Customer Rating Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Context\n",
    "An international e-commerce company based wants to discover key insights from their customer database. They want to use some of the most advanced machine learning techniques to study their customers. The company sells electronic products.🚢\n",
    "\n",
    "## Content\n",
    "The dataset used for model building contained 10999 observations of 12 variables.\n",
    "The data contains the following information:\n",
    "\n",
    "* ID: ID Number of Customers.\n",
    "* Warehouse block: The Company have big Warehouse which is divided in to block such as A,B,C,D,E.\n",
    "* Mode of shipment:The Company Ships the products in multiple way such as Ship, Flight and Road.\n",
    "* Customer care calls: The number of calls made from enquiry for enquiry of the shipment.\n",
    "* Customer rating: The company has rated from every customer. 1 is the lowest (Worst), 5 is the highest (Best).\n",
    "* Cost of the product: Cost of the Product in US Dollars.\n",
    "* Prior purchases: The Number of Prior Purchase.\n",
    "* Product importance: The company has categorized the product in the various parameter such as low, medium, high.\n",
    "* Gender: Male and Female.\n",
    "* Discount offered: Discount offered on that specific product.\n",
    "* Weight in gms: It is the weight in grams.\n",
    "* Reached on time: It is the target variable, where 1 Indicates that the product has NOT reached on time and 0 indicates it has reached on time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from IPython.core.display import HTML\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import scale  # for normalizing data\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier as DT\n",
    "from sklearn.naive_bayes import GaussianNB as GB  # for Naive Bayesian \n",
    "from sklearn.svm import SVC # Support Vendor Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
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    }
   },
   "outputs": [],
   "source": [
    "# load the file\n",
    "ecomm = pd.read_csv('https://raw.githubusercontent.com/jadanpl/E-Commerce-Shipping/main/raw_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 📌 Explanatory Analysis & Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2022-03-22T12:33:47.426771Z",
     "shell.execute_reply.started": "2022-03-22T12:33:47.386394Z"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10999 entries, 0 to 10998\n",
      "Data columns (total 12 columns):\n",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   ID                   10999 non-null  int64 \n",
      " 1   Warehouse_block      10999 non-null  object\n",
      " 2   Mode_of_Shipment     10999 non-null  object\n",
      " 3   Customer_care_calls  10999 non-null  int64 \n",
      " 4   Customer_rating      10999 non-null  int64 \n",
      " 5   Cost_of_the_Product  10999 non-null  int64 \n",
      " 6   Prior_purchases      10999 non-null  int64 \n",
      " 7   Product_importance   10999 non-null  object\n",
      " 8   Gender               10999 non-null  object\n",
      " 9   Discount_offered     10999 non-null  int64 \n",
      " 10  Weight_in_gms        10999 non-null  int64 \n",
      " 11  Reached.on.Time_Y.N  10999 non-null  int64 \n",
      "dtypes: int64(8), object(4)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "ecomm.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
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    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                     0\n",
       "Warehouse_block        0\n",
       "Mode_of_Shipment       0\n",
       "Customer_care_calls    0\n",
       "Customer_rating        0\n",
       "Cost_of_the_Product    0\n",
       "Prior_purchases        0\n",
       "Product_importance     0\n",
       "Gender                 0\n",
       "Discount_offered       0\n",
       "Weight_in_gms          0\n",
       "Reached.on.Time_Y.N    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecomm.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ID',\n",
       " 'Warehouse Block',\n",
       " 'Mode Of Shipment',\n",
       " 'Customer Care Calls',\n",
       " 'Customer Rating',\n",
       " 'Cost Of The Product',\n",
       " 'Prior Purchases',\n",
       " 'Product Importance',\n",
       " 'Gender',\n",
       " 'Discount Offered',\n",
       " 'Weight In Gms',\n",
       " 'Arrival']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_cols=[]\n",
    "for i in ecomm.columns[1:-1]:\n",
    "    i = i.replace(\"_\",\" \")\n",
    "    i = i.title()\n",
    "    new_cols.append(i);\n",
    "new_cols = ['ID'] +  new_cols\n",
    "new_cols.append('Arrival')\n",
    "ecomm.columns = new_cols\n",
    "ecomm.columns.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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    "execution": {
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       "      <th>Cost Of The Product</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10987</th>\n",
       "      <td>10988</td>\n",
       "      <td>F</td>\n",
       "      <td>Ship</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>low</td>\n",
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       "      <td>3</td>\n",
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       "      <td>5910</td>\n",
       "      <td>F</td>\n",
       "      <td>Ship</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>198</td>\n",
