Predictive Model building.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "66b58d43",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ee5d290c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('C:/Users/kruna/Downloads/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "227e549f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>08/11/2017</td>\n",
       "      <td>11/11/2017</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>08/11/2017</td>\n",
       "      <td>11/11/2017</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CA-2017-138688</td>\n",
       "      <td>12/06/2017</td>\n",
       "      <td>16/06/2017</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>11/10/2016</td>\n",
       "      <td>18/10/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>11/10/2016</td>\n",
       "      <td>18/10/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>9795</th>\n",
       "      <td>9796</td>\n",
       "      <td>CA-2017-125920</td>\n",
       "      <td>21/05/2017</td>\n",
       "      <td>28/05/2017</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SH-19975</td>\n",
       "      <td>Sally Hughsby</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>60610.0</td>\n",
       "      <td>Central</td>\n",
       "      <td>OFF-BI-10003429</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Cardinal HOLDit! Binder Insert Strips,Extra St...</td>\n",
       "      <td>3.7980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9796</th>\n",
       "      <td>9797</td>\n",
       "      <td>CA-2016-128608</td>\n",
       "      <td>12/01/2016</td>\n",
       "      <td>17/01/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>CS-12490</td>\n",
       "      <td>Cindy Schnelling</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Toledo</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>43615.0</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-AR-10001374</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Art</td>\n",
       "      <td>BIC Brite Liner Highlighters, Chisel Tip</td>\n",
       "      <td>10.3680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9797</th>\n",
       "      <td>9798</td>\n",
       "      <td>CA-2016-128608</td>\n",
       "      <td>12/01/2016</td>\n",
       "      <td>17/01/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>CS-12490</td>\n",
       "      <td>Cindy Schnelling</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Toledo</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>43615.0</td>\n",
       "      <td>East</td>\n",
       "      <td>TEC-PH-10004977</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>GE 30524EE4</td>\n",
       "      <td>235.1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9798</th>\n",
       "      <td>9799</td>\n",
       "      <td>CA-2016-128608</td>\n",
       "      <td>12/01/2016</td>\n",
       "      <td>17/01/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>CS-12490</td>\n",
       "      <td>Cindy Schnelling</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Toledo</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>43615.0</td>\n",
       "      <td>East</td>\n",
       "      <td>TEC-PH-10000912</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>Anker 24W Portable Micro USB Car Charger</td>\n",
       "      <td>26.3760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9799</th>\n",
       "      <td>9800</td>\n",
       "      <td>CA-2016-128608</td>\n",
       "      <td>12/01/2016</td>\n",
       "      <td>17/01/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>CS-12490</td>\n",
       "      <td>Cindy Schnelling</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Toledo</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>43615.0</td>\n",
       "      <td>East</td>\n",
       "      <td>TEC-AC-10000487</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>SanDisk Cruzer 4 GB USB Flash Drive</td>\n",
       "      <td>10.3840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9800 rows × 18 columns</p>\n",
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      ],
      "text/plain": [
       "      Row ID        Order ID  Order Date   Ship Date       Ship Mode  \\\n",
       "0          1  CA-2017-152156  08/11/2017  11/11/2017    Second Class   \n",
       "1          2  CA-2017-152156  08/11/2017  11/11/2017    Second Class   \n",
       "2          3  CA-2017-138688  12/06/2017  16/06/2017    Second Class   \n",
       "3          4  US-2016-108966  11/10/2016  18/10/2016  Standard Class   \n",
       "4          5  US-2016-108966  11/10/2016  18/10/2016  Standard Class   \n",
       "...      ...             ...         ...         ...             ...   \n",
       "9795    9796  CA-2017-125920  21/05/2017  28/05/2017  Standard Class   \n",
       "9796    9797  CA-2016-128608  12/01/2016  17/01/2016  Standard Class   \n",
       "9797    9798  CA-2016-128608  12/01/2016  17/01/2016  Standard Class   \n",
       "9798    9799  CA-2016-128608  12/01/2016  17/01/2016  Standard Class   \n",
       "9799    9800  CA-2016-128608  12/01/2016  17/01/2016  Standard Class   \n",
       "\n",
       "     Customer ID     Customer Name    Segment        Country             City  \\\n",
       "0       CG-12520       Claire Gute   Consumer  United States        Henderson   \n",
       "1       CG-12520       Claire Gute   Consumer  United States        Henderson   \n",
       "2       DV-13045   Darrin Van Huff  Corporate  United States      Los Angeles   \n",
       "3       SO-20335    Sean O'Donnell   Consumer  United States  Fort Lauderdale   \n",
       "4       SO-20335    Sean O'Donnell   Consumer  United States  Fort Lauderdale   \n",
       "...          ...               ...        ...            ...              ...   \n",
