHsK/dTICE/rAp1E8/OO7Inr0XABA6b511X6OsL5j0WTqu4ibsggB7W5C8ZbP4RffFYpGi+Itn6Hx2JcR1OU2FHz/ej29C6pvGXklmPPNftkxPBYLpZaWbzuJlbvOyI5BtaTx8YNPVDzM52s3zpq54BRKD25AWL9xMJzcB7+mnaANCEVA+372PcWMPL7BU3218zQ+335KdgyPxEKphYPZFzD3vwdkx6A6EBYzzAWnoA2KqPljhUDBj28i/JYHofHxB4QNwnbpJE4VPwUPbPVkz/13Pw7lXpAdw+OwUGrIaLHib5/tgtHCGYYnOf/LezCc3AdzUS6M2Ydx7psFsJnKENRpEADAWn4RprxjMOefBACYC0/DlHcM1pLzlZ6rZM8a+9JI214AAN8miTCc2AvjmUO4sP1b6CObQ+MX1HBvjlzOYLZhxqe7YDDzxFw1wY3yNbRgdTre+e2Y7BhUQ+e+XQjj6QOwll2ANiAEvnHtEdpvHHyimgMASvb9jILVb1R6XGif+xDWd6zjurX0PHI+monG416BLjjScXvRpuW4uOO/0ASEIirlcfjGJdT7e6L695d+LfFMCo9PqS4WSg1sO16Ie9/ZAp74jcg7aBRgxUO90bNlzVeNeiOu8qqmUqMFM7/YzTIh8iI2Acz6Yg/KTBbZUTwCC6Wa5n+fjlOF5bJjEFEDO1lYxjM9VhMLpRrWHz6L5dtOyo5BRJJ88sdJjtVXDSyU6yg1Wji0ChHhf77aixIjV31dCwvlOl7/KQM5xQbZMYhIspxiA15fy9N6XwsL5RoOZl/gKMJE5PDhliwczOYBj1fDQrkKIQTmfLMPVu7WRUSXWG32+QKPtqgaC+Uqlm87hZ0ni2THICI3s/NkET7jWF9VYqFUoaDEiIU/HpIdg4jc1MIfD+F8qUl2DLfDQqnCiz8cQnG5WXYMInJT58vMePEHHptyJRbKFQ5kF+OrnadlxyAiN/dF2mnsP1MsO4ZbYaFc4cXVh8DtbUR0PUIAL/3AVeOXY6Fc5teMc/j9aL7sGETkIX4/ms8j6C/DQrnEZhN4cTXXiRJRzbz0wyHuRnwJC+WSr3aexqHci7JjEJGHOZhzAV/zdOAAWCgAAIPZitd/4pAKRFQ7r63NgNHCszuyUAB8tCWL43URUa2dKSrHR5tPyI4hndcXisFsxTu/HZcdg4g83JLfMr3+HPReXygrtp1EfolRdgwi8nD5JSas8PLzJnl1oZgsNrzz2zHZMYhIJd757RjMVpvsGNJ4daF8tfM0srnthIhcJLvYgK/SvHekDa8tFKtN4O0NmbJjEJHKvP1rptee9sJrC+Xb3WdwsrBMdgwiUpkTBWVYtTdbdgwpvLZQFv/KpRMiqh+L1h/1yqPnvbJQNh/NR0ZeiewYRKRSGXkl2HjE+8YF9MpC4Xniiai+eeN8xusK5UxROdYdOis7BhGp3PrDZ5GVXyo7RoPyukJZtuWE1+6BQUQNRwjgoy3eNRyLVxWKwWzFZ9u9+0hWImo4X6adQrnJe4Zj8apC+e/ubJwv47niiahhXDBY8N0e79mF2KsK5eM/vGvxk4jk86b5jtcUyuHci9h7ulh2DCLyMntPFyMjzztO3uc1hfLFjlOyIxCRl1q50zvO6OgVhWKx2vDNbu9Zj0lE7uXb3Wdg84K9S72iUDYeyec5T4hImpxiA7YeK5Ado955RaF8vcs7FjeJyH2t9IL5kOoLpdRowU8H82THICIv9+P+XNWfIlj1hfLTwTyUq/yXSETur8RowZoDubJj1CvVF8qP+9X9CyQiz7F6X47sCPVK1YViMFvx25FzsmMQEQGw7yCk5tVeqi6UTUfzUeZF4+gQkXsrM1mx6ah6z5Oi6kJZe4Ab44nIvah5JyHVForNJrDukHp/cUTkmX5OP6va0wOrtlDSTp5HfolJdgwiIif5JUbsOlUkO0a9UG2hqHmxkog8m1rnT6otlN8yuHcXEbmnX9LVeRpyVRZKQYkRh71kuGgi8jwZZy+iQIXjC6qyULYcK4BKt3kRkQoIAWw9Vig7hsupslA2Z6p/VE8i8mybM9V3PIoqC2ULC4WI3NwWFQ5nr7pCySkux/H8UtkxiIiu6di5Upy9YJAdw6VUVyhcOiEiT6G2pRTVFcq24+rb0EVE6qS2L8CqK5TdKj0ClYjUR23zK1UVisFsxdGzJbJjEBFVy5GzJaoazl5VhXIguxgWGw9AISLPYLUJHMi+IDuGy6iqUPaeLpYdgYioRvadLpIdwWVYKEREEu09o575lsoKpUh2BCKiGtmnoi/CqimUEqMFx3hAIxF5mMxzJSgzWWTHcAnVFMqRvIscEJKIPI5NAIdy1TE6umoK5dg5Lp0QkWdSy/xLPYWSz+NPiMgzHVfJ/Es9haKShici76OW+ZdqCiXznDoanoi8j1pGSFdFodhsAlkFZbJjEBHVyvH8UggV7FWkikI5fb4cJotNdgwioloxWmw4U1QuO0adqaJQsgrUsbhIRN5LDdtRVFEoucXqOusZEXmfnGIuobiFXJWdRpOIvE/eBaPsCHWmikLJY6EQkYdTw3xMJYXi+c1ORN5NDfMxlRSK5zc7EXm3cxc9fz7GQiEicgNcQnEDVptAQalJdgwiojrJLzHC5uGnMPf4QikuN8Pq4b8EIiKLTeB8mWd/Ofb4QikxqOPENEREJUbPnp95fqF4+C+AiKiCp8/PWChERG6izGSVHaFOVFAoZtkRiIhcwtO/IKugUDy70YmIKpSyUOTiRnkiUgsWimRlJs/+BRARVSj18DUuHl8oFh6DQkQqYbCwUKRSwVkziYgAeP78zOMLhYhILTx96BWd7AB1JeDZvwByf3qNQJjOAo3CvzWqX3rFs7cJe36h8P84XcZfa0WU3owoHzPC9WZE6EwI05kRqjUiRGtCsMaIIMWIIMWAABjgDwP8hAG+tnL42sqgt5ZDZy2DzlIGjbkMirkUisXzT81KHkL7DIBE2SlqzeMLhTxXiM6CKB8zIvVmROjNCNebEK4zIlRjQojGiCCtfeYfCCMCUO408/exlkF3aeavtZRBYy6FYiqFYrt0oKvp0oXIk2i0shPUCQuFrktRBKL0ZkT6WBCpNyFcZ5/5h2lNCNEaEaIxIVhjQKBiROClb/3+wgBfUW6f+dvKobeUQWstg9ZcBo2lFDCVQRFWwAbAeOlC5O00nj1L9uz0ABRFdgL34quxIfKyb/0RejPCtEaE6cwI0RgRfOnivMqnHH6XZvw+tnLoLPZv/VpLKRRzGWAuhwIBWGC/EFH9YKHI5avz3EXEIJ0FjfRmROgtiPSxr+sP0xkRqjVdmvmbEKTYv/lfPvO3r+8vh95aBp2lHFpLKTSWMvsqH+ulr/rmSxci8hwsFLn89Q1TKBGXNvTa1/WbEa6zr/IJ1ZoQrDUiWDEiSGNAIIzwh/3bv5+tzD7jd8z8/1zfD3MZFJsFEOD6fiKy4zYUufx9nA+l0Wsq1vebEKk3I0xvRpj20vp+nREhihFBl/b0CaiY8Qv7N3+fyzf2Wuzr+u0z/3IowgZYYb/wFPZEVB80etkJ6sTjC2VIQAaORj9t38vHXArFcmluz1U+RORpdL6yE9SJxxdKgI8euHBSdgwioroLbCQ7QZ14/tArfqGyExARuUZwY9kJ6sTzC8U/THYCIiLXCGKhyMUlFCJSA40eCIiQnaJOPL9QfEMAxfPfBhF5uaAYjz9S2/PnxIoC+AbLTkFEVDfBMbIT1JnnFwoA+IfLTkBEVDcevv0EUEuhhDWXnYCIqG64hOImItvITkBEVDdcQnETEa1lJyAiqhsuobiJSBYKEXk4LqG4CS6hEJGn8/Cj5AG1FEp4PKB49rDPROTlWChuQucDhDWTnYKIqHYUjccPDAmopVAArvYiIs8VHOfxJ9cC1FQo3DBPRJ4qtovsBC6hnkLhEgoRearYG2QncAn1FAoPbiQiTxXbVXYCl1BRobSSnYCIqHa4hOJmwlrYzydARORJgmKAkFjZKVxCPYWi0QLhLWSnICKqGZUsnQBqKhQAiEqQnYCIqGZYKG6q+Y2yExAR1YxKNsgDaiuU+D6yExAR1QyXUNxUbFfAh6cDJiIPERCpqmGj1FUoGi3QvJfsFERE1aOipRNAbYUCAC242ouIPISKtp8AaiyU+L6yExARVQ+XUNxcXDdAHyA7BRHR9cV1lZ3ApdRXKFo90Kyn7BRERNcW2tx+ckAVUV+hAEALrvYiIjfXbojsBC6nzkLhdhQicncJQ2UncDl1FkqT7oDOX3YKIqKq+QQB8f1kp3A5dRaKzgdomiw7BRFR1VoNAHS+slO4nDoLBeBqLyJyX+3Ut7oLUHOh8ABHInJLiio3yANqLpTmNwL+EbJTEBE5a5IEBEXLTlEv1FsoWj3Q4U7ZKYiInLW7XXaCeqPeQgGAznfLTkBE5EyFuwtXUHehtLgJCGkqOwURkV1IU6BxZ9kp6o26C0VRgE4jZacgIrJT6cb4CuouFICrvYjIfah0d+EK6i+U2BuAqATZKYjI2+kDgJY3y05Rr9RfKADQebTsBETk7RJuB/R+slPUKxYKEVFDSJ4sO0G9845CiWhlHzCSiEiGRu29Yjgo7ygUgBvniUie7pNkJ2gQ3lMoHUcBilZ2CiLyNvoA4IZ7ZadoEN5TKMExQEv1nX+AiNxcp1GAf5jsFA3CewoF4GovImp4XrAxvoJ3FUrHUUBApOwUROQtYrt61Q5B3lUoPgFAj7/ITkFE3iLZOzbGV/CuQgGAXlPtG8mIiOqTb4jXrWb3vkIJiAC6jZOdgojUrss9gE+g7BQNyvsKBQB6zwA0OtkpiEjNvGhjfAXvLJTwFkBHDmtPRPWkeW8gpoPsFA3OOwsFAPo8JjsBEalV8hTZCaTw3kJp3BloPUh2CiJSm4jWXrsGxHsLBeBSChG53i1/B7TeuY3WuwulVX8grpvsFESkFjGdgE53yU4hjXcXCsClFCJynYFzAEWRnUIaFkrinfbzpRAR1UWzXvazMnoxFopGA9z0V9kpiMjTDXpOdgLpWCgAcMP9QGgz2SmIyFO1usUrzsh4PSwUAND7Abf+r+wUROSpuHQCgIXyp06jgHiegIuIaigxFWiSJDuFW2ChXO72hTxNMBFVn6IBBj4rO4XbYKFcLqajVw7oRkS11OUeoFGC7BRug4VypYHPAP4RslMQkbvT+gADZstO4VZYKFfyD7cfnEREdC1JD9hHLicHFkpVuk+yDx5JRFSVwEb2MbvICQulKhoNcPsrslMQkbsa9or97K/khIVyNS16A51Gy05BRO4mMdVrh6e/HhbKtdz2D0DvXeeEJqJr8AsDhr0mO4XbYqFcS0gc0O8J2SmIyF0MfREIjpGdwm2xUK7npr8C4S1lpyAi2drcCnS9X3YKt8ZCuR6dL5D6f/YjYonIO/kEA6lvyE7h9jiXrI5W/XkiLiJvdus8ILSp7BRuj4VSXbfMAZp0l52CiBpai74ckqmaWCjVpdUBd71nX/Ql1XlxoxHKvAv4248Gx215JTZM/KYcca9dRMALFzD041IcKbA6Pe6JNQZELLyA5v+8iBX7zU73fX7AjNTlZQ2Sn+qJPgC4899efVrfmmCh1ERESyCFuwyqzfYzVryz04QuMX/+dxBCYMRn5Th23oZv7w3ArqmBaBGqweBlZSg1CQDAd4fN+HSfGWvHB2LhYD9M+rYcBWU2AECRQeCZX4xYNMxPynsiF7nlGZ4ivAZYKDV1wz32EUZJFUpMAmNXluPdVH+E+/35LfRIoQ1bT1vxdoofejTRIiFKi7dS/FBiApZfWhJJz7dhQLwWyXFa3NdZjxBfBcfO28vmqZ8MmJasR/NQ/hfzWE2SgRunyU7hUfjXXhspr3FXYpWYvtqAlLY6DG6lc7rdaLH/9NP9WTJajQIfLfD7SftqrxtitNiRbcX5coG0bCvKzQJtIjT4/aQFO3OseLSXT4O9D3IxnyBgxFv2YZio2vhp1YZvMDD6PUCjl52E6mDFfjN25ljx4mDfSve1j9KgRaiC2esMOF8uYLIKvPS7EbklAjkl9tVaQ9roMK6LHj3eLcHEb8vx4Qh/BPoAj3xvwJLh/nh7hxkJb5agz/ulOHDWWuk1yI2NeIvnOakFFkptNeluP3cKeaRTxTY89qMBH4/0d1oKqaDXKvhqTAAyCmyIePkiAl64iA1ZFtzeRgftZRto5w7ww9FHg7HvkSCMTNRjwUYjBrfUQa8F5v9mxO+TAvBgNz0mfFPekG+P6qLfTKDDnbJTeCRFCCFkh/BYQgAf3Qkc/1V2Eqqhbw6ZMfKzcmgv6xKrABQAGgUwzgmGVmO/s9hgX0JpFKhBr/+UIDlWi0Up/pWe81C+FanLy7FraiDe32XC7yet+PzuAJSaBIJevIjip4MR4su9hdxa29uA+z7jqq5a0l1/EroqRQFGvQO8fRNQViA7DdXAoJY67HvEeeDPSd+Wo32UFv/Tx8dRJgAQ6qcAUHCkwIod2Tb845bKe24JIfDQdwa8dpsvgnwUWG2A2b5mzPHTxq9u7i2iNTDqXZZJHbBQ6iq4MXDnW8By7vnlSYJ9FXSK1jrdFqhXEOn/5+1fHDCjUaCC5qEa7Muz4rEfDRjRXofbWlf+b/PuTjOiAxXckWDfrtanuQ5zfzVi62kLfjhiQYdGGoT5cenEbfkEA/d+CviHyU7i0VgorpAwFOg3C9j4quwk5EI5JTY8sdaEvBKB2GAFE7ro8Wz/yhvw80psWLDRiM1T/lzi6dlEi5m9fZHyaTmiAxV8OKLyKjJyFwow8m0gur3sIB6P21Bc6euHgT3LZacgopq4+SnuYOMiLBRXspqBT0YDxzbITkJE1dFuKHDfCg6t4iLc+uRKWj0wZhkQ00l2EiK6nsi29p1qWCYuw0JxNb8QYOwXQAiHuiZyW74h9o3wfqGyk6gKC6U+hMQB477kHyuRW1KAkUuARu1kB1EdFkp9iU4E7vkE0HI8JyK3MmQB0H6Y7BSqxEKpTy37ASPehv34ayKSbsDfgd4cQbi+sFDqW+fRwOC5slMQUe8ZwID/kZ1C1VgoDaHv34CeD8lOQeS9kh4AhrwgO4Xq8TiUhmKzAZ+PBw6tkp2EyLt0Gs0xuhoIP+GGotEAd/0HiO8nOwmR90hIse/RxTJpEPyUG5Le336MSutBspMQqV/74cCYDwEthyxsKCyUhqb3tw/1kJAiOwmReiWmAnd/YB+9ghoMC0UGnQ8w5iOg40jZSYjUp8OdwOgPWCYSsFBk0eqAu94DbrhfdhIi9eg4Erjrfa7mkoSFIpNGC4x4C0ieIjsJkefrdJf9SxrLRBoWimyKAgx/HRgwW3YSIs/V5zF7mWi015+W6g2PQ3EnO94Hvp8FCKvsJESeQesDDH8D6DZWdhICC8X9pK8CvpoCWAyykxC5N/8I4J6Pgfg+spPQJSwUd3RiM7D8XsBQLDsJkXuKagfc/xkQ0Up2EroMC8Vd5R0EPr0HKD4pOwmRe2l1i/0YE/8w2UnoCiwUd1ZWCHw9FTiyVnYSIveQPAW4/WXuyeWmWCjuTghg46vA+he5sZ68l6K1nxjrxodlJ6FrYKF4imO/Al89CJSelZ2EqGH5hgCjlwJtB8tOQtfBQvEkF3OBLyYBJzfLTkLUMMKaA/d/bj+lNrk9FoqnsVqAdfOAzf8GwF8dqVirAfaDFQOjZCehamKheKpDq4FvHuauxaQ+On/g1nn2s5wqiuw0VAMsFE92Pgv4fAKQs0d2EiLXaNLdfkKsqLayk1AtsFA8ncUI/PAUkPaB7CREtafRA/2fAvrN5HhcHoyFohZ7VtjHATNdlJ2EqGYatQdGLgbiuslOQnXEQlGTC9nAD/8DpP9XdhKialCAG6cBg54D9H6yw5ALsFDUKGMNsHoWUMRhW8hNhTa3nwuoZT/ZSciFWChqZSoDfl0IbHkTsFlkpyH6U9dxwNAXAb8Q2UnIxVgoapd3EFj1N+DUH7KTkLcLbASk/h/QPkV2EqonLBRvIIR9L7Cf5wKGIslhyOvo/IBeDwP9ngD8QmWnoXrEQvEmJeeANX8H9n0uOwl5BQXofLd9o3tYM9lhqAGwULxR5nrg+5lAYabsJKRWLfoCQ+ZzV2Avw0LxVhYj8Psb9o32xguy05BaRLUDBs8D2g+TnYQkYKF4u/LzwNa3ga2LASPHBaNaCogCBjwNdJ/Ek195MRYK2ZUXAX8sBra+xQEnqfp0/sCNjwB9H+duwMRCoSsYLgB/LAG2LrIvvRBVSQG63AMMehYIbSo7DLkJFgpVzXgR2PYOsPlNoLxQdhpyFzp/oMsY+1IJT3pFV2Ch0LUZS4Dt79pP6FVWIDsNyRIcB/R80L6NJCBCdhpyUywUqh5TKbD9PWDzv4DSc7LTUENp2sN+UGKHEdzYTtfFQqGaMZUBB78Bdn0MnNgkOw3VB40e6HCnfbVW02TZaciDsFCo9gqPAbs+AfYsBy6ckZ2G6iogEug+EejxFyAkVnYa8kAsFKo7mw04tt6+1HLoe8BqlJ2IaiK6I3Djw0DnMTwvCdUJC4Vcq/w8sO9LYPcnQPYu2WnoaqLaAYl3AB3uAGJvkJ2GVIKFQvUn74B9ldjez4CyfNlpKKbTnyXCXX6pHrBQqP5ZzfazSGb8YB+YkttbGk5ckr1AEu8AIlvLTkMqx0Khhnf2EJD5i/1yYhNgLpOdSEUUoFmvSyWSCoQ1lx2IvAgLheSyGIGTW/8smNx9APgnWSOBjYCmPYHWt9hLJLix7ETkpVgo5F5Kztn3GMv8xb56rCRXdiL3otHZt4U062kvkWY9gPB42amIALBQyN3lHQBObQPOHgTOptt/etMQMEEx9qPVm/awl0hcN0DvLzsVUZVYKOR5Ss7aiyXv4J9Fc+4QYCqRnaxuAqKAiJZAk+5/lkh4C9mpiKqNhULqIARQdPLPpZizB+0lczHPfmyMzSw3n6KxD7AY1gwIbXbFz+b2IeB9AuRmJKojFgp5B8MF+zD8ZYWXfp6/4voVP8uLAGGzF4GiXPp56QIF0PrYVz3p/QF9gL0M9AH26z6BlcsjpAmg1cv+FIjqFQuFiIhcQiM7ABERqQMLhYiIXIKFQkRELsFCISIil2ChEBGRS7BQiIjIJVgoRETkEiwUIiJyCRYKkcrEx8fjjTfekB2DvBALhagOJk6cCEVRKl2OHj0qOxpRg9PJDkDk6YYOHYqlS5c63daoUSNJaYjk4RIKUR35+vqicePGThetVovvvvsO3bt3h5+fH1q1aoV58+bBYrE4HqcoCpYsWYLhw4cjICAAiYmJ2LJlC44ePYoBAwYgMDAQvXv3RmZmpuMxmZmZuPPOOxETE4OgoCD06NEDP//88zXzFRcX46GHHkJ0dDRCQkIwcOBA7Nmzp94+D/JeLBSierBmzRqMGzcOjz76KA4ePIglS5bggw8+wAsvvOA03T/+8Q9MmDABu3fvRvv27XH//fdj6tSpmD17Nnbs2AEAmDFjhmP6kpISDBs2DD///DN27dqFIUOGIDU1FSdPnqwyhxACKSkpyM3NxerVq5GWloakpCQMGjQIhYWF9fcBkHcSRFRrDzzwgNBqtSIwMNBxGT16tOjXr59YsGCB07TLli0TsbGxjusAxJw5cxzXt2zZIgCI9957z3Hb8uXLhZ+f3zUzdOjQQfz73/92XG/RooX45z//KYQQYt26dSIkJEQYDAanx7Ru3VosWbKkxu+X6Fq4DYWojm655Ra8/fbbjuuBgYFo06YNtm/f7rREYrVaYTAYUFZWhoAA+8m0unTp4rg/JiYGANC5c2en2wwGAy5cuICQkBCUlpZi3rx5WLVqFbKzs2GxWFBeXn7VJZS0tDSUlJQgMjLS6fby8nKnVWlErsBCIaqjigK5nM1mw7x58zBq1KhK0/v5+Tn+rdf/edItRVGuepvNZgMAPPnkk1izZg1effVVtGnTBv7+/hg9ejRMJlOV2Ww2G2JjY7Fhw4ZK94WFhVXvDRJVEwuFqB4kJSXh8OHDlYqmrjZu3IiJEydi5MiRAOzbVLKysq6ZIzc3FzqdDvHx8S7NQnQlFgpRPXjuuecwfPhwNGvWDHfffTc0Gg327t2Lffv2Yf78+bV+3jZt2mDlypVITU2Foih49tlnHUsvVRk8eDB69+6NESNGYOHChUhISEB2djZWr16NESNGIDk5udZZiK7EvbyI6sGQIUOwatUq/PTTT+jRowduvPFGvP7662jRokWdnvef//wnwsPDcdNNNyE1NRVDhgxBUlLSVadXFAWrV6/GzTffjMmTJ6Ndu3a49957kZWV5dhmQ+QqPKc8ERG5BJdQiIjIJVgoRETkEiwUIiJyCRYKERG5BAuFiIhcgoVCREQuwUIhIiKXYKEQEZFLsFCIiMglWChEROQSLBQiInIJFgoREbkEC4WIiFyChUJERC7BQiEiIpdgoRARkUuwUIiIyCVYKERE5BIsFCIicgkWChERuQQLhYiIXIKFQkRELsFCISIil2ChEBGRS7BQiFxkwIAB+Nvf/iY7BpE0LBRSldzcXDz22GNo06YN/Pz8EBMTg759+2Lx4sUoKyuTHY9I1XSyAxC5yrFjx9CnTx+EhYVhwYIF6Ny5MywWCzIyMvD+++8jLi4Od9xxh+yYV2W1WqEoCjQafs8jz8S/XFKNadOmQafTYceOHRgzZgwSExPRuXNn3HXXXfj++++RmpoKACguLsZDDz2E6OhohISEYODAgdizZ4/jeebOnYuuXbti2bJliI+PR2hoKO69915cvHjRMU1paSkmTJiAoKAgxMbG4rXXXquUx2Qy4amnnkKTJk0QGBiIXr16YcOGDY77P/jgA4SFhWHVqlXo0KEDfH19ceLEifr7gIjqGQuFVKGgoABr167F9OnTERgYWOU0iqJACIGUlBTk5uZi9erVSEtLQ1JSEgYNGoTCwkLHtJmZmfjmm2+watUqrFq1Cr/++iteeuklx/1PPvkk1q9fj6+//hpr167Fhg0bkJaW5vR6kyZNwqZNm7BixQrs3bsXd999N4YOHYojR444pikrK8OLL76I//znPzhw4ACio6Nd/MkQNSBBpAJbt24VAMTKlSudbo+MjBSBgYEiMDBQPPXUU2LdunUiJCREGAwGp+lat24tlixZIoQQ4vnnnxcBAQHiwoULjvuffPJJ0atXLyGEEBcvXhQ+Pj5ixYoVjvsLCgqEv7+/eOyxx4QQQhw9elQoiiLOnDnj9DqDBg0Ss2fPFkIIsXTpUgFA7N692zUfApFk3IZCqqIoitP1bdu2wWazYezYsTAajUhLS0NJSQkiIyOdpisvL0dmZqbjenx8PIKDgx3XY2NjcfbsWQD2pReTyYTevXs77o+IiEBCQoLj+s6dOyGEQLt27Zxex2g0Or22j48PunTpUod3TOQ+WCikCm3atIGiKDh06JDT7a1atQIA+Pv7AwBsNhtiY2OdtmVUCAsLc/xbr9c73acoCmw2GwBACHHdPDabDVqtFmlpadBqtU73BQUFOf7t7+9fqQSJPBULhVQhMjISt956K95880389a9/vep2lKSkJOTm5kKn0yE+Pr5Wr9WmTRvo9Xps3boVzZs3BwCcP38eGRkZ6N+/PwCgW7dusFqtOHv2LPr161er1yHyNNwoT6rx1ltvwWKxIDk5GZ999hnS09Nx+PBhfPzxxzh06BC0Wi0GDx6M3r17Y8SIEVizZg2ysrKwefNmzJkzBzt27KjW6wQFBWHKlCl48sknsW7dOuzfvx8TJ0502t23Xbt2GDt2LCZMmICVK1fi+PHj2L59OxYuXIjVq1fX10dAJBWXUEg1WrdujV27dmHBggWYPXs2Tp8+DV9fX3To0AGzZs3CtGnToCgKVq9ejWeeeQaTJ0/GuXPn0LhxY9x8882IiYmp9mu98sorKCkpwR133IHg4GDMnDkTxcXFTtMsXboU8+fPx8yZM3HmzBlERkaid+/eGDZsmKvfOpFbUER1VggTERFdB1d5ERGRS7BQiIjIJVgoRETkEiwUIiJyCRYKERG5BAuFiIhcgoVCREQuwUIhIiKXYKEQEZFLsFCIiMglWChEROQS/w/LUe41Eot3AQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# gender distribution visualization\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.pie(df_cus[\"Gender\"].value_counts(),autopct='%1.0f%%',labels=['Male', 'Female'])\n",
