ChampoCarpetsCaseAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9f0f2d11",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Markdown in this area required"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "859a4dc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the required packages and getting the xlsx file\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import chi2_contingency as chitest\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "#Storing the file path in our desktop\n",
    "file_path = '~/Desktop/CompleteDataAnalysis/Champo.xlsx'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0e82c3b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Copyright @ IIMB', 'Raw Data-Order and Sample', 'Data Order ONLY', 'Data on Sample ONLY', 'Data for Recommendation', 'Data for Clustering', 'Data - Association Rules A-11 ']\n"
     ]
    }
   ],
   "source": [
    "# We can print all of our sheet names after traversing through the xlsx file\n",
    "xls = pd.ExcelFile(file_path)\n",
    "print(xls.sheet_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d2124d1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading all of our sheets data into seperate variables\n",
    "\n",
    "RawDataOrder = pd.read_excel(file_path, sheet_name = 'Raw Data-Order and Sample')\n",
    "DataOrderOnly = pd.read_excel(file_path, sheet_name = 'Data Order ONLY')\n",
    "DataOnSampleOnly = pd.read_excel(file_path, sheet_name = 'Data on Sample ONLY')\n",
    "RecommendationData = pd.read_excel(file_path, sheet_name = 'Data for Recommendation')\n",
    "ClusteringData = pd.read_excel(file_path, sheet_name = 'Data for Clustering')\n",
    "AssocRulesData = pd.read_excel(file_path, sheet_name = 'Data - Association Rules A-11 ')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "78e03efd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sheet Name: Raw Data-Order and Sample\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18955 entries, 0 to 18954\n",
      "Data columns (total 16 columns):\n",
      " #   Column           Non-Null Count  Dtype         \n",
      "---  ------           --------------  -----         \n",
      " 0   OrderType        18955 non-null  object        \n",
      " 1   OrderCategory    18955 non-null  object        \n",
      " 2   CustomerCode     18955 non-null  object        \n",
      " 3   CountryName      18955 non-null  object        \n",
      " 4   CustomerOrderNo  18946 non-null  object        \n",
      " 5   Custorderdate    18955 non-null  datetime64[ns]\n",
      " 6   UnitName         18955 non-null  object        \n",
      " 7   QtyRequired      18955 non-null  int64         \n",
      " 8   TotalArea        18955 non-null  float64       \n",
      " 9   Amount           18955 non-null  float64       \n",
      " 10  ITEM_NAME        18955 non-null  object        \n",
      " 11  QualityName      18955 non-null  object        \n",
      " 12  DesignName       18955 non-null  object        \n",
      " 13  ColorName        18955 non-null  object        \n",
      " 14  ShapeName        18955 non-null  object        \n",
      " 15  AreaFt           18955 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(3), int64(1), object(11)\n",
      "memory usage: 2.3+ MB\n",
      "\n",
      "==================================================\n",
      "\n",
      "Sheet Name: Data Order ONLY\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13135 entries, 0 to 13134\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   CustomerCode  13135 non-null  object \n",
      " 1   CountryName   13135 non-null  object \n",
      " 2   QtyRequired   13135 non-null  int64  \n",
      " 3   TotalArea     13135 non-null  float64\n",
      " 4   Amount        13135 non-null  float64\n",
      " 5   ITEM_NAME     13135 non-null  object \n",
      " 6   QualityName   13135 non-null  object \n",
      " 7   DesignName    13135 non-null  object \n",
      " 8   ColorName     13135 non-null  object \n",
      " 9   ShapeName     13135 non-null  object \n",
      " 10  AreaFt        13135 non-null  float64\n",
      " 11  AreaMtr       13135 non-null  float64\n",
      "dtypes: float64(4), int64(1), object(7)\n",
      "memory usage: 1.2+ MB\n",
      "\n",
      "==================================================\n",
      "\n",
      "Sheet Name: Data on Sample ONLY\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5820 entries, 0 to 5819\n",
      "Data columns (total 25 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   CustomerCode      5820 non-null   object \n",
      " 1   CountryName       5820 non-null   object \n",
      " 2   USA               5781 non-null   float64\n",
      " 3   UK                5781 non-null   float64\n",
      " 4   Italy             5781 non-null   float64\n",
      " 5   Belgium           5781 non-null   float64\n",
      " 6   Romania           5781 non-null   float64\n",
      " 7   Australia         5781 non-null   float64\n",
      " 8   India             5781 non-null   float64\n",
      " 9   QtyRequired       5820 non-null   int64  \n",
      " 10  ITEM_NAME         5820 non-null   object \n",
      " 11  Hand Tufted       5820 non-null   int64  \n",
      " 12  Durry             5820 non-null   int64  \n",
      " 13  Double Back       5820 non-null   int64  \n",
      " 14  Hand Woven        5820 non-null   int64  \n",
      " 15  Knotted           5820 non-null   int64  \n",
      " 16  Jacquard          5820 non-null   int64  \n",
      " 17  Handloom          5820 non-null   int64  \n",
      " 18  Other             5820 non-null   int64  \n",
      " 19  ShapeName         5820 non-null   object \n",
      " 20  REC               5820 non-null   int64  \n",
      " 21  Round             5820 non-null   int64  \n",
      " 22  Square            5820 non-null   int64  \n",
      " 23  AreaFt            5820 non-null   float64\n",
      " 24  Order Conversion  5820 non-null   int64  \n",
      "dtypes: float64(8), int64(13), object(4)\n",