       "9795    SH-19975     Sally Hughsby  Corporate  United States          Chicago   \n",
       "9796    CS-12490  Cindy Schnelling  Corporate  United States           Toledo   \n",
       "9797    CS-12490  Cindy Schnelling  Corporate  United States           Toledo   \n",
       "9798    CS-12490  Cindy Schnelling  Corporate  United States           Toledo   \n",
       "9799    CS-12490  Cindy Schnelling  Corporate  United States           Toledo   \n",
       "\n",
       "           State  Postal Code   Region       Product ID         Category  \\\n",
       "0       Kentucky      42420.0    South  FUR-BO-10001798        Furniture   \n",
       "1       Kentucky      42420.0    South  FUR-CH-10000454        Furniture   \n",
       "2     California      90036.0     West  OFF-LA-10000240  Office Supplies   \n",
       "3        Florida      33311.0    South  FUR-TA-10000577        Furniture   \n",
       "4        Florida      33311.0    South  OFF-ST-10000760  Office Supplies   \n",
       "...          ...          ...      ...              ...              ...   \n",
       "9795    Illinois      60610.0  Central  OFF-BI-10003429  Office Supplies   \n",
       "9796        Ohio      43615.0     East  OFF-AR-10001374  Office Supplies   \n",
       "9797        Ohio      43615.0     East  TEC-PH-10004977       Technology   \n",
       "9798        Ohio      43615.0     East  TEC-PH-10000912       Technology   \n",
       "9799        Ohio      43615.0     East  TEC-AC-10000487       Technology   \n",
       "\n",
       "     Sub-Category                                       Product Name     Sales  \n",
       "0       Bookcases                  Bush Somerset Collection Bookcase  261.9600  \n",
       "1          Chairs  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400  \n",
       "2          Labels  Self-Adhesive Address Labels for Typewriters b...   14.6200  \n",
       "3          Tables      Bretford CR4500 Series Slim Rectangular Table  957.5775  \n",
       "4         Storage                     Eldon Fold 'N Roll Cart System   22.3680  \n",
       "...           ...                                                ...       ...  \n",
       "9795      Binders  Cardinal HOLDit! Binder Insert Strips,Extra St...    3.7980  \n",
       "9796          Art           BIC Brite Liner Highlighters, Chisel Tip   10.3680  \n",
       "9797       Phones                                        GE 30524EE4  235.1880  \n",
       "9798       Phones           Anker 24W Portable Micro USB Car Charger   26.3760  \n",
       "9799  Accessories                SanDisk Cruzer 4 GB USB Flash Drive   10.3840  \n",
       "\n",
       "[9800 rows x 18 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "data"
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   "execution_count": 11,
   "id": "5b15bea4",
   "metadata": {},
   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9800 entries, 0 to 9799\n",
      "Data columns (total 18 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Row ID         9800 non-null   int64  \n",
      " 1   Order ID       9800 non-null   object \n",
      " 2   Order Date     9800 non-null   object \n",
      " 3   Ship Date      9800 non-null   object \n",
      " 4   Ship Mode      9800 non-null   object \n",
      " 5   Customer ID    9800 non-null   object \n",
      " 6   Customer Name  9800 non-null   object \n",
      " 7   Segment        9800 non-null   object \n",
      " 8   Country        9800 non-null   object \n",
      " 9   City           9800 non-null   object \n",
      " 10  State          9800 non-null   object \n",
      " 11  Postal Code    9789 non-null   float64\n",
      " 12  Region         9800 non-null   object \n",
      " 13  Product ID     9800 non-null   object \n",
      " 14  Category       9800 non-null   object \n",
      " 15  Sub-Category   9800 non-null   object \n",
      " 16  Product Name   9800 non-null   object \n",
      " 17  Sales          9800 non-null   float64\n",
      "dtypes: float64(2), int64(1), object(15)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad932e7a",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fc2fe973",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "def encode_dates(df, column):\n",
    "    df = df.copy()\n",
    "    df[column] = pd.to_datetime(df[column])\n",
    "    df[column + '_year'] = df[column].apply(lambda x: x.year)\n",
    "    df[column + '_month'] = df[column].apply(lambda x: x.month)\n",
    "    df[column + '_day'] = df[column].apply(lambda x: x.day)\n",
    "    df = df.drop(column, axis=1)\n",
    "    return df\n",
    "\n",
    "def onehot_encode(df, column):\n",
    "    df = df.copy()\n",
    "    dummies = pd.get_dummies(df[column], prefix=column)\n",
    "    df = pd.concat([df, dummies], axis=1)\n",
    "    df = df.drop(column, axis=1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0b7fac5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Over here I am trying to build a model which would predict sales without taking into consideration which customer is buying a product.\n",
    "# We would be dropping unnecessary columns like Row Id, Customer Id , Customer Name , Product Name, Country. ( we have droped Country because it has only one value.) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d23cf330",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_inputs(df):\n",
    "    df = df.copy()\n",
    "    \n",
    "    # Drop unnecessary columns\n",
    "    df = df.drop(['Row ID', 'Customer Name', 'Country', 'Product Name'], axis=1)\n",
    "    \n",
    "    # Drop customer-specific feature columns\n",
    "    df = df.drop(['Order ID', 'Customer ID'], axis=1)\n",
    "    \n",
    "    # Extract date features\n",