    "plt.xlabel('Gender')\n",
    "plt.title('Gender Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Exchange_Rate csv file and data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Currency</th>\n",
       "      <th>Exchange</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2015</td>\n",
       "      <td>USD</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/1/2015</td>\n",
       "      <td>CAD</td>\n",
       "      <td>1.1583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/1/2015</td>\n",
       "      <td>AUD</td>\n",
       "      <td>1.2214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/1/2015</td>\n",
       "      <td>EUR</td>\n",
       "      <td>0.8237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/1/2015</td>\n",
       "      <td>GBP</td>\n",
       "      <td>0.6415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date Currency  Exchange\n",
       "0  1/1/2015      USD    1.0000\n",
       "1  1/1/2015      CAD    1.1583\n",
       "2  1/1/2015      AUD    1.2214\n",
       "3  1/1/2015      EUR    0.8237\n",
       "4  1/1/2015      GBP    0.6415"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exchange = pd.read_csv(\"csv_data/DataSet/Exchange_Rates.csv\", encoding='ISO-8859-1')\n",
    "df_exchange.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11215 entries, 0 to 11214\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Date      11215 non-null  object \n",
      " 1   Currency  11215 non-null  object \n",
      " 2   Exchange  11215 non-null  float64\n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 263.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_exchange.info() # check data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date        0\n",
       "Currency    0\n",
       "Exchange    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exchange.isnull().sum() # check for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Currency_Code</th>\n",
       "      <th>Exchange</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2015</td>\n",
       "      <td>USD</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/1/2015</td>\n",
       "      <td>CAD</td>\n",
       "      <td>1.1583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/1/2015</td>\n",
       "      <td>AUD</td>\n",
       "      <td>1.2214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/1/2015</td>\n",
       "      <td>EUR</td>\n",
       "      <td>0.8237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/1/2015</td>\n",
       "      <td>GBP</td>\n",
       "      <td>0.6415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date Currency_Code  Exchange\n",
       "0  1/1/2015           USD    1.0000\n",
       "1  1/1/2015           CAD    1.1583\n",
       "2  1/1/2015           AUD    1.2214\n",
       "3  1/1/2015           EUR    0.8237\n",
       "4  1/1/2015           GBP    0.6415"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exchange.rename(columns={'Currency':'Currency_Code'}, inplace=True) # rename column\n",
    "df_exchange.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Currency_Code</th>\n",
       "      <th>Exchange</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015/01/01</td>\n",
       "      <td>USD</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015/01/01</td>\n",
       "      <td>CAD</td>\n",
       "      <td>1.1583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015/01/01</td>\n",
       "      <td>AUD</td>\n",
       "      <td>1.2214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015/01/01</td>\n",
       "      <td>EUR</td>\n",
       "      <td>0.8237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015/01/01</td>\n",
       "      <td>GBP</td>\n",
       "      <td>0.6415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Currency_Code  Exchange\n",
       "0  2015/01/01           USD    1.0000\n",
       "1  2015/01/01           CAD    1.1583\n",
       "2  2015/01/01           AUD    1.2214\n",
       "3  2015/01/01           EUR    0.8237\n",
       "4  2015/01/01           GBP    0.6415"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exchange['Date'] = df_exchange['Date'].apply(lambda x: pd.to_datetime(x).strftime('%Y/%m/%d')) # datetime format adjustment for 'Date' column as YYYY/MM/DD\n",
    "df_exchange.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['USD', 'CAD', 'AUD', 'EUR', 'GBP'], dtype='object', name='Currency_Code')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "er_labels = df_exchange['Currency_Code'].value_counts().index\n",
    "er_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "plt.pie(df_exchange['Currency_Code'].value_counts(),autopct='%1.0f%%',labels=er_labels)\n",
    "plt.xlabel('Currency Code')\n",
    "plt.title('Currency Code Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### product csv file and data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>ProductKey</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Color</th>\n",