      "memory usage: 1.1+ MB\n",
      "\n",
      "==================================================\n",
      "\n",
      "Sheet Name: Data for Recommendation\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20 entries, 0 to 19\n",
      "Data columns (total 44 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Customer      20 non-null     object \n",
      " 1   Hand Tufted   20 non-null     int64  \n",
      " 2   Double Wowen  20 non-null     int64  \n",
      " 3   Durry         20 non-null     int64  \n",
      " 4   Double Back   20 non-null     int64  \n",
      " 5   Knotted       20 non-null     int64  \n",
      " 6   Jacquared     20 non-null     int64  \n",
      " 7   Handloom      20 non-null     int64  \n",
      " 8   Other         20 non-null     int64  \n",
      " 9   Rectangle     20 non-null     int64  \n",
      " 10  Square        20 non-null     int64  \n",
      " 11  Round         20 non-null     int64  \n",
      " 12  Purple        20 non-null     int64  \n",
      " 13  Gray          20 non-null     int64  \n",
      " 14  Navy          20 non-null     int64  \n",
      " 15  PINK          20 non-null     int64  \n",
      " 16  BLUE          20 non-null     int64  \n",
      " 17  BLUSH PINK    20 non-null     int64  \n",
      " 18  NEUTRAL       20 non-null     int64  \n",
      " 19  TAN           20 non-null     int64  \n",
      " 20  NAVY          20 non-null     int64  \n",
      " 21  Unnamed: 21   0 non-null      float64\n",
      " 22  Unnamed: 22   0 non-null      float64\n",
      " 23  Customer .1   20 non-null     object \n",
      " 24  H-2           20 non-null     int64  \n",
      " 25  P-5           20 non-null     int64  \n",
      " 26  M-1           20 non-null     int64  \n",
      " 27  A-9           20 non-null     int64  \n",
      " 28  C-2           20 non-null     int64  \n",
      " 29  JL            20 non-null     int64  \n",
      " 30  N-1           20 non-null     int64  \n",
      " 31  T-5           20 non-null     int64  \n",
      " 32  C-1           20 non-null     int64  \n",
      " 33  T-2           20 non-null     int64  \n",
      " 34  I-2           20 non-null     int64  \n",
      " 35  PD            20 non-null     int64  \n",
      " 36  L-5           20 non-null     int64  \n",
      " 37  M-2           20 non-null     int64  \n",
      " 38  RC            20 non-null     int64  \n",
      " 39  P-4           20 non-null     int64  \n",
      " 40  T-4           20 non-null     int64  \n",
      " 41  PC            20 non-null     int64  \n",
      " 42  A-11          20 non-null     int64  \n",
      " 43  CC            20 non-null     int64  \n",
      "dtypes: float64(2), int64(40), object(2)\n",
      "memory usage: 7.0+ KB\n",
      "\n",
      "==================================================\n",
      "\n",
      "Sheet Name: Data for Clustering\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45 entries, 0 to 44\n",
      "Data columns (total 14 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   Row Labels          45 non-null     object \n",
      " 1   Sum of QtyRequired  45 non-null     int64  \n",
      " 2   Sum of TotalArea    45 non-null     float64\n",
      " 3   Sum of Amount       45 non-null     float64\n",
      " 4   DURRY               45 non-null     int64  \n",
      " 5   HANDLOOM            45 non-null     int64  \n",
      " 6   DOUBLE BACK         45 non-null     int64  \n",
      " 7   JACQUARD            45 non-null     int64  \n",
      " 8   HAND TUFTED         45 non-null     int64  \n",
      " 9   HAND WOVEN          45 non-null     int64  \n",
      " 10  KNOTTED             45 non-null     int64  \n",
      " 11  GUN TUFTED          45 non-null     int64  \n",
      " 12  Powerloom Jacquard  45 non-null     int64  \n",
      " 13  INDO TEBETAN        45 non-null     int64  \n",
      "dtypes: float64(2), int64(11), object(1)\n",
      "memory usage: 5.1+ KB\n",
      "\n",
      "==================================================\n",
      "\n",
      "Sheet Name: Data - Association Rules A-11 \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11 entries, 0 to 10\n",
      "Data columns (total 17 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Order No      11 non-null     int64  \n",
      " 1   BEIGE         11 non-null     int64  \n",
      " 2   CHARCOAL      11 non-null     int64  \n",
      " 3   DENIM         11 non-null     int64  \n",
      " 4   DK GREY       11 non-null     int64  \n",
      " 5   LT TURQUOISE  11 non-null     int64  \n",
      " 6   MID GREY      11 non-null     int64  \n",
      " 7   OLIVE GREEN   11 non-null     int64  \n",
      " 8   Unnamed: 8    0 non-null      float64\n",
      " 9   Order No.1    11 non-null     int64  \n",
      " 10  SKU 1         11 non-null     object \n",
      " 11  SKU 2         5 non-null      object \n",
      " 12  SKU 3         2 non-null      object \n",
      " 13  SKU 4         2 non-null      object \n",
      " 14  SKU 5         2 non-null      object \n",
      " 15  SKU 6         0 non-null      float64\n",
      " 16  SKU 7         0 non-null      float64\n",
      "dtypes: float64(3), int64(9), object(5)\n",
      "memory usage: 1.6+ KB\n",
      "\n",
      "==================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Going through the data for finding out missing values through all of the sheets for missing values\n",
    "\n",
    "# First we are loading all of the sheets into a dictionary of dataframes\n",
    "\n",
    "dfs = pd.read_excel(file_path, sheet_name = None) #Note that sheet_name is None loads all of the sheets\n",
    "\n",
    "\n",
    "#Now we try to iterate through each sheet for its info\n",
    "for sheet_name, df in list(dfs.items())[1:]:\n",
    "    print(f\"Sheet Name: {sheet_name}\")\n",
    "    df.info()\n",
    "    print(\"\\n\"+ \"=\"*50 + \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "74ccc91b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      OrderType OrderCategory CustomerCode CountryName CustomerOrderNo  \\\n",