       "      <td>4</td>\n",
       "      <td>low</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>5182</td>\n",
       "      <td>0</td>\n",
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       "      <th>1642</th>\n",
       "      <td>1643</td>\n",
       "      <td>C</td>\n",
       "      <td>Flight</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>259</td>\n",
       "      <td>3</td>\n",
       "      <td>low</td>\n",
       "      <td>F</td>\n",
       "      <td>45</td>\n",
       "      <td>3335</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5489</th>\n",
       "      <td>5490</td>\n",
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       "      <td>Flight</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>259</td>\n",
       "      <td>7</td>\n",
       "      <td>medium</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>5102</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID Warehouse Block Mode Of Shipment  Customer Care Calls  \\\n",
       "10987  10988               F             Ship                    5   \n",
       "153      154               B           Flight                    3   \n",
       "5909    5910               F             Ship                    4   \n",
       "1642    1643               C           Flight                    5   \n",
       "5489    5490               F           Flight                    4   \n",
       "\n",
       "       Customer Rating  Cost Of The Product  Prior Purchases  \\\n",
       "10987                3                  242                5   \n",
       "153                  3                  183                2   \n",
       "5909                 1                  198                4   \n",
       "1642                 3                  259                3   \n",
       "5489                 1                  259                7   \n",
       "\n",
       "      Product Importance Gender  Discount Offered  Weight In Gms  Arrival  \n",
       "10987                low      F                10           1846        1  \n",
       "153                  low      M                53           3932        1  \n",
       "5909                 low      F                 5           5182        0  \n",
       "1642                 low      F                45           3335        1  \n",
       "5489              medium      F                 4           5102        0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "ecomm.sample(5)"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Care Calls</th>\n",
       "      <th>Customer Rating</th>\n",
       "      <th>Cost Of The Product</th>\n",
       "      <th>Prior Purchases</th>\n",
       "      <th>Discount Offered</th>\n",
       "      <th>Weight In Gms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10999.000000</td>\n",
       "      <td>10999.000000</td>\n",
       "      <td>10999.000000</td>\n",
       "      <td>10999.000000</td>\n",
       "      <td>10999.000000</td>\n",
       "      <td>10999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.054459</td>\n",
       "      <td>2.990545</td>\n",
       "      <td>210.196836</td>\n",
       "      <td>3.567597</td>\n",
       "      <td>13.373216</td>\n",
       "      <td>3634.016729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.141490</td>\n",
       "      <td>1.413603</td>\n",
       "      <td>48.063272</td>\n",
       "      <td>1.522860</td>\n",
       "      <td>16.205527</td>\n",
       "      <td>1635.377251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1001.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1839.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4149.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>251.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5050.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>310.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>7846.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       Customer Care Calls  Customer Rating  Cost Of The Product  \\\n",
       "count         10999.000000     10999.000000         10999.000000   \n",
       "mean              4.054459         2.990545           210.196836   \n",
       "std               1.141490         1.413603            48.063272   \n",
       "min               2.000000         1.000000            96.000000   \n",
       "25%               3.000000         2.000000           169.000000   \n",
       "50%               4.000000         3.000000           214.000000   \n",
       "75%               5.000000         4.000000           251.000000   \n",
       "max               7.000000         5.000000           310.000000   \n",
       "\n",
       "       Prior Purchases  Discount Offered  Weight In Gms  \n",
       "count     10999.000000      10999.000000   10999.000000  \n",
       "mean          3.567597         13.373216    3634.016729  \n",
       "std           1.522860         16.205527    1635.377251  \n",
       "min           2.000000          1.000000    1001.000000  \n",
       "25%           3.000000          4.000000    1839.500000  \n",
       "50%           3.000000          7.000000    4149.000000  \n",
       "75%           4.000000         10.000000    5050.000000  \n",
       "max          10.000000         65.000000    7846.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecomm.iloc[:,1:-1].describe()"
   ]
  },
  {