       "      <th>Unit Cost USD</th>\n",
       "      <th>Unit Price USD</th>\n",
       "      <th>SubcategoryKey</th>\n",
       "      <th>Subcategory</th>\n",
       "      <th>CategoryKey</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Contoso 512MB MP3 Player E51 Silver</td>\n",
       "      <td>Contoso</td>\n",
       "      <td>Silver</td>\n",
       "      <td>$6.62</td>\n",
       "      <td>$12.99</td>\n",
       "      <td>101</td>\n",
       "      <td>MP4&amp;MP3</td>\n",
       "      <td>1</td>\n",
       "      <td>Audio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Contoso 512MB MP3 Player E51 Blue</td>\n",
       "      <td>Contoso</td>\n",
       "      <td>Blue</td>\n",
       "      <td>$6.62</td>\n",
       "      <td>$12.99</td>\n",
       "      <td>101</td>\n",
       "      <td>MP4&amp;MP3</td>\n",
       "      <td>1</td>\n",
       "      <td>Audio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Contoso 1G MP3 Player E100 White</td>\n",
       "      <td>Contoso</td>\n",
       "      <td>White</td>\n",
       "      <td>$7.40</td>\n",
       "      <td>$14.52</td>\n",
       "      <td>101</td>\n",
       "      <td>MP4&amp;MP3</td>\n",
       "      <td>1</td>\n",
       "      <td>Audio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Contoso 2G MP3 Player E200 Silver</td>\n",
       "      <td>Contoso</td>\n",
       "      <td>Silver</td>\n",
       "      <td>$11.00</td>\n",
       "      <td>$21.57</td>\n",
       "      <td>101</td>\n",
       "      <td>MP4&amp;MP3</td>\n",
       "      <td>1</td>\n",
       "      <td>Audio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Contoso 2G MP3 Player E200 Red</td>\n",
       "      <td>Contoso</td>\n",
       "      <td>Red</td>\n",
       "      <td>$11.00</td>\n",
       "      <td>$21.57</td>\n",
       "      <td>101</td>\n",
       "      <td>MP4&amp;MP3</td>\n",
       "      <td>1</td>\n",
       "      <td>Audio</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ProductKey                         Product Name    Brand   Color  \\\n",
       "0           1  Contoso 512MB MP3 Player E51 Silver  Contoso  Silver   \n",
       "1           2    Contoso 512MB MP3 Player E51 Blue  Contoso    Blue   \n",
       "2           3     Contoso 1G MP3 Player E100 White  Contoso   White   \n",
       "3           4    Contoso 2G MP3 Player E200 Silver  Contoso  Silver   \n",
       "4           5       Contoso 2G MP3 Player E200 Red  Contoso     Red   \n",
       "\n",
       "  Unit Cost USD Unit Price USD  SubcategoryKey Subcategory  CategoryKey  \\\n",
       "0        $6.62         $12.99              101     MP4&MP3            1   \n",
       "1        $6.62         $12.99              101     MP4&MP3            1   \n",
       "2        $7.40         $14.52              101     MP4&MP3            1   \n",
       "3       $11.00         $21.57              101     MP4&MP3            1   \n",
       "4       $11.00         $21.57              101     MP4&MP3            1   \n",
       "\n",
       "  Category  \n",
       "0    Audio  \n",
       "1    Audio  \n",
       "2    Audio  \n",
       "3    Audio  \n",
       "4    Audio  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_product = pd.read_csv(\"csv_data/DataSet/Products.csv\", encoding='ISO-8859-1')\n",
    "df_product.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2517 entries, 0 to 2516\n",
      "Data columns (total 10 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   ProductKey      2517 non-null   int64 \n",
      " 1   Product Name    2517 non-null   object\n",
      " 2   Brand           2517 non-null   object\n",
      " 3   Color           2517 non-null   object\n",
      " 4   Unit Cost USD   2517 non-null   object\n",
      " 5   Unit Price USD  2517 non-null   object\n",
      " 6   SubcategoryKey  2517 non-null   int64 \n",
      " 7   Subcategory     2517 non-null   object\n",
      " 8   CategoryKey     2517 non-null   int64 \n",
      " 9   Category        2517 non-null   object\n",
      "dtypes: int64(3), object(7)\n",
      "memory usage: 196.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_product.info() # check data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ProductKey        0\n",
       "Product Name      0\n",
       "Brand             0\n",
       "Color             0\n",
       "Unit Cost USD     0\n",
       "Unit Price USD    0\n",
       "SubcategoryKey    0\n",
       "Subcategory       0\n",
       "CategoryKey       0\n",
       "Category          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_product.isnull().sum() # check for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_product[\"Unit Cost USD\"] = df_product[\"Unit Cost USD\"].str.replace(\"$\", \"\").str.replace(\",\", \"\")\n",
    "df_product[\"Unit Cost USD\"] = df_product[\"Unit Cost USD\"].astype(float)\n",
    "\n",
    "df_product[\"Unit Price USD\"] = df_product[\"Unit Price USD\"].str.replace(\"$\", \"\").str.replace(\",\", \"\")\n",
    "df_product[\"Unit Price USD\"] = df_product[\"Unit Price USD\"].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2517 entries, 0 to 2516\n",
      "Data columns (total 10 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   ProductKey      2517 non-null   int64  \n",
      " 1   Product Name    2517 non-null   object \n",
      " 2   Brand           2517 non-null   object \n",
      " 3   Color           2517 non-null   object \n",
      " 4   Unit Cost USD   2517 non-null   float64\n",
      " 5   Unit Price USD  2517 non-null   float64\n",
      " 6   SubcategoryKey  2517 non-null   int64  \n",
      " 7   Subcategory     2517 non-null   object \n",
      " 8   CategoryKey     2517 non-null   int64  \n",