      "6210    Pc Wise         Order          P-5         USA             NaN   \n",
      "6213    Pc Wise         Order          P-5         USA             NaN   \n",
      "6215    Pc Wise         Order          P-5         USA             NaN   \n",
      "6217    Pc Wise         Order          P-5         USA             NaN   \n",
      "6219    Pc Wise         Order          P-5         USA             NaN   \n",
      "6223    Pc Wise         Order          P-5         USA             NaN   \n",
      "8957  Area Wise         Order           CC       INDIA             NaN   \n",
      "8958  Area Wise         Order           CC       INDIA             NaN   \n",
      "8959  Area Wise         Order           CC       INDIA             NaN   \n",
      "\n",
      "     Custorderdate UnitName  QtyRequired  TotalArea  Amount    ITEM_NAME  \\\n",
      "6210    2017-08-29       Ft           50        6.0  1005.0  HAND TUFTED   \n",
      "6213    2017-08-29       Ft           20       15.0  1005.0  HAND TUFTED   \n",
      "6215    2017-08-29       Ft            5       40.0   670.0  HAND TUFTED   \n",
      "6217    2017-08-29       Ft           10       80.0  2680.0  HAND TUFTED   \n",
      "6219    2017-08-29       Ft           50        3.0   502.5  HAND TUFTED   \n",
      "6223    2017-08-29       Ft           10       20.0   670.0  HAND TUFTED   \n",
      "8957    2018-04-05       Ft            1        6.0     0.0  HAND TUFTED   \n",
      "8958    2018-04-05       Ft            1        6.0     0.0  HAND TUFTED   \n",
      "8959    2018-04-05       Ft            1        6.0     0.0  HAND TUFTED   \n",
      "\n",
      "                QualityName      DesignName    ColorName ShapeName  AreaFt  \n",
      "6210  TUFTED 30C HARD TWIST  PAISELY [8874]        MULTI       REC     6.0  \n",
      "6213  TUFTED 30C HARD TWIST  PAISELY [8874]        MULTI       REC    15.0  \n",
      "6215  TUFTED 30C HARD TWIST  PAISELY [8874]        MULTI       REC    40.0  \n",
      "6217  TUFTED 30C HARD TWIST  PAISELY [8874]        MULTI       REC    80.0  \n",
      "6219  TUFTED 30C HARD TWIST  PAISELY [8874]        MULTI       REC     3.0  \n",
      "6223  TUFTED 30C HARD TWIST  PAISELY [8874]        MULTI       REC    20.0  \n",
      "8957             TUFTED 60C           10041   WHITE/DOVE       REC     6.0  \n",
      "8958             TUFTED 60C           10041  GREY/INDIGO       REC     6.0  \n",
      "8959             TUFTED 60C           10041  WHITE/PETAL       REC     6.0  \n"
     ]
    }
   ],
   "source": [
    "# Observed there are 9 values that are null in first sheet displaying the null values \n",
    "\n",
    "nullRows_CustomerOrderNo = RawDataOrder[RawDataOrder['CustomerOrderNo'].isnull()]\n",
    "print(nullRows_CustomerOrderNo)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "07692675",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing the missing values with the median\n",
    "RawDataOrder['CustomerOrderNo'] = pd.to_numeric(RawDataOrder['CustomerOrderNo'], errors='coerce')\n",
    "regularRowsMedian = RawDataOrder['CustomerOrderNo'].median()\n",
    "\n",
    "RawDataOrder['CustomerOrderNo'] = RawDataOrder['CustomerOrderNo'].fillna(regularRowsMedian)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3e287d21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18955 entries, 0 to 18954\n",
      "Data columns (total 16 columns):\n",
      " #   Column           Non-Null Count  Dtype         \n",
      "---  ------           --------------  -----         \n",
      " 0   OrderType        18955 non-null  object        \n",
      " 1   OrderCategory    18955 non-null  object        \n",
      " 2   CustomerCode     18955 non-null  object        \n",
      " 3   CountryName      18955 non-null  object        \n",
      " 4   CustomerOrderNo  18955 non-null  float64       \n",
      " 5   Custorderdate    18955 non-null  datetime64[ns]\n",
      " 6   UnitName         18955 non-null  object        \n",
      " 7   QtyRequired      18955 non-null  int64         \n",
      " 8   TotalArea        18955 non-null  float64       \n",
      " 9   Amount           18955 non-null  float64       \n",
      " 10  ITEM_NAME        18955 non-null  object        \n",
      " 11  QualityName      18955 non-null  object        \n",
      " 12  DesignName       18955 non-null  object        \n",
      " 13  ColorName        18955 non-null  object        \n",
      " 14  ShapeName        18955 non-null  object        \n",
      " 15  AreaFt           18955 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(4), int64(1), object(10)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# as we can see the data is now cleaned for the first sheet\n",
    "RawDataOrder.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "28634774",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5820 entries, 0 to 5819\n",
      "Data columns (total 25 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   CustomerCode      5820 non-null   object \n",
      " 1   CountryName       5820 non-null   object \n",
      " 2   USA               5781 non-null   float64\n",
      " 3   UK                5781 non-null   float64\n",
      " 4   Italy             5781 non-null   float64\n",
      " 5   Belgium           5781 non-null   float64\n",
      " 6   Romania           5781 non-null   float64\n",
      " 7   Australia         5781 non-null   float64\n",
      " 8   India             5781 non-null   float64\n",
      " 9   QtyRequired       5820 non-null   int64  \n",
      " 10  ITEM_NAME         5820 non-null   object \n",
      " 11  Hand Tufted       5820 non-null   int64  \n",
      " 12  Durry             5820 non-null   int64  \n",
      " 13  Double Back       5820 non-null   int64  \n",
      " 14  Hand Woven        5820 non-null   int64  \n",
      " 15  Knotted           5820 non-null   int64  \n",