    "    df = encode_dates(df, column='Order Date')\n",
    "    df = encode_dates(df, column='Ship Date')\n",
    "    \n",
    "    # One-hot encode categorical features\n",
    "    for column in ['Ship Mode', 'Segment', 'City', 'State', 'Postal Code', 'Region', 'Product ID', 'Category', 'Sub-Category']:\n",
    "        df = onehot_encode(df, column=column)\n",
    "    \n",
    "    # Split df into X and y\n",
    "    y = df['Sales']\n",
    "    X = df.drop('Sales', axis=1)\n",
    "    \n",
    "    # Train-test split\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, shuffle=True, random_state=1)\n",
    "    \n",
    "    # Scale X\n",
    "    scaler = StandardScaler()\n",
    "    scaler.fit(X_train)\n",
    "    X_train = pd.DataFrame(scaler.transform(X_train), columns=X.columns)\n",
    "    X_test = pd.DataFrame(scaler.transform(X_test), columns=X.columns)\n",
    "    \n",
    "    return X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2a8a54d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = preprocess_inputs(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1182640f",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order Date_year</th>\n",
       "      <th>Order Date_month</th>\n",
       "      <th>Order Date_day</th>\n",
       "      <th>Ship Date_year</th>\n",
       "      <th>Ship Date_month</th>\n",
       "      <th>Ship Date_day</th>\n",
       "      <th>Ship Mode_First Class</th>\n",
       "      <th>Ship Mode_Same Day</th>\n",
       "      <th>Ship Mode_Second Class</th>\n",
       "      <th>Ship Mode_Standard Class</th>\n",
       "      <th>...</th>\n",
       "      <th>Sub-Category_Envelopes</th>\n",
       "      <th>Sub-Category_Fasteners</th>\n",
       "      <th>Sub-Category_Furnishings</th>\n",
       "      <th>Sub-Category_Labels</th>\n",
       "      <th>Sub-Category_Machines</th>\n",
       "      <th>Sub-Category_Paper</th>\n",
       "      <th>Sub-Category_Phones</th>\n",
       "      <th>Sub-Category_Storage</th>\n",
       "      <th>Sub-Category_Supplies</th>\n",
       "      <th>Sub-Category_Tables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.153020</td>\n",
       "      <td>-0.351009</td>\n",
       "      <td>-0.142358</td>\n",
       "      <td>1.138089</td>\n",
       "      <td>-0.387963</td>\n",
       "      <td>0.315968</td>\n",
       "      <td>-0.429885</td>\n",
       "      <td>-0.249166</td>\n",
       "      <td>-0.484918</td>\n",
       "      <td>0.824704</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.162742</td>\n",
       "      <td>-0.145387</td>\n",
       "      <td>-0.326269</td>\n",
       "      <td>-0.187924</td>\n",
       "      <td>-0.108625</td>\n",
       "      <td>-0.400194</td>\n",
       "      <td>-0.314653</td>\n",
       "      <td>-0.30333</td>\n",
       "      <td>-0.13789</td>\n",
       "      <td>-0.179443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.626244</td>\n",
       "      <td>0.817035</td>\n",
       "      <td>-0.389593</td>\n",
       "      <td>-0.638920</td>\n",
       "      <td>0.792139</td>\n",
       "      <td>0.076408</td>\n",
       "      <td>-0.429885</td>\n",
       "      <td>-0.249166</td>\n",
       "      <td>-0.484918</td>\n",
       "      <td>0.824704</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.162742</td>\n",
       "      <td>-0.145387</td>\n",
       "      <td>-0.326269</td>\n",
       "      <td>-0.187924</td>\n",
       "      <td>-0.108625</td>\n",
       "      <td>-0.400194</td>\n",
       "      <td>-0.314653</td>\n",
       "      <td>-0.30333</td>\n",
       "      <td>-0.13789</td>\n",
       "      <td>-0.179443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.153020</td>\n",
       "      <td>-1.811064</td>\n",
       "      <td>-1.131297</td>\n",
       "      <td>1.138089</td>\n",
       "      <td>0.202088</td>\n",
       "      <td>-1.121394</td>\n",
       "      <td>-0.429885</td>\n",
       "      <td>-0.249166</td>\n",
       "      <td>-0.484918</td>\n",
       "      <td>0.824704</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.162742</td>\n",
       "      <td>-0.145387</td>\n",
       "      <td>-0.326269</td>\n",
       "      <td>-0.187924</td>\n",
       "      <td>-0.108625</td>\n",
       "      <td>2.498785</td>\n",
       "      <td>-0.314653</td>\n",
       "      <td>-0.30333</td>\n",
       "      <td>-0.13789</td>\n",
       "      <td>-0.179443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.626244</td>\n",
       "      <td>1.109046</td>\n",
       "      <td>0.475728</td>\n",
       "      <td>-0.638920</td>\n",
       "      <td>1.087165</td>\n",
       "      <td>1.034650</td>\n",
       "      <td>-0.429885</td>\n",
       "      <td>-0.249166</td>\n",
       "      <td>-0.484918</td>\n",
       "      <td>0.824704</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.162742</td>\n",
       "      <td>-0.145387</td>\n",
       "      <td>-0.326269</td>\n",
       "      <td>-0.187924</td>\n",
       "      <td>-0.108625</td>\n",
       "      <td>-0.400194</td>\n",
       "      <td>-0.314653</td>\n",
       "      <td>-0.30333</td>\n",
       "      <td>-0.13789</td>\n",
       "      <td>-0.179443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.153020</td>\n",
       "      <td>-0.643020</td>\n",
       "      <td>-0.884062</td>\n",
       "      <td>1.138089</td>\n",
       "      <td>0.497114</td>\n",
       "      <td>-0.881834</td>\n",
       "      <td>-0.429885</td>\n",
       "      <td>-0.249166</td>\n",
       "      <td>-0.484918</td>\n",
       "      <td>0.824704</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.162742</td>\n",
       "      <td>-0.145387</td>\n",
       "      <td>-0.326269</td>\n",
       "      <td>-0.187924</td>\n",
       "      <td>-0.108625</td>\n",