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   "execution_count": 8,
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       "      <th>Product Importance</th>\n",
       "      <th>Gender</th>\n",
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       "      <th>count</th>\n",
       "      <td>10999</td>\n",
       "      <td>10999</td>\n",
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       "      <th>top</th>\n",
       "      <td>F</td>\n",
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       "      <td>F</td>\n",
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       "      <th>freq</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Warehouse Block Mode Of Shipment Product Importance Gender\n",
       "count            10999            10999              10999  10999\n",
       "unique               5                3                  3      2\n",
       "top                  F             Ship                low      F\n",
       "freq              3666             7462               5297   5545"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecomm.describe(include='object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Frequency Distribution for Numeric Data 🙋‍♀️"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-22T12:33:47.600006Z",
     "iopub.status.busy": "2022-03-22T12:33:47.598732Z",
     "iopub.status.idle": "2022-03-22T12:33:49.226394Z",
     "shell.execute_reply": "2022-03-22T12:33:49.225125Z",
     "shell.execute_reply.started": "2022-03-22T12:33:47.599954Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1152 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting multiple graphs in a grid\n",
    "# exploring the distribution of numeric columns using cumulative frequency distribution\n",
    "fig, ax = plt.subplots(figsize=(20,16), facecolor='#F2F4F4')\n",
    "fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.2, hspace=0.5)\n",
    "count=1\n",
    "ls = ['Cost Of The Product','Weight In Gms','Discount Offered']\n",
    "for i in ls:\n",
    "    plt.subplot(3,1,count)\n",
    "    h = sns.histplot(x=i, kde=True, data=ecomm)\n",
    "    h.set_title(('frequency distribution of ' + i).title(), fontsize=13)\n",
    "    count+=1\n",
    "\n",
    "# fig, axes = plt.subplots(3,1,figsize=(20,16))\n",
    "\n",
    "# # cumulative frequency distribution for'Cost_of_the_Product'\n",
    "# sns.histplot(ecomm['Cost Of The Product'], kde=True, ax=axes[0])\n",
    "# ax=axes[0].set_title('Frequency Distribution of Product Cost',fontsize=12)\n",
    "\n",
    "# # cumulative frequency distribution for 'Weight_in_gms'\n",
    "# sns.histplot(ecomm['Weight In Gms'], kde=True, ax=axes[1])\n",
    "# ax=axes[1].set_title('Frequency Distribution of Weight',fontsize=12)\n",
    "\n",
    "# # cumulative frequency distribution for'Discount_offered'\n",
    "# sns.histplot(ecomm['Discount Offered'], kde=True, ax=axes[2])\n",
    "# ax=axes[2].set_title('Frequency Distribution of Discount Offered',fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the cumulative frequency distribution above, we can infer that:\n",
    "<ol>\n",
    "    <li>The cost of most of the products falls within the range of 240.00 - 275.00 dollars.</li>\n",
    "    <li>The weight of most of the products falls within the range of 1,000-2,000gms and 4,000-6,000gms. </li>\n",
    "    <li>The discount that is most likely to be given is between 1% to 10%. There are many outliers located at the third quartile. </li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Countplots for Categorical Data 💁‍♀️"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-22T12:33:49.231264Z",
     "iopub.status.busy": "2022-03-22T12:33:49.230890Z",
     "iopub.status.idle": "2022-03-22T12:33:51.032120Z",
     "shell.execute_reply": "2022-03-22T12:33:51.030637Z",
     "shell.execute_reply.started": "2022-03-22T12:33:49.231208Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for categorical data\n",
    "fig, axes = plt.subplots(4,2,figsize=(16,25), facecolor='#F2F4F4')\n",
    "\n",
    "# countplot for 'Warehouse Block'\n",
    "abs_whs=ecomm[\"Warehouse Block\"].value_counts(ascending=False)\n",
    "sns.countplot(x=ecomm[\"Warehouse Block\"], order=abs_whs.index, ax=axes[0,0], palette='CMRmap_r')\n",
    "axes[0,0].set_title('Orders Handled By Each Warehouse Block', fontsize=12)\n",
    "# axes[0,0].set_xlabel('Warehouse Block', fontsize=10)\n",
    "# axes[0,0].set_ylabel('Count', fontsize=10)\n",
    "rel_whs=ecomm[\"Warehouse Block\"].value_counts(ascending=False, normalize=True).values*100\n",
    "lbs_whs=[f\"{w[0]} ({w[1]:.2f}%)\" for w in zip(abs_whs,rel_whs)]\n",
    "axes[0,0].bar_label(container=axes[0,0].containers[0], labels=lbs_whs)\n",
    "\n",
    "# countplot for 'Mode Of Shipment' \n",
    "abs_ship = ecomm[\"Mode Of Shipment\"].value_counts(ascending=False) \n",
    "sns.countplot(x=ecomm[\"Mode Of Shipment\"], order=abs_ship.index, ax=axes[0,1], palette=['#DC143C','#556b2f','#008b8b'])\n",
    "axes[0,1].set_title('Number of Orders By Shipment Mode', fontsize=12)\n",
    "# axes[0,1].set_xlabel('Shipment Mode', fontsize=10)\n",
    "# axes[0,1].set_ylabel('Count', fontsize=10)\n",
    "rel_ship = ecomm[\"Mode Of Shipment\"].value_counts(ascending=False, normalize=True).values*100\n",
    "lbs_ship = [f\"{s[0]} ({s[1]:.2f}%)\" for s in zip (abs_ship,rel_ship)]\n",
    "axes[0,1].bar_label(container=axes[0,1].containers[0], labels=lbs_ship)\n",
    "\n",