      " 16  Jacquard          5820 non-null   int64  \n",
      " 17  Handloom          5820 non-null   int64  \n",
      " 18  Other             5820 non-null   int64  \n",
      " 19  ShapeName         5820 non-null   object \n",
      " 20  REC               5820 non-null   int64  \n",
      " 21  Round             5820 non-null   int64  \n",
      " 22  Square            5820 non-null   int64  \n",
      " 23  AreaFt            5820 non-null   float64\n",
      " 24  Order Conversion  5820 non-null   int64  \n",
      "dtypes: float64(8), int64(13), object(4)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# Data on Sample Only has some missing values so we can continue cleaning it\n",
    "\n",
    "DataOnSampleOnly.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ddad909d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column 'CountryName' is not numeric and will be skipped.\n"
     ]
    }
   ],
   "source": [
    "# Identified columns USA to india i.e 2 to 8 have null values so replacing them\n",
    "columnsRemoveNull = DataOnSampleOnly.columns[1:9]\n",
    "\n",
    "#Adding median to every columns from 2 to 8 that contain missing values\n",
    "for column in columnsRemoveNull:\n",
    "    if pd.api.types.is_numeric_dtype(DataOnSampleOnly[column]):\n",
    "        median = DataOnSampleOnly[column].median()\n",
    "        DataOnSampleOnly[column] = DataOnSampleOnly[column].fillna(median)\n",
    "    else:\n",
    "        print(f\"Column '{column}' is not numeric and will be skipped.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b81cb9f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5820 entries, 0 to 5819\n",
      "Data columns (total 25 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   CustomerCode      5820 non-null   object \n",
      " 1   CountryName       5820 non-null   object \n",
      " 2   USA               5820 non-null   float64\n",
      " 3   UK                5820 non-null   float64\n",
      " 4   Italy             5820 non-null   float64\n",
      " 5   Belgium           5820 non-null   float64\n",
      " 6   Romania           5820 non-null   float64\n",
      " 7   Australia         5820 non-null   float64\n",
      " 8   India             5820 non-null   float64\n",
      " 9   QtyRequired       5820 non-null   int64  \n",
      " 10  ITEM_NAME         5820 non-null   object \n",
      " 11  Hand Tufted       5820 non-null   int64  \n",
      " 12  Durry             5820 non-null   int64  \n",
      " 13  Double Back       5820 non-null   int64  \n",
      " 14  Hand Woven        5820 non-null   int64  \n",
      " 15  Knotted           5820 non-null   int64  \n",
      " 16  Jacquard          5820 non-null   int64  \n",
      " 17  Handloom          5820 non-null   int64  \n",
      " 18  Other             5820 non-null   int64  \n",
      " 19  ShapeName         5820 non-null   object \n",
      " 20  REC               5820 non-null   int64  \n",
      " 21  Round             5820 non-null   int64  \n",
      " 22  Square            5820 non-null   int64  \n",
      " 23  AreaFt            5820 non-null   float64\n",
      " 24  Order Conversion  5820 non-null   int64  \n",
      "dtypes: float64(8), int64(13), object(4)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "DataOnSampleOnly.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b5465cf7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20 entries, 0 to 19\n",
      "Data columns (total 42 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Customer      20 non-null     object\n",
      " 1   Hand Tufted   20 non-null     int64 \n",
      " 2   Double Wowen  20 non-null     int64 \n",
      " 3   Durry         20 non-null     int64 \n",
      " 4   Double Back   20 non-null     int64 \n",
      " 5   Knotted       20 non-null     int64 \n",
      " 6   Jacquared     20 non-null     int64 \n",
      " 7   Handloom      20 non-null     int64 \n",
      " 8   Other         20 non-null     int64 \n",
      " 9   Rectangle     20 non-null     int64 \n",
      " 10  Square        20 non-null     int64 \n",
      " 11  Round         20 non-null     int64 \n",
      " 12  Purple        20 non-null     int64 \n",
      " 13  Gray          20 non-null     int64 \n",
      " 14  Navy          20 non-null     int64 \n",
      " 15  PINK          20 non-null     int64 \n",
      " 16  BLUE          20 non-null     int64 \n",
      " 17  BLUSH PINK    20 non-null     int64 \n",
      " 18  NEUTRAL       20 non-null     int64 \n",
      " 19  TAN           20 non-null     int64 \n",
      " 20  NAVY          20 non-null     int64 \n",
      " 21  Customer .1   20 non-null     object\n",
      " 22  H-2           20 non-null     int64 \n",
      " 23  P-5           20 non-null     int64 \n",
      " 24  M-1           20 non-null     int64 \n",
      " 25  A-9           20 non-null     int64 \n",
      " 26  C-2           20 non-null     int64 \n",
      " 27  JL            20 non-null     int64 \n",
      " 28  N-1           20 non-null     int64 \n",
      " 29  T-5           20 non-null     int64 \n",
      " 30  C-1           20 non-null     int64 \n",
      " 31  T-2           20 non-null     int64 \n",
      " 32  I-2           20 non-null     int64 \n",
      " 33  PD            20 non-null     int64 \n",
      " 34  L-5           20 non-null     int64 \n",
      " 35  M-2           20 non-null     int64 \n",
      " 36  RC            20 non-null     int64 \n",
      " 37  P-4           20 non-null     int64 \n",
      " 38  T-4           20 non-null     int64 \n",
      " 39  PC            20 non-null     int64 \n",
      " 40  A-11          20 non-null     int64 \n",
      " 41  CC            20 non-null     int64 \n",
      "dtypes: int64(40), object(2)\n",
      "memory usage: 6.7+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# We have two other rows that have missing values for unnamed columns in data for recommendation\n",
    "\n",