      " 9   Category        2517 non-null   object \n",
      "dtypes: float64(2), int64(3), object(5)\n",
      "memory usage: 196.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_product.info() # check data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Contoso', 'Fabrikam', 'Litware', 'Proseware', 'Adventure Works',\n",
       "       'Southridge Video', 'Wide World Importers', 'The Phone Company',\n",
       "       'Tailspin Toys', 'A. Datum', 'Northwind Traders'],\n",
       "      dtype='object', name='Brand')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_labels = df_product['Brand'].value_counts().index\n",
    "product_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "plt.pie(df_product['Brand'].value_counts(),autopct='%1.0f%%',labels=product_labels)\n",
    "plt.xlabel('Brand')\n",
    "plt.title('Brand Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "sns.countplot(x=df_product[\"Category\"], data=df_product, hue ='Category', palette='Set1')\n",
    "plt.xlabel(\"Category\")\n",
    "plt.ylabel(\"Counts\")\n",
    "plt.xticks(rotation=0, size=5)\n",
    "plt.title(\"Counts as per Category\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sales data csv file and data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Order Number</th>\n",
       "      <th>Line Item</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Delivery Date</th>\n",
       "      <th>CustomerKey</th>\n",
       "      <th>StoreKey</th>\n",
       "      <th>ProductKey</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Currency Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>366000</td>\n",
       "      <td>1</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>265598</td>\n",
       "      <td>10</td>\n",
       "      <td>1304</td>\n",
       "      <td>1</td>\n",
       "      <td>CAD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>366001</td>\n",
       "      <td>1</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>1/13/2016</td>\n",
       "      <td>1269051</td>\n",
       "      <td>0</td>\n",
       "      <td>1048</td>\n",
       "      <td>2</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>366001</td>\n",
       "      <td>2</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>1/13/2016</td>\n",
       "      <td>1269051</td>\n",
       "      <td>0</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>366002</td>\n",
       "      <td>1</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>1/12/2016</td>\n",
       "      <td>266019</td>\n",
       "      <td>0</td>\n",
       "      <td>1106</td>\n",
       "      <td>7</td>\n",
       "      <td>CAD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>366002</td>\n",
       "      <td>2</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>1/12/2016</td>\n",
       "      <td>266019</td>\n",
       "      <td>0</td>\n",
       "      <td>373</td>\n",
       "      <td>1</td>\n",
       "      <td>CAD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Order Number  Line Item Order Date Delivery Date  CustomerKey  StoreKey  \\\n",
       "0        366000          1   1/1/2016           NaN       265598        10   \n",
       "1        366001          1   1/1/2016     1/13/2016      1269051         0   \n",
       "2        366001          2   1/1/2016     1/13/2016      1269051         0   \n",
       "3        366002          1   1/1/2016     1/12/2016       266019         0   \n",
       "4        366002          2   1/1/2016     1/12/2016       266019         0   \n",
       "\n",
       "   ProductKey  Quantity Currency Code  \n",
       "0        1304         1           CAD  \n",
       "1        1048         2           USD  \n",
       "2        2007         1           USD  \n",
       "3        1106         7           CAD  \n",
       "4         373         1           CAD  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sales = pd.read_csv(\"csv_data/DataSet/Sales.csv\", encoding='ISO-8859-1')\n",
    "df_sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 62884 entries, 0 to 62883\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   Order Number   62884 non-null  int64 \n",
      " 1   Line Item      62884 non-null  int64 \n",
      " 2   Order Date     62884 non-null  object\n",
      " 3   Delivery Date  13165 non-null  object\n",
      " 4   CustomerKey    62884 non-null  int64 \n",
      " 5   StoreKey       62884 non-null  int64 \n",
      " 6   ProductKey     62884 non-null  int64 \n",
      " 7   Quantity       62884 non-null  int64 \n",
      " 8   Currency Code  62884 non-null  object\n",
      "dtypes: int64(6), object(3)\n",
      "memory usage: 4.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# sales data type check\n",
    "df_sales.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order Number         0\n",
       "Line Item            0\n",
       "Order Date           0\n",
       "Delivery Date    49719\n",
       "CustomerKey          0\n",
       "StoreKey             0\n",
       "ProductKey           0\n",
       "Quantity             0\n",
       "Currency Code        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sales.isnull().sum() # check for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order Number</th>\n",
       "      <th>Line Item</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Delivery Date</th>\n",
       "      <th>CustomerKey</th>\n",
       "      <th>StoreKey</th>\n",
       "      <th>ProductKey</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Currency Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>366000</td>\n",
       "      <td>1</td>\n",
       "      <td>2016/01/01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>265598</td>\n",