    "# countplot for 'Customer Care Calls'\n",
    "abs_calls=ecomm[\"Customer Care Calls\"].value_counts(ascending=False)\n",
    "sns.countplot(x=ecomm[\"Customer Care Calls\"], order=abs_calls.index,ax=axes[1,0],palette='cubehelix')\n",
    "axes[1,0].set_title('Number of Customer Care Calls Made by Customers', fontsize=12)\n",
    "# axes[1,0].set_xlabel('Customer Care Calls', fontsize=10)\n",
    "# axes[1,0].set_ylabel('Count', fontsize=10)\n",
    "rel_calls=ecomm[\"Customer Care Calls\"].value_counts(ascending=False, normalize=True).values*100\n",
    "lbs_calls=[f\"{c[0]} ({c[1]:.2f}%)\" for c in zip(abs_calls, rel_calls)]\n",
    "axes[1,0].bar_label(container=axes[1,0].containers[0], labels=lbs_calls)\n",
    "\n",
    "# countplot for 'Customer Rating'\n",
    "abs_rating = ecomm[\"Customer Rating\"].value_counts(ascending=False)\n",
    "sns.countplot(x=ecomm[\"Customer Rating\"], order=abs_rating.index,ax=axes[1,1],palette=\"rocket\")\n",
    "axes[1,1].set_title('Customer Rating Received', fontsize=12);\n",
    "# axes[1,1].set_xlabel('Customer Rating', fontsize=10)\n",
    "# axes[1,1].set_ylabel('Count', fontsize=10)\n",
    "rel_rating = ecomm[\"Customer Rating\"].value_counts(ascending=False, normalize=True).values*100\n",
    "lbs_rating = [f\"{r[0]} ({r[1]:.2f}%)\" for r in zip(abs_rating, rel_rating)]\n",
    "axes[1,1].bar_label(container=axes[1,1].containers[0], labels=lbs_rating)\n",
    "\n",
    "# countplot for 'Prior Purchases'\n",
    "abs_prior_pur = ecomm[\"Prior Purchases\"].value_counts(ascending=False)\n",
    "sns.countplot(x=ecomm[\"Prior Purchases\"], order=abs_prior_pur.index,ax=axes[2,0],palette='viridis')\n",
    "axes[2,0].set_title('Number of Prior Purchases Made by Customers', fontsize=12)\n",
    "# axes[2,0].set_xlabel('Prior Purchases', fontsize=10)\n",
    "# axes[2,0].set_ylabel('Count', fontsize=10)\n",
    "rel_prior_pur = ecomm[\"Prior Purchases\"].value_counts(ascending=False, normalize=True).values*100\n",
    "lbs_prior_pur = [f\"{pur[0]} ({pur[1]:.0f}%)\" for pur in zip(abs_prior_pur, rel_prior_pur)]\n",
    "axes[2,0].bar_label(container=axes[2,0].containers[0], labels=lbs_prior_pur)\n",
    "\n",
    "# countplot for'Product Importance'\n",
    "abs_priority = ecomm[\"Product Importance\"].value_counts(ascending=False)\n",
    "sns.countplot(x=ecomm[\"Product Importance\"], order=abs_priority.index,ax=axes[2,1])\n",
    "axes[2,1].set_title('Number of Orders Made by Product Importance', fontsize=12)\n",
    "# axes[2,1].set_xlabel('Product Importance', fontsize=10)\n",
    "# axes[2,1].set_ylabel('Count', fontsize=10)\n",
    "rel_priority = ecomm[\"Product Importance\"].value_counts(ascending=False, normalize=True).values*100\n",
    "lbs_priority = [f\"{i[0]} ({i[1]:.2f}%)\" for i in zip(abs_priority, rel_priority)]\n",
    "axes[2,1].bar_label(container=axes[2,1].containers[0], labels=lbs_priority)\n",
    "\n",
    "# countplot for'Gender'\n",
    "abs_gender = ecomm[\"Gender\"].value_counts(ascending=False)\n",
    "sns.countplot(x=ecomm[\"Gender\"], order=abs_gender.index,ax=axes[3,0],palette=['#800000','#191970'])\n",
    "axes[3,0].set_title(\"Number of Orders Made by Customers' Gender\", fontsize=12)\n",
    "# axes[3,0].set_xlabel(\"Customers' Gender\", fontsize=10)\n",
    "# axes[3,0].set_ylabel('Count', fontsize=10)\n",
    "rel_gender = ecomm[\"Gender\"].value_counts(ascending=False, normalize=True).values*100\n",
    "lbs_gender = [f\"{g[0]} ({g[1]:.2f}%)\" for g in zip(abs_gender, rel_gender)]\n",
    "axes[3,0].bar_label(container=axes[3,0].containers[0], labels=lbs_gender)\n",
    "\n",
    "# countplot for'Arrival'\n",
    "abs_arrival = ecomm[\"Arrival\"].value_counts(ascending=False)\n",
    "sns.countplot(x=ecomm[\"Arrival\"], order=abs_arrival.index,ax=axes[3,1],palette='tab20c_r')\n",
    "axes[3,1].set_title('Number of Orders Based On Arrival Time', fontsize=12)\n",
    "axes[3,1].set_xticklabels(['Late', 'On Time'])\n",
    "# axes[3,1].set_xlabel('Arrival', fontsize=10)\n",
    "# axes[3,1].set_ylabel('Count', fontsize=10)\n",
    "rel_arrival = ecomm[\"Arrival\"].value_counts(ascending=False, normalize=True).values*100\n",
    "lbls_arrival=[f\"{a[0]}({a[1]:.2f}%)\" for a in zip(abs_arrival,rel_arrival)]\n",
    "axes[3,1].bar_label(container=axes[3,1].containers[0],labels=lbls_arrival);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the countplots above, we can infer that:\n",
    "1. Most of the orders were handled by warehouse block F, while other warehouse blocks handled the rest of the orders equally.\n",
    "2. Most of the orders were shipped by ship, followed by flight, and finally by road.\n",
    "3. Many of the customers needed to make 4 calls to track their shipment(s). This is a warning point for the company as the concern need to be solved as soon as possible without the need for the customers to make several calls. \n",
    "4. 1 is the rating with the second highest count. It is more likely due to the fact the products are not delivered on time and a high number of calls that the customers need to make. \n",
    "5. Many of the customers had made 3 prior purchases. And, we can infer that we have 128 loyal customers as they had made at least 8 (max=10) purchases previously.\n",
    "6. Most of the orders are of low importance (48.16%), followed by medium (43.22%) and then high (8.62%).\n",
    "7. 50.41% of the customers are females, while the remaining are males.\n",