    "RecommendationData = RecommendationData.dropna(axis = 1, how = 'all')\n",
    "\n",
    "print(RecommendationData.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e161bc23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11 entries, 0 to 10\n",
      "Data columns (total 17 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Order No      11 non-null     int64  \n",
      " 1   BEIGE         11 non-null     int64  \n",
      " 2   CHARCOAL      11 non-null     int64  \n",
      " 3   DENIM         11 non-null     int64  \n",
      " 4   DK GREY       11 non-null     int64  \n",
      " 5   LT TURQUOISE  11 non-null     int64  \n",
      " 6   MID GREY      11 non-null     int64  \n",
      " 7   OLIVE GREEN   11 non-null     int64  \n",
      " 8   Unnamed: 8    0 non-null      float64\n",
      " 9   Order No.1    11 non-null     int64  \n",
      " 10  SKU 1         11 non-null     object \n",
      " 11  SKU 2         5 non-null      object \n",
      " 12  SKU 3         2 non-null      object \n",
      " 13  SKU 4         2 non-null      object \n",
      " 14  SKU 5         2 non-null      object \n",
      " 15  SKU 6         0 non-null      float64\n",
      " 16  SKU 7         0 non-null      float64\n",
      "dtypes: float64(3), int64(9), object(5)\n",
      "memory usage: 1.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# The data seems highly irregular and small for the association rules data evaluating the columns\n",
    "AssocRulesData.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7dfe0fe2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     3314\n",
      "1     3449\n",
      "2     3661\n",
      "3     3662\n",
      "4     3666\n",
      "5     4947\n",
      "6     5443\n",
      "7     5445\n",
      "8     6785\n",
      "9     6790\n",
      "10    7338\n",
      "Name: Order No, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Assoc Rules Column evaluation\n",
    "print(AssocRulesData['Order No'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2bbb60a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing 'Unnamed: 8' column completely\n",
    "AssocRulesData.drop(columns=['Unnamed: 8'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c6c50f29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     3314\n",
      "1     3449\n",
      "2     3661\n",
      "3     3662\n",
      "4     3666\n",
      "5     4947\n",
      "6     5443\n",
      "7     5445\n",
      "8     6785\n",
      "9     6790\n",
      "10    7338\n",
      "Name: Order No.1, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(AssocRulesData['Order No.1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f21e62b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Order No  BEIGE  CHARCOAL  DENIM  DK GREY  LT TURQUOISE  MID GREY  \\\n",
      "0       3314      1         0      0        1             1         1   \n",
      "1       3449      1         0      0        1             1         1   \n",
      "2       3661      1         0      0        1             0         0   \n",
      "3       3662      1         0      0        1             0         0   \n",
      "4       3666      0         0      0        1             0         0   \n",
      "5       4947      1         0      0        1             0         0   \n",
      "6       5443      0         1      0        0             0         0   \n",
      "7       5445      0         1      0        0             0         0   \n",
      "8       6785      0         0      1        0             0         0   \n",
      "9       6790      0         0      1        0             0         0   \n",
      "10      7338      0         0      1        0             0         0   \n",
      "\n",
      "    OLIVE GREEN  Order No.1     SKU 1    SKU 2         SKU 3     SKU 4  \\\n",
      "0             1        3314     BEIGE  DK GREY  LT TURQUOISE  MID GREY   \n",
      "1             1        3449     BEIGE  DK GREY  LT TURQUOISE  MID GREY   \n",
      "2             0        3661     BEIGE  DK GREY  LT TURQUOISE  MID GREY   \n",
      "3             0        3662     BEIGE  DK GREY  LT TURQUOISE  MID GREY   \n",
      "4             0        3666   DK GREY  DK GREY  LT TURQUOISE  MID GREY   \n",
      "5             0        4947     BEIGE  DK GREY  LT TURQUOISE  MID GREY   \n",
      "6             0        5443  CHARCOAL  DK GREY  LT TURQUOISE  MID GREY   \n",
      "7             0        5445  CHARCOAL  DK GREY  LT TURQUOISE  MID GREY   \n",
      "8             0        6785     DENIM  DK GREY  LT TURQUOISE  MID GREY   \n",
      "9             0        6790     DENIM  DK GREY  LT TURQUOISE  MID GREY   \n",
      "10            0        7338     DENIM  DK GREY  LT TURQUOISE  MID GREY   \n",
      "\n",
      "          SKU 5  SKU 6  SKU 7  \n",
      "0   OLIVE GREEN    NaN    NaN  \n",
      "1   OLIVE GREEN    NaN    NaN  \n",
      "2   OLIVE GREEN    NaN    NaN  \n",
      "3   OLIVE GREEN    NaN    NaN  \n",
      "4   OLIVE GREEN    NaN    NaN  \n",
      "5   OLIVE GREEN    NaN    NaN  \n",
      "6   OLIVE GREEN    NaN    NaN  \n",
      "7   OLIVE GREEN    NaN    NaN  \n",
      "8   OLIVE GREEN    NaN    NaN  \n",
      "9   OLIVE GREEN    NaN    NaN  \n",
      "10  OLIVE GREEN    NaN    NaN  \n"
     ]
    }
   ],
   "source": [
    "# Observing through the SKU columns data\n",
    "# Copy the SKU columns\n",
    "sku_columns = AssocRulesData[['SKU 1', 'SKU 2', 'SKU 3', 'SKU 4', 'SKU 5', 'SKU 6', 'SKU 7']].copy()\n",
    "\n",
    "# Apply forward fill\n",
    "sku_columns.fillna(method='ffill', inplace=True)\n",
    "\n",
    "# Apply backward fill\n",
    "sku_columns.fillna(method='bfill', inplace=True)\n",
    "\n",
    "# Attach the cleaned SKU columns back to the original DataFrame\n",
    "AssocRulesData[['SKU 1', 'SKU 2', 'SKU 3', 'SKU 4', 'SKU 5', 'SKU 6', 'SKU 7']] = sku_columns\n",
    "\n",