       "      <td>2.498785</td>\n",
       "      <td>-0.314653</td>\n",
       "      <td>-0.30333</td>\n",
       "      <td>-0.13789</td>\n",
       "      <td>-0.179443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6855</th>\n",
       "      <td>1.153020</td>\n",
       "      <td>0.817035</td>\n",
       "      <td>0.846580</td>\n",
       "      <td>1.138089</td>\n",
       "      <td>0.792139</td>\n",
       "      <td>1.513771</td>\n",
       "      <td>-0.429885</td>\n",
       "      <td>-0.249166</td>\n",
       "      <td>-0.484918</td>\n",
       "      <td>0.824704</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.162742</td>\n",
       "      <td>-0.145387</td>\n",
       "      <td>-0.326269</td>\n",
       "      <td>-0.187924</td>\n",
       "      <td>-0.108625</td>\n",
       "      <td>-0.400194</td>\n",
       "      <td>3.178101</td>\n",
       "      <td>-0.30333</td>\n",
       "      <td>-0.13789</td>\n",
       "      <td>-0.179443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6856</th>\n",
       "      <td>0.263388</td>\n",
       "      <td>-1.519053</td>\n",
       "      <td>-0.265975</td>\n",
       "      <td>0.249584</td>\n",
       "      <td>-1.568065</td>\n",
       "      <td>-0.163153</td>\n",
       "      <td>2.326202</td>\n",
       "      <td>-0.249166</td>\n",
       "      <td>-0.484918</td>\n",
       "      <td>-1.212556</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.162742</td>\n",
       "      <td>-0.145387</td>\n",
       "      <td>3.064953</td>\n",
       "      <td>-0.187924</td>\n",
       "      <td>-0.108625</td>\n",
       "      <td>-0.400194</td>\n",
       "      <td>-0.314653</td>\n",
       "      <td>-0.30333</td>\n",
       "      <td>-0.13789</td>\n",
       "      <td>-0.179443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6857</th>\n",
       "      <td>0.263388</td>\n",
       "      <td>0.233013</td>\n",
       "      <td>-1.502149</td>\n",
       "      <td>0.249584</td>\n",
       "      <td>1.382191</td>\n",
       "      <td>-1.480735</td>\n",
       "      <td>-0.429885</td>\n",
       "      <td>-0.249166</td>\n",
       "      <td>-0.484918</td>\n",
       "      <td>0.824704</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.162742</td>\n",
       "      <td>-0.145387</td>\n",
       "      <td>-0.326269</td>\n",
       "      <td>-0.187924</td>\n",
       "      <td>-0.108625</td>\n",
       "      <td>2.498785</td>\n",
       "      <td>-0.314653</td>\n",
       "      <td>-0.30333</td>\n",
       "      <td>-0.13789</td>\n",
       "      <td>-0.179443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6858</th>\n",
       "      <td>-1.515875</td>\n",
       "      <td>1.109046</td>\n",
       "      <td>1.464667</td>\n",
       "      <td>-1.527425</td>\n",
       "      <td>-1.863091</td>\n",
       "      <td>-0.522493</td>\n",
       "      <td>-0.429885</td>\n",
       "      <td>-0.249166</td>\n",
       "      <td>2.062203</td>\n",
       "      <td>-1.212556</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.162742</td>\n",
       "      <td>-0.145387</td>\n",
       "      <td>3.064953</td>\n",
       "      <td>-0.187924</td>\n",
       "      <td>-0.108625</td>\n",
       "      <td>-0.400194</td>\n",
       "      <td>-0.314653</td>\n",
       "      <td>-0.30333</td>\n",
       "      <td>-0.13789</td>\n",
       "      <td>-0.179443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6859</th>\n",
       "      <td>1.153020</td>\n",
       "      <td>-0.058998</td>\n",
       "      <td>-1.502149</td>\n",
       "      <td>1.138089</td>\n",
       "      <td>1.382191</td>\n",
       "      <td>-1.480735</td>\n",
       "      <td>-0.429885</td>\n",
       "      <td>-0.249166</td>\n",
       "      <td>-0.484918</td>\n",
       "      <td>0.824704</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.162742</td>\n",
       "      <td>-0.145387</td>\n",
       "      <td>-0.326269</td>\n",
       "      <td>-0.187924</td>\n",
       "      <td>-0.108625</td>\n",
       "      <td>-0.400194</td>\n",
       "      <td>-0.314653</td>\n",
       "      <td>-0.30333</td>\n",
       "      <td>-0.13789</td>\n",
       "      <td>-0.179443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6860 rows × 3102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Order Date_year  Order Date_month  Order Date_day  Ship Date_year  \\\n",
       "0            1.153020         -0.351009       -0.142358        1.138089   \n",
       "1           -0.626244          0.817035       -0.389593       -0.638920   \n",
       "2            1.153020         -1.811064       -1.131297        1.138089   \n",
       "3           -0.626244          1.109046        0.475728       -0.638920   \n",
       "4            1.153020         -0.643020       -0.884062        1.138089   \n",
       "...               ...               ...             ...             ...   \n",
       "6855         1.153020          0.817035        0.846580        1.138089   \n",
       "6856         0.263388         -1.519053       -0.265975        0.249584   \n",
       "6857         0.263388          0.233013       -1.502149        0.249584   \n",
       "6858        -1.515875          1.109046        1.464667       -1.527425   \n",
       "6859         1.153020         -0.058998       -1.502149        1.138089   \n",
       "\n",
       "      Ship Date_month  Ship Date_day  Ship Mode_First Class  \\\n",
       "0           -0.387963       0.315968              -0.429885   \n",
       "1            0.792139       0.076408              -0.429885   \n",
       "2            0.202088      -1.121394              -0.429885   \n",
       "3            1.087165       1.034650              -0.429885   \n",