    "8. Out of the 10,999 orders, 6,563 orders were not delivered on time and it made up more than half of the orders (59.67%). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 📌Asking and Answering Questions 🧐\n",
    "\n",
    "*Below are just the questions that I came out by myself and I think these might help the e-commerce company to determine the factors that caused the late orders.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q1. What are the average cost of the product and the average discount given to the customers whose orders are of high importance but arrived late? Group the result based on the customer rating and sort them in descendingly according to the number of orders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prior Purchases</th>\n",
       "      <th>Number of Highly Important But Late Orders</th>\n",
       "      <th>Discount Offered</th>\n",
       "      <th>Cost Of The Product</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer Rating</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.671756</td>\n",
       "      <td>131</td>\n",
       "      <td>17.053435</td>\n",
       "      <td>198.763359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.312500</td>\n",
       "      <td>128</td>\n",
       "      <td>21.984375</td>\n",
       "      <td>193.960938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.693548</td>\n",
       "      <td>124</td>\n",
       "      <td>18.274194</td>\n",
       "      <td>208.161290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.550847</td>\n",
       "      <td>118</td>\n",
       "      <td>19.127119</td>\n",
       "      <td>202.915254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.643478</td>\n",
       "      <td>115</td>\n",
       "      <td>22.643478</td>\n",
       "      <td>206.513043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Prior Purchases  Number of Highly Important But Late Orders  \\\n",
       "Customer Rating                                                                \n",
       "5                       3.671756                                         131   \n",
       "1                       3.312500                                         128   \n",
       "2                       3.693548                                         124   \n",
       "3                       3.550847                                         118   \n",
       "4                       3.643478                                         115   \n",
       "\n",
       "                 Discount Offered  Cost Of The Product  \n",
       "Customer Rating                                         \n",
       "5                       17.053435           198.763359  \n",
       "1                       21.984375           193.960938  \n",
       "2                       18.274194           208.161290  \n",
       "3                       19.127119           202.915254  \n",
       "4                       22.643478           206.513043  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_late = ecomm[(ecomm['Product Importance']=='high') & (ecomm['Arrival']==1)][['ID','Prior Purchases','Discount Offered','Cost Of The Product','Customer Rating']]\n",
    "rating_discount_cost = high_late.groupby('Customer Rating').agg({'Prior Purchases':'mean','ID':'count','Discount Offered':'mean', 'Cost Of The Product':'mean'})\n",
    "rating_discount_cost = rating_discount_cost.rename(columns={'ID':'Number of Highly Important But Late Orders'})\n",
    "rating_discount_cost.sort_values('Number of Highly Important But Late Orders', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the table above, we can see that 131 highly important orders were late but the customers gave a rating of 5, even the discount given to them is much lower than those discount given to customers rated 1. This might be due to good experience in their prior purchases. It also can imply that there is other factors that led the customers to give rating 1, and it might be due to defective/ broken items received, bad packaging, or other factors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q2. What is the percentage of highly important orders that arrived late? Which warehouse block resulted the highest number of highly important but late orders (in proportion of the total number of orders handled)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-22T12:33:51.119639Z",
     "iopub.status.busy": "2022-03-22T12:33:51.119183Z",
     "iopub.status.idle": "2022-03-22T12:33:52.901700Z",
     "shell.execute_reply": "2022-03-22T12:33:52.900549Z",
     "shell.execute_reply.started": "2022-03-22T12:33:51.119588Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3,2,figsize=(16,16),facecolor='#F2F4F4')\n",
    "\n",
    "late_vs_importance = ecomm[ecomm['Arrival']==1].groupby('Product Importance').agg({'Product Importance':'count'})\n",
    "late_vs_importance.rename(columns={'Product Importance':'Number of Late Orders'},inplace=True)\n",
    "a = sns.barplot(x=late_vs_importance.index, y='Number of Late Orders', data=late_vs_importance,ax=axes[0,0])\n",
    "a.set(xlabel='Product Importance', ylabel = 'Number of Late Orders', title='Number of Late Orders by Product Importance')\n",
    "a.bar_label(a.containers[0])\n",
    "\n",
    "t = ecomm[ecomm['Arrival']==1].groupby('Warehouse Block').agg({'Arrival':'count'}).sort_values('Arrival',ascending=False) \n",
    "t.rename(columns={'Arrival':'Number of Late Orders'},inplace=True)\n",