       "      <td>10</td>\n",
       "      <td>1304</td>\n",
       "      <td>1</td>\n",
       "      <td>CAD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>366001</td>\n",
       "      <td>1</td>\n",
       "      <td>2016/01/01</td>\n",
       "      <td>1/13/2016</td>\n",
       "      <td>1269051</td>\n",
       "      <td>0</td>\n",
       "      <td>1048</td>\n",
       "      <td>2</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>366001</td>\n",
       "      <td>2</td>\n",
       "      <td>2016/01/01</td>\n",
       "      <td>1/13/2016</td>\n",
       "      <td>1269051</td>\n",
       "      <td>0</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>366002</td>\n",
       "      <td>1</td>\n",
       "      <td>2016/01/01</td>\n",
       "      <td>1/12/2016</td>\n",
       "      <td>266019</td>\n",
       "      <td>0</td>\n",
       "      <td>1106</td>\n",
       "      <td>7</td>\n",
       "      <td>CAD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>366002</td>\n",
       "      <td>2</td>\n",
       "      <td>2016/01/01</td>\n",
       "      <td>1/12/2016</td>\n",
       "      <td>266019</td>\n",
       "      <td>0</td>\n",
       "      <td>373</td>\n",
       "      <td>1</td>\n",
       "      <td>CAD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Order Number  Line Item  Order Date Delivery Date  CustomerKey  StoreKey  \\\n",
       "0        366000          1  2016/01/01           NaN       265598        10   \n",
       "1        366001          1  2016/01/01     1/13/2016      1269051         0   \n",
       "2        366001          2  2016/01/01     1/13/2016      1269051         0   \n",
       "3        366002          1  2016/01/01     1/12/2016       266019         0   \n",
       "4        366002          2  2016/01/01     1/12/2016       266019         0   \n",
       "\n",
       "   ProductKey  Quantity Currency Code  \n",
       "0        1304         1           CAD  \n",
       "1        1048         2           USD  \n",
       "2        2007         1           USD  \n",
       "3        1106         7           CAD  \n",
       "4         373         1           CAD  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# datetime format adjustment for 'Date' column as YYYY/MM/DD\n",
    "df_sales['Order Date'] = df_sales['Order Date'].apply(lambda x: pd.to_datetime(x).strftime('%Y/%m/%d'))\n",
    "df_sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Order Number</th>\n",
       "      <th>Line Item</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Delivery Date</th>\n",
       "      <th>CustomerKey</th>\n",
       "      <th>StoreKey</th>\n",
       "      <th>ProductKey</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Currency Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>366000</td>\n",
       "      <td>1</td>\n",
       "      <td>2016/01/01</td>\n",
       "      <td>0</td>\n",
       "      <td>265598</td>\n",
       "      <td>10</td>\n",
       "      <td>1304</td>\n",
       "      <td>1</td>\n",
       "      <td>CAD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>366001</td>\n",
       "      <td>1</td>\n",
       "      <td>2016/01/01</td>\n",
       "      <td>1/13/2016</td>\n",
       "      <td>1269051</td>\n",
       "      <td>0</td>\n",
       "      <td>1048</td>\n",
       "      <td>2</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>366001</td>\n",
       "      <td>2</td>\n",
       "      <td>2016/01/01</td>\n",
       "      <td>1/13/2016</td>\n",
       "      <td>1269051</td>\n",
       "      <td>0</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>366002</td>\n",
       "      <td>1</td>\n",
       "      <td>2016/01/01</td>\n",
       "      <td>1/12/2016</td>\n",
       "      <td>266019</td>\n",
       "      <td>0</td>\n",
       "      <td>1106</td>\n",
       "      <td>7</td>\n",
       "      <td>CAD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>366002</td>\n",
       "      <td>2</td>\n",
       "      <td>2016/01/01</td>\n",
       "      <td>1/12/2016</td>\n",
       "      <td>266019</td>\n",
       "      <td>0</td>\n",
       "      <td>373</td>\n",
       "      <td>1</td>\n",
       "      <td>CAD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Order Number  Line Item  Order Date Delivery Date  CustomerKey  StoreKey  \\\n",
       "0        366000          1  2016/01/01             0       265598        10   \n",
       "1        366001          1  2016/01/01     1/13/2016      1269051         0   \n",
       "2        366001          2  2016/01/01     1/13/2016      1269051         0   \n",
       "3        366002          1  2016/01/01     1/12/2016       266019         0   \n",
       "4        366002          2  2016/01/01     1/12/2016       266019         0   \n",
       "\n",
       "   ProductKey  Quantity Currency Code  \n",
       "0        1304         1           CAD  \n",
       "1        1048         2           USD  \n",
       "2        2007         1           USD  \n",
       "3        1106         7           CAD  \n",
       "4         373         1           CAD  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fill the missing values\n",
    "df_sales[\"Delivery Date\"]=df_sales[\"Delivery Date\"].fillna(0)\n",
    "df_sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "plt.pie(df_sales['Currency Code'].value_counts(),autopct='%1.0f%%',labels=df_sales['Currency Code'].value_counts().index)\n",
    "plt.xlabel('Currency Code')\n",
    "plt.title('Currency Code Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### stores csv file and data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>StoreKey</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>Square Meters</th>\n",