    "print(AssocRulesData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ed819ae7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi-Square Statsitic between Order Type and Item Name is: 352.2796532945555\n",
      "P-value between Order Type and Item Name is: 7.982178979445792e-69\n",
      "Degrees of Freesom between Order Type and Item Name is: 11\n",
      "frequency between Order Type and Item Name is: [[2.99783698e+00 1.85416217e+03 3.26389501e+03 6.82007913e+01\n",
      "  5.31741335e+03 2.67556951e+02 1.74624004e+03 8.24405170e+00\n",
      "  3.57492060e+02 1.18039831e+03 1.07922131e+02 3.14772883e+01]\n",
      " [1.00216302e+00 6.19837826e+02 1.09110499e+03 2.27992087e+01\n",
      "  1.77758665e+03 8.94430493e+01 5.83759958e+02 2.75594830e+00\n",
      "  1.19507940e+02 3.94601688e+02 3.60778686e+01 1.05227117e+01]]\n"
     ]
    }
   ],
   "source": [
    "# Perform Chi-Square Tests\n",
    "\n",
    "orderType_itemName_cTab = pd.crosstab(RawDataOrder['OrderType'], RawDataOrder['ITEM_NAME'])\n",
    "\n",
    "chi2Val, pVal, dofVal, expectedVal = chitest(orderType_itemName_cTab)\n",
    "\n",
    "print(f\"Chi-Square Statsitic between Order Type and Item Name is: {chi2Val}\")\n",
    "print(f\"P-value between Order Type and Item Name is: {pVal}\")\n",
    "print(f\"Degrees of Freesom between Order Type and Item Name is: {dofVal}\")\n",
    "print(f\"frequency between Order Type and Item Name is: {expectedVal}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "803dae6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Variable Pair  Chi2 Statistic  P-value  Degrees of Freedom\n",
      "33    DesignName vs ColorName    6.514634e+06      0.0             1833942\n",
      "30  QualityName vs DesignName    4.829016e+06      0.0              858393\n",
      "31   QualityName vs ColorName    1.375436e+06      0.0              310134\n"
     ]
    }
   ],
   "source": [
    "# Performing chi square tests on all Categorical variables and seeing the top 3 values \n",
    "\n",
    "categorical_columns = ['OrderType', 'OrderCategory','CustomerCode', 'CountryName','ITEM_NAME', 'QualityName', 'DesignName', 'ColorName', 'ShapeName']  # Add more if needed\n",
    "\n",
    "# List to store results of Chi-Square tests\n",
    "chi_square_results = []\n",
    "\n",
    "# Loop through all pairs of categorical columns\n",
    "for i in range(len(categorical_columns)):\n",
    "    for j in range(i + 1, len(categorical_columns)):\n",
    "        # Create a cross-tabulation for the two columns\n",
    "        cross_tab = pd.crosstab(RawDataOrder[categorical_columns[i]], RawDataOrder[categorical_columns[j]])\n",
    "        \n",
    "        # Perform the Chi-Square test\n",
    "        chi2, p, dof, expected = chitest(cross_tab)\n",
    "        \n",
    "        # Store the results (Chi-Square statistic, p-value, degrees of freedom, and the variable pair)\n",
    "        chi_square_results.append({\n",
    "            'Variable Pair': f\"{categorical_columns[i]} vs {categorical_columns[j]}\",\n",
    "            'Chi2 Statistic': chi2,\n",
    "            'P-value': p,\n",
    "            'Degrees of Freedom': dof\n",
    "        })\n",
    "\n",
    "# Convert results to a DataFrame for easier sorting and viewing\n",
    "chi_square_df = pd.DataFrame(chi_square_results)\n",
    "\n",
    "# Sort by Chi-Square statistic or P-value (depending on what you want to prioritize)\n",
    "# Sort by Chi-Square statistic (descending) to find the strongest associations\n",
    "top_chi_square_pairs = chi_square_df.sort_values(by='Chi2 Statistic', ascending=False).head(3)\n",
    "\n",
    "# Display the top 3 pairs with the strongest association\n",
    "print(top_chi_square_pairs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8f975915",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 3 correlated pairs:\n",
      "AreaFt - TotalArea: 0.8442\n",
      "Amount - QtyRequired: 0.1168\n",
      "Amount - TotalArea: 0.0828\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Performing chi square tests on all Numerical variables and seeing the top 3 values \n",
    "\n",
    "numeric_cols = ['QtyRequired', 'TotalArea', 'Amount', 'AreaFt']\n",
    "\n",
    "numeric_cols = [col for col in numeric_cols if col in RawDataOrder.columns]\n",
    "\n",
    "# Compute correlation matrix\n",
    "corr_matrix = RawDataOrder[numeric_cols].corr()\n",
    "\n",
    "# Get upper triangle of correlation matrix\n",
    "upper_tri = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n",
    "\n",
    "# Find the top 3 correlations\n",
    "top_correlations = upper_tri.unstack().sort_values(kind=\"quicksort\", ascending=False)[:3]\n",
    "\n",
    "# Print top 3 correlations\n",
    "print(\"Top 3 correlated pairs:\")\n",
    "for (col1, col2), corr in top_correlations.items():\n",
    "    print(f\"{col1} - {col2}: {corr:.4f}\")\n",
    "\n",
    "# Visualize top 3 correlations\n",
    "fig, axes = plt.subplots(1, 3, figsize=(18, 6))\n",
    "fig.suptitle(\"Top 3 Correlated Pairs\", fontsize=16)\n",
    "\n",
    "for i, ((col1, col2), corr) in enumerate(top_correlations.items()):\n",
    "    sns.scatterplot(data=RawDataOrder.sample(1000), x=col1, y=col2, ax=axes[i])  # Sample 1000 points for faster plotting\n",
    "    axes[i].set_title(f\"{col1} vs {col2}\\nCorrelation: {corr:.4f}\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Correlation matrix heatmap\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', vmin=-1, vmax=1, center=0, fmt='.2f')\n",
    "plt.title(\"Correlation Matrix\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "795936f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identify categorical and numerical columns\n",
    "categorical_columns = RawDataOrder.select_dtypes(include=['object', 'category']).columns.tolist()\n",
    "numerical_columns = RawDataOrder.select_dtypes(include=[np.number]).columns.tolist()\n",
    "\n",
    "# List to store results of statistical tests\n",
    "test_results = []\n",
    "\n",
    "# Function to check if variance is too low (i.e., nearly identical values)\n",