    "late_whs = t.reset_index()\n",
    "b = sns.barplot(x='Warehouse Block', y='Number of Late Orders', data=late_whs, palette=\"icefire\",ax=axes[0,1])\n",
    "# b.set_xlabel('Warehouse Block', fontsize=12)\n",
    "# b.set_ylabel('Number of Late Orders', fontsize=12)\n",
    "# b.set_title('Number of Late Orders by Warehouse Block', fontsize=14)\n",
    "b.set(xlabel='Warehouse Block', ylabel = 'Number of Late Orders', title='Number of Late Orders by Warehouse Block')\n",
    "b.bar_label(b.containers[0])\n",
    "\n",
    "# autopct: display the percentage of each segment we can define the format.\n",
    "late_order_by_ship_mode = ecomm[ecomm['Arrival']==1].groupby('Mode Of Shipment').agg({'Mode Of Shipment':'count'})\n",
    "late_order_by_ship_mode.plot.pie(y='Mode Of Shipment', \n",
    "                                 autopct='%1.2f%%', \n",
    "                                 colors=['darkolivegreen','darkcyan','crimson'],\n",
    "                                 ylabel=\"\",\n",
    "                                 title='Number of Late Orders by Ship Mode',\n",
    "                                 fontsize=12,\n",
    "                                 ax=axes[1,0]);\n",
    "\n",
    "s = ecomm[ecomm['Arrival']==1][['Warehouse Block','Product Importance']].groupby(['Warehouse Block','Product Importance']).agg({'Product Importance':'count'})\n",
    "s.rename(columns={'Product Importance':'Number of Late Orders'}, inplace=True)\n",
    "whs_late_important = s.reset_index()\n",
    "c = sns.barplot(x='Warehouse Block', y='Number of Late Orders', hue='Product Importance', data=whs_late_important,ax=axes[1,1])\n",
    "c.set(xlabel='Warehouse Block', title='Product Importance of Late Orders by Warehouse Block')\n",
    "c.bar_label(c.containers[0])\n",
    "c.bar_label(c.containers[1])\n",
    "c.bar_label(c.containers[2])\n",
    "\n",
    "chosen_colors=['tomato','mediumaquamarine','orchid','darkgoldenrod', 'steelblue']\n",
    "whs_late_orders = ecomm[(ecomm['Arrival']==1) & (ecomm['Product Importance']=='high')].groupby('Warehouse Block').agg({'Arrival':'count'})\n",
    "whs_late_orders.rename(columns={'Arrival':'Number of Late Orders'},inplace=True)\n",
    "whs_late_orders.plot.pie(y='Number of Late Orders', autopct='%1.2f%%', colors=chosen_colors, fontsize=10, ylabel=\"\", ax=axes[2,0])\n",
    "axes[2,0].set_title('Highly Important But Late Orders by Warehouse Block')\n",
    "\n",
    "k=ecomm[(ecomm['Arrival']==1) & (ecomm['Product Importance']=='high')][['Warehouse Block','Mode Of Shipment']].groupby(['Warehouse Block','Mode Of Shipment']).agg({'Mode Of Shipment':'count'})\n",
    "k.rename(columns={'Mode Of Shipment':'Number of Highly Important but Late Orders'},inplace=True)\n",
    "whs_late_important_ship = k.reset_index()\n",
    "v = sns.barplot(x='Warehouse Block', y='Number of Highly Important but Late Orders', hue='Mode Of Shipment', data=whs_late_important_ship,ax=axes[2,1],palette=['#556b2f','#008b8b','#DC143C'])\n",
    "v.set(xlabel='Warehouse Block', title='Highly Important but Late Orders by Warehouse Block & Ship Mode')\n",
    "v.bar_label(v.containers[0])\n",
    "v.bar_label(v.containers[1])\n",
    "v.bar_label(v.containers[2]);"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
    "execution": {
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Number of Orders Handled</th>\n",
       "      <th>Number of Highly Important Orders Handled</th>\n",
       "      <th>Number of Highly Important Orders That Arrived Late</th>\n",
       "      <th>% of Late Orders (In Proportion Of Total Highly Important Orders Handled)</th>\n",
       "      <th>% of Late Orders (In Proportion Of Total Orders Handled)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Warehouse Block</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1833</td>\n",
       "      <td>165</td>\n",
       "      <td>111</td>\n",
       "      <td>67.27</td>\n",
       "      <td>6.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>1833</td>\n",
       "      <td>168</td>\n",
       "      <td>109</td>\n",
       "      <td>64.88</td>\n",
       "      <td>5.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>1834</td>\n",
       "      <td>166</td>\n",
       "      <td>104</td>\n",
       "      <td>62.65</td>\n",
       "      <td>5.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1833</td>\n",
       "      <td>146</td>\n",
       "      <td>100</td>\n",
       "      <td>68.49</td>\n",
       "      <td>5.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>3666</td>\n",
       "      <td>303</td>\n",
       "      <td>192</td>\n",
       "      <td>63.37</td>\n",
       "      <td>5.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Total Number of Orders Handled  \\\n",
       "Warehouse Block                                   \n",
       "A                                          1833   \n",
       "C                                          1833   \n",
       "D                                          1834   \n",
       "B                                          1833   \n",
       "F                                          3666   \n",
       "\n",
       "                 Number of Highly Important Orders Handled  \\\n",
       "Warehouse Block                                              \n",
       "A                                                      165   \n",
       "C                                                      168   \n",