       "      <th>Open Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>595.0</td>\n",
       "      <td>1/1/2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>665.0</td>\n",
       "      <td>1/12/2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Australia</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1/7/2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Tasmania</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1/1/2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>12/9/2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   StoreKey    Country                         State  Square Meters  Open Date\n",
       "0         1  Australia  Australian Capital Territory          595.0   1/1/2008\n",
       "1         2  Australia            Northern Territory          665.0  1/12/2008\n",
       "2         3  Australia               South Australia         2000.0   1/7/2012\n",
       "3         4  Australia                      Tasmania         2000.0   1/1/2010\n",
       "4         5  Australia                      Victoria         2000.0  12/9/2015"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_store = pd.read_csv(\"csv_data/DataSet/Stores.csv\", encoding='ISO-8859-1')\n",
    "df_store.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 67 entries, 0 to 66\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   StoreKey       67 non-null     int64  \n",
      " 1   Country        67 non-null     object \n",
      " 2   State          67 non-null     object \n",
      " 3   Square Meters  66 non-null     float64\n",
      " 4   Open Date      67 non-null     object \n",
      "dtypes: float64(1), int64(1), object(3)\n",
      "memory usage: 2.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df_store.info() # check data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StoreKey         0\n",
       "Country          0\n",
       "State            0\n",
       "Square Meters    1\n",
       "Open Date        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_store.isnull().sum() # check for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StoreKey         0\n",
       "Country          0\n",
       "State            0\n",
       "Square Meters    0\n",
       "Open Date        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_store['Square Meters'] = df_store['Square Meters'].fillna(0)\n",
    "df_store.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>StoreKey</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>Square Meters</th>\n",
       "      <th>Open Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>595.0</td>\n",
       "      <td>2008/01/01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>665.0</td>\n",
       "      <td>2008/01/12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Australia</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2012/01/07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Tasmania</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2010/01/01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2015/12/09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   StoreKey    Country                         State  Square Meters  \\\n",
       "0         1  Australia  Australian Capital Territory          595.0   \n",
       "1         2  Australia            Northern Territory          665.0   \n",
       "2         3  Australia               South Australia         2000.0   \n",
       "3         4  Australia                      Tasmania         2000.0   \n",
       "4         5  Australia                      Victoria         2000.0   \n",
       "\n",
       "    Open Date  \n",
       "0  2008/01/01  \n",
       "1  2008/01/12  \n",
       "2  2012/01/07  \n",
       "3  2010/01/01  \n",
       "4  2015/12/09  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_store['Open Date'] = df_store['Open Date'].apply(lambda x: pd.to_datetime(x).strftime('%Y/%m/%d')) # datetime format adjustment for 'Open Date' column as YYYY/MM/DD\n",
    "df_store.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "sns.countplot(x=df_store[\"Country\"], data=df_store, hue ='Country', palette='Set1')\n",
    "plt.xlabel(\"Country\")\n",
    "plt.ylabel(\"Counts\")\n",
    "plt.title(\"Counts as per Country\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create SQL database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Database created successfully\n"
     ]
    }
   ],
   "source": [
    "#sql connection\n",
    "sql = mysql.connector.connect(host=\"localhost\", user=\"root\", password=\"\")\n",
    "my_cursor = sql.cursor()\n",
    "my_cursor.execute(\"CREATE DATABASE IF NOT EXISTS customer_sale_data\")\n",
    "print(\"Database created successfully\")\n",
    "sql.database = \"customer_sale_data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table created successfully\n"
     ]
    }
   ],
   "source": [
    "#create table for customer details\n",
    "my_cursor.execute('''CREATE TABLE IF NOT EXISTS customer_details(\n",
    "                  customerkey INT,\n",
    "                  gender VARCHAR(255),\n",
    "                  name VARCHAR(255),\n",
    "                  city VARCHAR(255),\n",
    "                  state VARCHAR(255),\n",
    "                  country VARCHAR(255),\n",
    "                  continent VARCHAR(255),\n",
    "                  birthday VARCHAR(255)\n",
    "                  )''')\n",
    "print(\"Table created successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inserted successfully\n"
     ]
    }
   ],
   "source": [
    "df_cus = df_cus[['CustomerKey', 'Gender', 'Name', 'City', 'State', 'Country', 'Continent', 'Birthday']]\n",
    "\n",
    "insert_query='''INSERT INTO customer_details(\n",