       "4            0.497114      -0.881834              -0.429885   \n",
       "...               ...            ...                    ...   \n",
       "6855         0.792139       1.513771              -0.429885   \n",
       "6856        -1.568065      -0.163153               2.326202   \n",
       "6857         1.382191      -1.480735              -0.429885   \n",
       "6858        -1.863091      -0.522493              -0.429885   \n",
       "6859         1.382191      -1.480735              -0.429885   \n",
       "\n",
       "      Ship Mode_Same Day  Ship Mode_Second Class  Ship Mode_Standard Class  \\\n",
       "0              -0.249166               -0.484918                  0.824704   \n",
       "1              -0.249166               -0.484918                  0.824704   \n",
       "2              -0.249166               -0.484918                  0.824704   \n",
       "3              -0.249166               -0.484918                  0.824704   \n",
       "4              -0.249166               -0.484918                  0.824704   \n",
       "...                  ...                     ...                       ...   \n",
       "6855           -0.249166               -0.484918                  0.824704   \n",
       "6856           -0.249166               -0.484918                 -1.212556   \n",
       "6857           -0.249166               -0.484918                  0.824704   \n",
       "6858           -0.249166                2.062203                 -1.212556   \n",
       "6859           -0.249166               -0.484918                  0.824704   \n",
       "\n",
       "      ...  Sub-Category_Envelopes  Sub-Category_Fasteners  \\\n",
       "0     ...               -0.162742               -0.145387   \n",
       "1     ...               -0.162742               -0.145387   \n",
       "2     ...               -0.162742               -0.145387   \n",
       "3     ...               -0.162742               -0.145387   \n",
       "4     ...               -0.162742               -0.145387   \n",
       "...   ...                     ...                     ...   \n",
       "6855  ...               -0.162742               -0.145387   \n",
       "6856  ...               -0.162742               -0.145387   \n",
       "6857  ...               -0.162742               -0.145387   \n",
       "6858  ...               -0.162742               -0.145387   \n",
       "6859  ...               -0.162742               -0.145387   \n",
       "\n",
       "      Sub-Category_Furnishings  Sub-Category_Labels  Sub-Category_Machines  \\\n",
       "0                    -0.326269            -0.187924              -0.108625   \n",
       "1                    -0.326269            -0.187924              -0.108625   \n",
       "2                    -0.326269            -0.187924              -0.108625   \n",
       "3                    -0.326269            -0.187924              -0.108625   \n",
       "4                    -0.326269            -0.187924              -0.108625   \n",
       "...                        ...                  ...                    ...   \n",
       "6855                 -0.326269            -0.187924              -0.108625   \n",
       "6856                  3.064953            -0.187924              -0.108625   \n",
       "6857                 -0.326269            -0.187924              -0.108625   \n",
       "6858                  3.064953            -0.187924              -0.108625   \n",
       "6859                 -0.326269            -0.187924              -0.108625   \n",
       "\n",
       "      Sub-Category_Paper  Sub-Category_Phones  Sub-Category_Storage  \\\n",
       "0              -0.400194            -0.314653              -0.30333   \n",
       "1              -0.400194            -0.314653              -0.30333   \n",
       "2               2.498785            -0.314653              -0.30333   \n",
       "3              -0.400194            -0.314653              -0.30333   \n",
       "4               2.498785            -0.314653              -0.30333   \n",
       "...                  ...                  ...                   ...   \n",
       "6855           -0.400194             3.178101              -0.30333   \n",
       "6856           -0.400194            -0.314653              -0.30333   \n",
       "6857            2.498785            -0.314653              -0.30333   \n",
       "6858           -0.400194            -0.314653              -0.30333   \n",
       "6859           -0.400194            -0.314653              -0.30333   \n",
       "\n",
       "      Sub-Category_Supplies  Sub-Category_Tables  \n",
       "0                  -0.13789            -0.179443  \n",
       "1                  -0.13789            -0.179443  \n",
       "2                  -0.13789            -0.179443  \n",
       "3                  -0.13789            -0.179443  \n",
       "4                  -0.13789            -0.179443  \n",
       "...                     ...                  ...  \n",
       "6855               -0.13789            -0.179443  \n",
       "6856               -0.13789            -0.179443  \n",
       "6857               -0.13789            -0.179443  \n",
       "6858               -0.13789            -0.179443  \n",
       "6859               -0.13789            -0.179443  \n",
       "\n",
       "[6860 rows x 3102 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "46d27d01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order Date_year</th>\n",
       "      <th>Order Date_month</th>\n",
       "      <th>Order Date_day</th>\n",
       "      <th>Ship Date_year</th>\n",
       "      <th>Ship Date_month</th>\n",
       "      <th>Ship Date_day</th>\n",
       "      <th>Ship Mode_First Class</th>\n",