       "D                                                      166   \n",
       "B                                                      146   \n",
       "F                                                      303   \n",
       "\n",
       "                 Number of Highly Important Orders That Arrived Late  \\\n",
       "Warehouse Block                                                        \n",
       "A                                                              111     \n",
       "C                                                              109     \n",
       "D                                                              104     \n",
       "B                                                              100     \n",
       "F                                                              192     \n",
       "\n",
       "                 % of Late Orders (In Proportion Of Total Highly Important Orders Handled)  \\\n",
       "Warehouse Block                                                                              \n",
       "A                                                            67.27                           \n",
       "C                                                            64.88                           \n",
       "D                                                            62.65                           \n",
       "B                                                            68.49                           \n",
       "F                                                            63.37                           \n",
       "\n",
       "                 % of Late Orders (In Proportion Of Total Orders Handled)  \n",
       "Warehouse Block                                                            \n",
       "A                                                             6.06         \n",
       "C                                                             5.95         \n",
       "D                                                             5.67         \n",
       "B                                                             5.46         \n",
       "F                                                             5.24         "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total orders handled by each warehouse block\n",
    "w = ecomm.groupby('Warehouse Block').agg({'ID':'count'}).sort_values('ID',ascending=False)\n",
    "w.rename(columns={'ID':'Total Number of Orders Handled'}, inplace=True)\n",
    "\n",
    "# total highly important orders handled by each warehouse block\n",
    "x = ecomm[ecomm['Product Importance']=='high'].groupby('Warehouse Block').agg({'Arrival':'count'})\n",
    "x.rename(columns={'Arrival':'Number of Highly Important Orders Handled'}, inplace=True)\n",
    "\n",
    "# total late but highly important orders handled by each warehouse block\n",
    "y = ecomm[(ecomm['Arrival']==1) & (ecomm['Product Importance']=='high')].groupby('Warehouse Block').agg({'Arrival':'count'})\n",
    "y.rename(columns={'Arrival':'Number of Highly Important Orders That Arrived Late'}, inplace=True)\n",
    "\n",
    "# concatenate w,x & y\n",
    "z = pd.concat([w,x,y], axis=1)\n",
    "z['% of Late Orders (In Proportion Of Total Highly Important Orders Handled)'] = round(z['Number of Highly Important Orders That Arrived Late'] / z['Number of Highly Important Orders Handled'] *100, 2)\n",
    "z['% of Late Orders (In Proportion Of Total Orders Handled)'] = round(z['Number of Highly Important Orders That Arrived Late'] / z['Total Number of Orders Handled'] *100, 2)\n",
    "z.sort_values('% of Late Orders (In Proportion Of Total Orders Handled)', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the charts and table above, we can infer that:\n",
    "1. Warehouse block F resulted the highest number of late orders. It is understandable as it handled the highest number of orders, and this might due to the lack of workforce in warehouse block F inefficient workflow or lack of facilities to ease the work of the staff. Anyhow, more investigation and visit to warehouse block F are needed to determine real issue of the high number of late orders.\n",
    "2. If we solely look at the data from the warehouse block A to D (warehouse E does not exist), we can clearly see that warehouse block A resulted the highest number of 'low importance' late orders, while warehouse block B resulted the highest number of 'medium importance' late orders and warehouse block A resulted the highest number of 'high importance' late orders.\n",
    "3. Warehouse block A resulted a relatively high number of highly important but late orders (111), which is 18.02% [111/ 616*100] out of the total late but highly important orders (616). \n",
    "4. The table above also showed that warehouse block A  resulted the highest percentage of late but highly important order (6.06% @ 165/1833*100) in proportion of the total orders that the warehouse block A handled. Besides, warehouse block A handled 165 highly important orders but 111 of them were late for arrival, which is 67.27%. Hence, it is suggested to check whether warehouse block A is taking the make an evaluation on the workflow in warehouse block A or check the distance between warehouse block A to destination to determine whether warehouse block A is always to far locations. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q3. What is the range of discount given for the products of different importance?"