    "                  customerkey,\n",
    "                  gender,\n",
    "                  name,\n",
    "                  city,\n",
    "                  state,\n",
    "                  country,\n",
    "                  continent,\n",
    "                  birthday)\n",
    "                  VALUES(%s,%s,%s,%s,%s,%s,%s,%s)'''\n",
    "data=df_cus.values.tolist()\n",
    "my_cursor.executemany(insert_query, data)\n",
    "print(\"Inserted successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table Created successfully\n"
     ]
    }
   ],
   "source": [
    "my_cursor.execute('''CREATE TABLE IF NOT EXISTS exchange_details(\n",
    "                  date VARCHAR(255),\n",
    "                  currency_code VARCHAR(255),\n",
    "                  exchange FLOAT\n",
    "                  )''')\n",
    "print(\"Table Created successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inserted successfully\n"
     ]
    }
   ],
   "source": [
    "df_exchange = df_exchange[['Date', 'Currency_Code', 'Exchange']]\n",
    "insert_query ='''INSERT INTO exchange_details(\n",
    "                  date,\n",
    "                 currency_code,\n",
    "                  exchange)\n",
    "                  VALUES (%s, %s, %s)'''\n",
    "data=df_exchange.values.tolist()\n",
    "my_cursor.executemany(insert_query,data)\n",
    "print(\"Inserted successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table created successfully\n"
     ]
    }
   ],
   "source": [
    "my_cursor.execute('''CREATE TABLE IF NOT EXISTS product_details(\n",
    "                  productkey INT,\n",
    "                  product_name VARCHAR(255),\n",
    "                  brand VARCHAR(255),\n",
    "                  color VARCHAR(255),\n",
    "                  unit_cost_USD FLOAT,\n",
    "                  unit_price_USD FLOAT,\n",
    "                  subcategory VARCHAR(255),\n",
    "                  categorykey VARCHAR(255),\n",
    "                  category VARCHAR(255)\n",
    "                  )''')\n",
    "print(\"Table created successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inserted successfully\n"
     ]
    }
   ],
   "source": [
    "df_product = df_product[['ProductKey', 'Product Name', 'Brand', 'Color', 'Unit Cost USD', 'Unit Price USD', 'Subcategory', 'CategoryKey', 'Category']]\n",
    "insert_query ='''INSERT INTO product_details(\n",
    "                  productkey,\n",
    "                  product_name,\n",
    "                  brand,\n",
    "                  color,\n",
    "                  unit_cost_USD,\n",
    "                  unit_price_USD,\n",
    "                  subcategory,\n",
    "                  categoryKey,\n",
    "                  category)\n",
    "                  VALUES (%s, %s, %s, %s, %s, %s, %s, %s,%s)'''\n",
    "data=df_product.values.tolist()\n",
    "my_cursor.executemany(insert_query,data)\n",
    "print(\"Inserted successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table created successfully\n"
     ]
    }
   ],
   "source": [
    "my_cursor.execute('''CREATE TABLE IF NOT EXISTS sales_details(\n",
    "                  order_number INT,\n",
    "                  line_item INT,\n",
    "                  order_date VARCHAR(255),\n",
    "                  delivery_date VARCHAR(255),\n",
    "                  customerkey INT,\n",
    "                  storekey INT,\n",
    "                  productkey INT,\n",
    "                  quantity INT,\n",
    "                  currency_code VARCHAR(255)\n",
    "                  )''')\n",
    "print(\"Table created successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inserted successfully\n"
     ]
    }
   ],
   "source": [
    "df_sales = df_sales[['Order Number', 'Line Item', 'Order Date', 'Delivery Date', 'CustomerKey', 'StoreKey', 'ProductKey', 'Quantity', 'Currency Code']]\n",
    "\n",
    "insert_query ='''INSERT INTO sales_details(\n",
    "                  order_number,\n",
    "                  line_item,\n",
    "                  order_date,\n",
    "                  delivery_date,\n",
    "                  customerkey,\n",
    "                  storekey,\n",
    "                  productkey,\n",
    "                  quantity,\n",
    "                  currency_code)\n",
    "                  VALUES (%s, %s, %s, %s, %s, %s, %s, %s,%s)'''\n",
    "data=df_sales.values.tolist()\n",
    "my_cursor.executemany(insert_query,data)\n",
    "print(\"Inserted successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table created successfully\n"
     ]
    }
   ],
   "source": [
    "my_cursor.execute('''CREATE TABLE IF NOT EXISTS store_details(\n",
    "                  storekey INT,\n",
    "                  country VARCHAR(255),\n",
    "                  state VARCHAR(255),\n",
    "                  square_meters FLOAT,\n",
    "                  open_date VARCHAR(255)\n",
    "                  )''')\n",
    "print(\"Table created successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inserted successfully\n"
     ]
    }
   ],
   "source": [
    "df_store = df_store[['StoreKey', 'Country', 'State', 'Square Meters', 'Open Date']]\n",
    "\n",
    "insert_query ='''INSERT INTO store_details(\n",
    "                 storekey,\n",
    "                  country,\n",
    "                  state,\n",
    "                  square_meters,\n",
    "                  open_date)\n",
    "                  VALUES (%s, %s, %s, %s, %s)'''\n",
    "data=df_store.values.tolist()\n",
    "my_cursor.executemany(insert_query,data)\n",
    "print(\"Inserted successfully\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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