    "def has_low_variance(series):\n",
    "    return series.var() < 1e-6  # Threshold for low variance\n",
    "\n",
    "# Loop through all combinations of categorical and numerical columns\n",
    "for cat_col in categorical_columns:\n",
    "    for num_col in numerical_columns:\n",
    "        # Check if the categorical variable is binary or multi-level\n",
    "        if RawDataOrder[cat_col].nunique() == 2:\n",
    "            # Perform T-Test for binary categorical variables\n",
    "            group1 = RawDataOrder[RawDataOrder[cat_col] == RawDataOrder[cat_col].unique()[0]][num_col]\n",
    "            group2 = RawDataOrder[RawDataOrder[cat_col] == RawDataOrder[cat_col].unique()[1]][num_col]\n",
    "            \n",
    "            # Skip if either group has low variance\n",
    "            if has_low_variance(group1) or has_low_variance(group2):\n",
    "                continue\n",
    "            \n",
    "            t_stat, p_value = stats.ttest_ind(group1, group2)\n",
    "            test_type = 'T-Test'\n",
    "        else:\n",
    "            # Perform ANOVA for multi-level categorical variables\n",
    "            groups = [RawDataOrder[RawDataOrder[cat_col] == category][num_col] for category in RawDataOrder[cat_col].unique()]\n",
    "            \n",
    "            # Skip if any group has low variance\n",
    "            if any(has_low_variance(group) for group in groups):\n",
    "                continue\n",
    "            \n",
    "            f_stat, p_value = stats.f_oneway(*groups)\n",
    "            test_type = 'ANOVA'\n",
    "        \n",
    "        # Store the results\n",
    "        test_results.append({\n",
    "            'Categorical Variable': cat_col,\n",
    "            'Numerical Variable': num_col,\n",
    "            'P-value': p_value,\n",
    "            'Test Type': test_type\n",
    "        })\n",
    "\n",
    "# Convert results to a DataFrame for easier sorting and viewing\n",
    "results_df = pd.DataFrame(test_results)\n",
    "\n",
    "# Sort by P-value to find the top 3 pairs with the strongest association (smallest p-values)\n",
    "top_3_pairs = results_df.sort_values(by='P-value').head(3)\n",
    "\n",
    "# Display the top 3 pairs with their respective test type and p-values\n",
    "print(top_3_pairs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32d291ad",
   "metadata": {},
   "source": [
    "# Analysis of Top 3 Relationships\n",
    "\n",
    "In this analysis, we explore the top 3 relationships across **Categorical-Categorical**, **Numerical-Numerical**, and **Categorical-Numerical** variable pairs based on statistical tests and correlation measures.\n",
    "\n",
    "---\n",
    "\n",
    "## 1. Top 3 Categorical-Categorical Pairs\n",
    "\n",
    "The following table lists the top 3 pairs of categorical variables based on the Chi-Square test results:\n",
    "\n",
    "| **Variable Pair**                | **Chi2 Statistic** | **P-value** | **Degrees of Freedom** |\n",
    "|----------------------------------|-------------------|-------------|------------------------|\n",
    "| **DesignName vs ColorName**      | 6.514634e+06      | 0.0         | 1,833,942              |\n",
    "| **QualityName vs DesignName**    | 4.829016e+06      | 0.0         | 858,393                |\n",
    "| **QualityName vs ColorName**     | 1.375436e+06      | 0.0         | 310,134                |\n",
    "\n",
    "These pairs show a strong association between the respective categorical variables.\n",
    "\n",
    "---\n",
    "\n",
    "## 2. Top 3 Numerical-Numerical Pairs\n",
    "\n",
    "The following table lists the top 3 correlated numerical variable pairs based on Pearson's correlation coefficient:\n",
    "\n",
    "| **Numerical Pair**               | **Correlation Coefficient** |\n",
    "|----------------------------------|-----------------------------|\n",
    "| **AreaFt vs TotalArea**          | 0.8442                      |\n",
    "| **Amount vs QtyRequired**        | 0.1168                      |\n",
    "| **Amount vs TotalArea**          | 0.0828                      |\n",
    "\n",
    "These correlations indicate the strength and direction of linear relationships between numerical variables.\n",
    "\n",
    "---\n",
    "\n",
    "## 3. Top 3 Categorical-Numerical Pairs\n",
    "\n",
    "The following table lists the top 3 relationships between categorical and numerical variables based on statistical tests (T-Test or ANOVA):\n",
    "\n",
    "| **Categorical Variable**         | **Numerical Variable**       | **P-value**          | **Test Type** |\n",
    "|----------------------------------|------------------------------|----------------------|---------------|\n",
    "| **OrderCategory**                | TotalArea                    | 0.000000e+00         | T-Test        |\n",
    "| **OrderCategory**                | AreaFt                       | 0.000000e+00         | T-Test        |\n",
    "| **ITEM_NAME**                    | AreaFt                       | 7.503166e-261        | ANOVA         |\n",
    "\n",
    "These results show significant relationships between categorical and numerical variables.\n",
    "\n",
    "---\n",
    "\n",
    "### Conclusion\n",
    "\n",
    "- The Chi-Square test results for categorical-categorical pairs indicate strong associations between design names, quality names, and color names.\n",
    "- The Pearson correlation coefficients for numerical-numerical pairs reveal a strong positive correlation between `AreaFt` and `TotalArea`.\n",
    "- The T-Test and ANOVA results for categorical-numerical pairs highlight significant differences in `TotalArea` and `AreaFt` across different categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "621c01bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let us create some visualizations \n",
    "plt.figure(figsize=(10, 6))\n",
    "RawDataOrder['OrderType'].value_counts().plot(kind='bar')\n",