       "      <th>Ship Mode_Same Day</th>\n",
       "      <th>Ship Mode_Second Class</th>\n",
       "      <th>Ship Mode_Standard Class</th>\n",
       "      <th>...</th>\n",
       "      <th>Sub-Category_Envelopes</th>\n",
       "      <th>Sub-Category_Fasteners</th>\n",
       "      <th>Sub-Category_Furnishings</th>\n",
       "      <th>Sub-Category_Labels</th>\n",
       "      <th>Sub-Category_Machines</th>\n",
       "      <th>Sub-Category_Paper</th>\n",
       "      <th>Sub-Category_Phones</th>\n",
       "      <th>Sub-Category_Storage</th>\n",
       "      <th>Sub-Category_Supplies</th>\n",
       "      <th>Sub-Category_Tables</th>\n",
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       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>6.860000e+03</td>\n",
       "      <td>6.860000e+03</td>\n",
       "      <td>6.860000e+03</td>\n",
       "      <td>6.860000e+03</td>\n",
       "      <td>6.860000e+03</td>\n",
       "      <td>6.860000e+03</td>\n",
       "      <td>6.860000e+03</td>\n",
       "      <td>6.860000e+03</td>\n",
       "      <td>6.860000e+03</td>\n",
       "      <td>6.860000e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>6.860000e+03</td>\n",
       "      <td>6.860000e+03</td>\n",
       "      <td>6.860000e+03</td>\n",
       "      <td>6.860000e+03</td>\n",
       "      <td>6.860000e+03</td>\n",
       "      <td>6.860000e+03</td>\n",
       "      <td>6.860000e+03</td>\n",
       "      <td>6.860000e+03</td>\n",
       "      <td>6.860000e+03</td>\n",
       "      <td>6.860000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.941205e-14</td>\n",
       "      <td>-1.303187e-16</td>\n",
       "      <td>-1.898708e-16</td>\n",
       "      <td>-1.168806e-14</td>\n",
       "      <td>-1.873461e-16</td>\n",
       "      <td>1.009558e-16</td>\n",
       "      <td>-3.038548e-16</td>\n",
       "      <td>1.877911e-16</td>\n",
       "      <td>3.895410e-16</td>\n",
       "      <td>9.154323e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.156034e-16</td>\n",
       "      <td>-5.327209e-16</td>\n",
       "      <td>4.219171e-16</td>\n",
       "      <td>5.783760e-16</td>\n",
       "      <td>-2.546999e-16</td>\n",
       "      <td>-2.523249e-16</td>\n",
       "      <td>-1.618401e-16</td>\n",
       "      <td>1.237106e-16</td>\n",
       "      <td>-3.848881e-16</td>\n",
       "      <td>-9.371350e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000073e+00</td>\n",
       "      <td>1.000073e+00</td>\n",
       "      <td>1.000073e+00</td>\n",
       "      <td>1.000073e+00</td>\n",
       "      <td>1.000073e+00</td>\n",
       "      <td>1.000073e+00</td>\n",
       "      <td>1.000073e+00</td>\n",
       "      <td>1.000073e+00</td>\n",
       "      <td>1.000073e+00</td>\n",
       "      <td>1.000073e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000073e+00</td>\n",
       "      <td>1.000073e+00</td>\n",
       "      <td>1.000073e+00</td>\n",
       "      <td>1.000073e+00</td>\n",
       "      <td>1.000073e+00</td>\n",
       "      <td>1.000073e+00</td>\n",
       "      <td>1.000073e+00</td>\n",
       "      <td>1.000073e+00</td>\n",
       "      <td>1.000073e+00</td>\n",
       "      <td>1.000073e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.515875e+00</td>\n",
       "      <td>-1.811064e+00</td>\n",
       "      <td>-1.873001e+00</td>\n",
       "      <td>-1.527425e+00</td>\n",
       "      <td>-1.863091e+00</td>\n",
       "      <td>-1.840076e+00</td>\n",
       "      <td>-4.298853e-01</td>\n",
       "      <td>-2.491664e-01</td>\n",
       "      <td>-4.849184e-01</td>\n",
       "      <td>-1.212556e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.627425e-01</td>\n",
       "      <td>-1.453865e-01</td>\n",
       "      <td>-3.262692e-01</td>\n",
       "      <td>-1.879240e-01</td>\n",
       "      <td>-1.086251e-01</td>\n",
       "      <td>-4.001944e-01</td>\n",
       "      <td>-3.146533e-01</td>\n",
       "      <td>-3.033297e-01</td>\n",
       "      <td>-1.378901e-01</td>\n",
       "      <td>-1.794431e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-6.262437e-01</td>\n",
       "      <td>-9.350309e-01</td>\n",
       "      <td>-7.604448e-01</td>\n",
       "      <td>-6.389203e-01</td>\n",
       "      <td>-6.829885e-01</td>\n",
       "      <td>-7.620538e-01</td>\n",
       "      <td>-4.298853e-01</td>\n",
       "      <td>-2.491664e-01</td>\n",
       "      <td>-4.849184e-01</td>\n",
       "      <td>-1.212556e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.627425e-01</td>\n",
       "      <td>-1.453865e-01</td>\n",
       "      <td>-3.262692e-01</td>\n",
       "      <td>-1.879240e-01</td>\n",
       "      <td>-1.086251e-01</td>\n",
       "      <td>-4.001944e-01</td>\n",
       "      <td>-3.146533e-01</td>\n",
       "      <td>-3.033297e-01</td>\n",
       "      <td>-1.378901e-01</td>\n",
       "      <td>-1.794431e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.633881e-01</td>\n",
       "      <td>2.330128e-01</td>\n",
       "      <td>-1.874082e-02</td>\n",
       "      <td>2.495843e-01</td>\n",
       "      <td>2.020882e-01</td>\n",
       "      <td>-4.337232e-02</td>\n",
       "      <td>-4.298853e-01</td>\n",
       "      <td>-2.491664e-01</td>\n",
       "      <td>-4.849184e-01</td>\n",
       "      <td>8.247043e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.627425e-01</td>\n",
       "      <td>-1.453865e-01</td>\n",
       "      <td>-3.262692e-01</td>\n",
       "      <td>-1.879240e-01</td>\n",
       "      <td>-1.086251e-01</td>\n",
       "      <td>-4.001944e-01</td>\n",
       "      <td>-3.146533e-01</td>\n",
       "      <td>-3.033297e-01</td>\n",
       "      <td>-1.378901e-01</td>\n",