    "plt.title('Distribution of Order Type')\n",
    "plt.xlabel('OrderType')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "287bdf17",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# After Observing in the previous data I observe two order types\n",
    "# Area Wise and PC Wise trying to test the correlation between Item name and Order Type\n",
    "\n",
    "#Creating an cross tab for Item name and order type\n",
    "CrossTab = pd.crosstab(RawDataOrder['OrderType'], RawDataOrder['ITEM_NAME'])\n",
    "\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.heatmap(CrossTab, annot=True, fmt='d', cmap='YlOrRd', cbar_kws={'label': 'Count'})\n",
    "plt.title('Correlation Heatmap between Order Type and Item-names that were brought by the customers')\n",
    "plt.show()\n",
    "\n",
    "# So from the heatmap we can make out that Champo Carpets can actually stop producing some types of\n",
    "# items like Gun_Tufted, INDO-TIBETIAN, TABLE TUFTED. They can also remove HANDLOOM, JACQUARD as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "94df9369",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating an cross tab for Item name, Quality Name, Design name, Color and order type\n",
    "# Concatenate categorical variables into a single column\n",
    "# Get the top 5 counts for each variable\n",
    "items_top_5 = RawDataOrder['ITEM_NAME'].value_counts().nlargest(5).index\n",
    "quality_top_5 = RawDataOrder['QualityName'].value_counts().nlargest(5).index\n",
    "\n",
    "\n",
    "# Filter the DataFrame to keep only the top 5 entries for each category\n",
    "filtered_df = RawDataOrder[\n",
    "    (RawDataOrder['ITEM_NAME'].isin(items_top_5)) & \n",
    "    (RawDataOrder['QualityName'].isin(quality_top_5)) \n",
    "\n",
    "]\n",
    "\n",
    "# Create a cross-tabulation for the filtered data\n",
    "cross_tab_filtered = pd.crosstab(\n",
    "    filtered_df['OrderType'], \n",
    "    filtered_df['ITEM_NAME'] + ' | ' + \n",
    "    filtered_df['QualityName'] \n",
    ")\n",
    "\n",
    "# Plot the heatmap for the filtered data\n",
    "plt.figure(figsize=(14, 12))\n",
    "sns.heatmap(cross_tab_filtered, annot=True, fmt='d', cmap='YlOrRd', cbar_kws={'label': 'Count'})\n",
    "plt.title('Heatmap of Order Type vs Combined Categories Item Name and Quality Name')\n",
    "plt.xlabel('Combined Categories - Item Name and Quality Name ')\n",
    "plt.ylabel('Order Type')\n",
    "plt.xticks(rotation=90, ha='right')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2e2f60db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 317402798.3984069\n",
      "R-squared: 0.0035149637000425615\n"
     ]
    }
   ],
   "source": [
    "# Applying Linear Regression on the ReData \n",
    "X = DataOrderOnly['TotalArea'].values\n",
    "Y = DataOrderOnly['Amount'].values\n",
    "X = X.reshape(-1, 1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Creating custom linear regression object\n",
    "model = LinearRegression()\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Mean Squared Error: {mse}\")\n",
    "print(f\"R-squared: {r2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "cd40b3a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "plt.subplot(121)\n",
    "plt.hist(DataOrderOnly['TotalArea'])\n",
    "plt.title('Distribution of TotalArea')\n",
    "plt.subplot(122)\n",
    "plt.hist(DataOrderOnly['Amount'])\n",
    "plt.title('Distribution of Amount')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9e74e3bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(DataOrderOnly['TotalArea'], DataOrderOnly['Amount'])\n",
    "plt.xlabel('TotalArea')\n",
    "plt.ylabel('Amount')\n",
    "plt.title('TotalArea vs Amount')\n",
    "plt.show()\n",
    "\n",
    "# The below graph shows how the pricing strategy is not so good from champo carpets \n",
    "#trying to find out the relationship between Total Area and amount to see how cost of making the carpet is affecting their sales and how much they are spending extra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5e4ee8fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 4.016929413574044\n",
      "R-squared: 0.05098532070423256\n"
     ]
    }
   ],
   "source": [
    "# Applying log transformation and seeing if mean squared error reduces\n",
    "\n",
    "# Log transformation\n",
    "DataOrderOnly['LogTotalArea'] = np.log1p(DataOrderOnly['TotalArea'])\n",
    "DataOrderOnly['LogAmount'] = np.log1p(DataOrderOnly['Amount'])\n",
    "\n",
    "# Use log-transformed features\n",
    "X = DataOrderOnly['LogTotalArea'].values.reshape(-1, 1)\n",
    "Y = DataOrderOnly['LogAmount'].values\n",
    "\n",
    "# Train-test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Creating linear regression model\n",
    "model = LinearRegression()\n",
    "\n",
    "# Fit model\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Predictions\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Calculate metrics\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Mean Squared Error: {mse}\")\n",
    "print(f\"R-squared: {r2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e2f9a323",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 3.4349848842413935\n",
      "R-squared: 0.18847190411451165\n"
     ]
    }
   ],
   "source": [
    "# The relationship between Total Area and Amount seems to be highly skewed and non-linear\n",
    "#So trying to implement decsision trees to see the relationship to provide recommendations\n",
    "\n",
    "# Create and fit the Decision Tree model\n",
    "tree_model = DecisionTreeRegressor(random_state=42)\n",
    "tree_model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "y_pred = tree_model.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Mean Squared Error: {mse}\")\n",
    "print(f\"R-squared: {r2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1dfac2aa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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