       "      <td>-1.794431e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.153020e+00</td>\n",
       "      <td>8.170347e-01</td>\n",
       "      <td>8.465805e-01</td>\n",
       "      <td>1.138089e+00</td>\n",
       "      <td>7.921394e-01</td>\n",
       "      <td>9.148696e-01</td>\n",
       "      <td>-4.298853e-01</td>\n",
       "      <td>-2.491664e-01</td>\n",
       "      <td>-4.849184e-01</td>\n",
       "      <td>8.247043e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.627425e-01</td>\n",
       "      <td>-1.453865e-01</td>\n",
       "      <td>-3.262692e-01</td>\n",
       "      <td>-1.879240e-01</td>\n",
       "      <td>-1.086251e-01</td>\n",
       "      <td>-4.001944e-01</td>\n",
       "      <td>-3.146533e-01</td>\n",
       "      <td>-3.033297e-01</td>\n",
       "      <td>-1.378901e-01</td>\n",
       "      <td>-1.794431e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.153020e+00</td>\n",
       "      <td>1.401057e+00</td>\n",
       "      <td>1.835519e+00</td>\n",
       "      <td>2.026594e+00</td>\n",
       "      <td>1.382191e+00</td>\n",
       "      <td>1.753331e+00</td>\n",
       "      <td>2.326202e+00</td>\n",
       "      <td>4.013382e+00</td>\n",
       "      <td>2.062203e+00</td>\n",
       "      <td>8.247043e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>6.144678e+00</td>\n",
       "      <td>6.878216e+00</td>\n",
       "      <td>3.064953e+00</td>\n",
       "      <td>5.321301e+00</td>\n",
       "      <td>9.205976e+00</td>\n",
       "      <td>2.498785e+00</td>\n",
       "      <td>3.178101e+00</td>\n",
       "      <td>3.296743e+00</td>\n",
       "      <td>7.252155e+00</td>\n",
       "      <td>5.572798e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 3102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Order Date_year  Order Date_month  Order Date_day  Ship Date_year  \\\n",
       "count     6.860000e+03      6.860000e+03    6.860000e+03    6.860000e+03   \n",
       "mean      8.941205e-14     -1.303187e-16   -1.898708e-16   -1.168806e-14   \n",
       "std       1.000073e+00      1.000073e+00    1.000073e+00    1.000073e+00   \n",
       "min      -1.515875e+00     -1.811064e+00   -1.873001e+00   -1.527425e+00   \n",
       "25%      -6.262437e-01     -9.350309e-01   -7.604448e-01   -6.389203e-01   \n",
       "50%       2.633881e-01      2.330128e-01   -1.874082e-02    2.495843e-01   \n",
       "75%       1.153020e+00      8.170347e-01    8.465805e-01    1.138089e+00   \n",
       "max       1.153020e+00      1.401057e+00    1.835519e+00    2.026594e+00   \n",
       "\n",
       "       Ship Date_month  Ship Date_day  Ship Mode_First Class  \\\n",
       "count     6.860000e+03   6.860000e+03           6.860000e+03   \n",
       "mean     -1.873461e-16   1.009558e-16          -3.038548e-16   \n",
       "std       1.000073e+00   1.000073e+00           1.000073e+00   \n",
       "min      -1.863091e+00  -1.840076e+00          -4.298853e-01   \n",
       "25%      -6.829885e-01  -7.620538e-01          -4.298853e-01   \n",
       "50%       2.020882e-01  -4.337232e-02          -4.298853e-01   \n",
       "75%       7.921394e-01   9.148696e-01          -4.298853e-01   \n",
       "max       1.382191e+00   1.753331e+00           2.326202e+00   \n",
       "\n",
       "       Ship Mode_Same Day  Ship Mode_Second Class  Ship Mode_Standard Class  \\\n",
       "count        6.860000e+03            6.860000e+03              6.860000e+03   \n",
       "mean         1.877911e-16            3.895410e-16              9.154323e-16   \n",
       "std          1.000073e+00            1.000073e+00              1.000073e+00   \n",
       "min         -2.491664e-01           -4.849184e-01             -1.212556e+00   \n",
       "25%         -2.491664e-01           -4.849184e-01             -1.212556e+00   \n",
       "50%         -2.491664e-01           -4.849184e-01              8.247043e-01   \n",
       "75%         -2.491664e-01           -4.849184e-01              8.247043e-01   \n",
       "max          4.013382e+00            2.062203e+00              8.247043e-01   \n",
       "\n",
       "       ...  Sub-Category_Envelopes  Sub-Category_Fasteners  \\\n",
       "count  ...            6.860000e+03            6.860000e+03   \n",
       "mean   ...           -2.156034e-16           -5.327209e-16   \n",
       "std    ...            1.000073e+00            1.000073e+00   \n",
       "min    ...           -1.627425e-01           -1.453865e-01   \n",
       "25%    ...           -1.627425e-01           -1.453865e-01   \n",
       "50%    ...           -1.627425e-01           -1.453865e-01   \n",
       "75%    ...           -1.627425e-01           -1.453865e-01   \n",
       "max    ...            6.144678e+00            6.878216e+00   \n",
       "\n",
       "       Sub-Category_Furnishings  Sub-Category_Labels  Sub-Category_Machines  \\\n",
       "count              6.860000e+03         6.860000e+03           6.860000e+03   \n",
       "mean               4.219171e-16         5.783760e-16          -2.546999e-16   \n",
       "std                1.000073e+00         1.000073e+00           1.000073e+00   \n",
       "min               -3.262692e-01        -1.879240e-01          -1.086251e-01   \n",
       "25%               -3.262692e-01        -1.879240e-01          -1.086251e-01   \n",
       "50%               -3.262692e-01        -1.879240e-01          -1.086251e-01   \n",
       "75%               -3.262692e-01        -1.879240e-01          -1.086251e-01   \n",
       "max                3.064953e+00         5.321301e+00           9.205976e+00   \n",
       "\n",
       "       Sub-Category_Paper  Sub-Category_Phones  Sub-Category_Storage  \\\n",
       "count        6.860000e+03         6.860000e+03          6.860000e+03   \n",
       "mean        -2.523249e-16        -1.618401e-16          1.237106e-16   \n",
       "std          1.000073e+00         1.000073e+00          1.000073e+00   \n",
       "min         -4.001944e-01        -3.146533e-01         -3.033297e-01   \n",
       "25%         -4.001944e-01        -3.146533e-01         -3.033297e-01   \n",
       "50%         -4.001944e-01        -3.146533e-01         -3.033297e-01   \n",
       "75%         -4.001944e-01        -3.146533e-01         -3.033297e-01   \n",
       "max          2.498785e+00         3.178101e+00          3.296743e+00   \n",
       "\n",
       "       Sub-Category_Supplies  Sub-Category_Tables  \n",
       "count           6.860000e+03         6.860000e+03  \n",
       "mean           -3.848881e-16        -9.371350e-17  \n",
       "std             1.000073e+00         1.000073e+00  \n",
       "min            -1.378901e-01        -1.794431e-01  \n",
       "25%            -1.378901e-01        -1.794431e-01  \n",
       "50%            -1.378901e-01        -1.794431e-01  \n",
       "75%            -1.378901e-01        -1.794431e-01  \n",
       "max             7.252155e+00         5.572798e+00  \n",
       "\n",
       "[8 rows x 3102 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8bebe357",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8788    698.352\n",
       "847     287.940\n",
       "3397     25.920\n",
       "6244    119.040\n",
       "8198     15.552\n",
       "         ...   \n",
       "2895     35.880\n",
       "7813     10.560\n",
       "905      12.960\n",
       "5192    397.600\n",
       "235     617.976\n",
       "Name: Sales, Length: 6860, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ca3cc7e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     6860.000000\n",
       "mean       234.030575\n",
       "std        660.951936\n",
       "min          0.444000\n",
       "25%         17.340000\n",
       "50%         54.514000\n",
       "75%        212.940000\n",
       "max      22638.480000\n",
       "Name: Sales, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa26db89",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "33ea8b01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None, 3102)]            0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 2048)              6354944   \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 2048)              4196352   \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 1)                 2049      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 10,553,345\n",
      "Trainable params: 10,553,345\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "#A two hidden layers neural network is developed ( 1. input layer and 2. two dense layer)\n",
    "\n",
    "inputs = tf.keras.Input(shape=(X_train.shape[1],))\n",
    "x = tf.keras.layers.Dense(2048, activation='relu')(inputs)\n",
    "x = tf.keras.layers.Dense(2048, activation='relu')(x)\n",
    "outputs = tf.keras.layers.Dense(1, activation='linear')(x)\n",
    "\n",
    "model = tf.keras.Model(inputs=inputs, outputs=outputs)\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2fa43768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "172/172 [==============================] - 8s 45ms/step - loss: 434940.3125 - val_loss: 160971.2969 - lr: 0.0010\n",
      "Epoch 2/100\n",
      "172/172 [==============================] - 8s 45ms/step - loss: 285409.9375 - val_loss: 412977.9062 - lr: 0.0010\n",
      "Epoch 3/100\n",
      "172/172 [==============================] - 7s 43ms/step - loss: 163313.7812 - val_loss: 1226673.7500 - lr: 0.0010\n",
      "Epoch 4/100\n",
      "172/172 [==============================] - 8s 45ms/step - loss: 92059.9688 - val_loss: 3036100.7500 - lr: 0.0010\n",
      "Epoch 5/100\n",
      "172/172 [==============================] - 8s 45ms/step - loss: 69573.6641 - val_loss: 4232857.5000 - lr: 0.0010\n",
      "Epoch 6/100\n",
      "172/172 [==============================] - 8s 46ms/step - loss: 42895.5352 - val_loss: 3518061.7500 - lr: 0.0010\n"
     ]
    }
   ],
   "source": [
    "model.compile(\n",
    "    optimizer='adam',\n",
    "    loss='mse'\n",
    ")\n",
    "\n",
    "history = model.fit(\n",
    "    X_train,\n",
    "    y_train,\n",
    "    validation_split=0.2,\n",
    "    batch_size=32,\n",
    "    epochs=100,\n",
    "    callbacks=[\n",
    "        tf.keras.callbacks.EarlyStopping(\n",
    "            monitor='val_loss',\n",
    "            patience=5,\n",
    "            restore_best_weights=True\n",
    "        ),\n",
    "        tf.keras.callbacks.ReduceLROnPlateau()\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b2bc61a",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "879b7a77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 208526.46875\n"
     ]
    }
   ],
   "source": [
    "\n",
    "test_loss = model.evaluate(X_test, y_test, verbose=0)\n",
    "\n",
    "print(\"Test Loss: {:.5f}\".format(test_loss))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "feb12925",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test R^2 Score: 0.27988\n"
     ]
    }
   ],
   "source": [
    "y_pred = np.squeeze(model.predict(X_test))\n",
    "test_r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "\n",
    "\n",
    "print(\"Test R^2 Score: {:.5f}\".format(test_r2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df06fa80",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "Here we conclude that the model is not functioning good even at 2048 nureons and thus we conclude that this model is not good and we need to develope more effecient model. \n",
    "According to the R^2 value it should be near 1.0. "
   ]
  }
 ],
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