   ]
  },
  {
   "cell_type": "code",
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    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=ecomm['Product Importance'],\n",
    "            y=ecomm['Discount Offered']);\n",
    "# sns.set(rc={'figure.figsize':(12,20)})\n",
    "# plt.figure(figsize=(8,10));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the boxplot above, we can infer that:\n",
    "1. The minimum and the median of discount offered for all product importance are the same.\n",
    "2. The range of discount for low and medium product importance are the same, which is 19-1=18. Meanwhile, the range of discount for high product importance is even larger, which is 39-1=38.There are many outliers for these categories. It is suggested that there need to have a maximum discount given for each of the categories. It is not advisable to give high discount to retain the customers as it might affect the company's revenue. It is recommended to improve the customer service and delivery service to retain the customers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 📌 Classification ML Algorithms - To Predict Customer Rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Cost Of The Product', 'Weight In Gms', 'Discount Offered',\n",
       "       'Warehouse Block_A', 'Warehouse Block_B', 'Warehouse Block_C',\n",
       "       'Warehouse Block_D', 'Warehouse Block_F', 'Mode Of Shipment_Flight',\n",
       "       'Mode Of Shipment_Road', 'Mode Of Shipment_Ship',\n",
       "       'Product Importance_high', 'Product Importance_low',\n",
       "       'Product Importance_medium', 'Customer Care Calls', 'Prior Purchases',\n",
       "       'Gender', 'Arrival', 'Customer Rating'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecomm['Gender'] = ecomm.Gender.map({'F':0, 'M':1})\n",
    "ecomm['Customer Rating'] = ecomm['Customer Rating'].map({5:0, 4:0, 3:0, 2:0, 1:1})\n",
    "dummy = pd.DataFrame(pd.get_dummies(ecomm[['Warehouse Block', 'Mode Of Shipment','Product Importance']]))\n",
    "ecomm1 = pd.DataFrame(scale(ecomm[['Cost Of The Product','Weight In Gms','Discount Offered']]),\n",
    "                      columns=['Cost Of The Product','Weight In Gms','Discount Offered'])\n",
    "ecomm_final = pd.concat([ecomm1,dummy,ecomm[['Customer Care Calls', 'Prior Purchases','Gender', 'Arrival','Customer Rating']]],\n",
    "                        axis=1)\n",
    "ecomm_final.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-22T12:33:53.303570Z",
     "iopub.status.busy": "2022-03-22T12:33:53.303333Z",
     "iopub.status.idle": "2022-03-22T12:33:53.327377Z",
     "shell.execute_reply": "2022-03-22T12:33:53.326740Z",
     "shell.execute_reply.started": "2022-03-22T12:33:53.303533Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cost Of The Product</th>\n",
       "      <th>Weight In Gms</th>\n",
       "      <th>Discount Offered</th>\n",
       "      <th>Warehouse Block_A</th>\n",
       "      <th>Warehouse Block_B</th>\n",
       "      <th>Warehouse Block_C</th>\n",
       "      <th>Warehouse Block_D</th>\n",
       "      <th>Warehouse Block_F</th>\n",
       "      <th>Mode Of Shipment_Flight</th>\n",
       "      <th>Mode Of Shipment_Road</th>\n",
       "      <th>Mode Of Shipment_Ship</th>\n",
       "      <th>Product Importance_high</th>\n",
       "      <th>Product Importance_low</th>\n",
       "      <th>Product Importance_medium</th>\n",
       "      <th>Customer Care Calls</th>\n",
       "      <th>Prior Purchases</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Arrival</th>\n",
       "      <th>Customer Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1627</th>\n",
       "      <td>-2.209622</td>\n",
       "      <td>-1.342881</td>\n",
       "      <td>2.013404</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9672</th>\n",
       "      <td>1.785294</td>\n",
       "      <td>-1.018170</td>\n",
       "      <td>-0.393292</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10496</th>\n",
       "      <td>0.848985</td>\n",
       "      <td>0.736856</td>\n",
       "      <td>-0.701843</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8076</th>\n",
       "      <td>-2.272043</td>\n",
       "      <td>0.884230</td>\n",
       "      <td>-0.455002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2163</th>\n",
       "      <td>-0.357812</td>\n",
       "      <td>-0.893423</td>\n",
       "      <td>1.211172</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with pd.option_context('display.max_columns',None):\n",
    "    display(HTML(ecomm_final.sample(5).to_html()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-22T12:33:53.329239Z",
     "iopub.status.busy": "2022-03-22T12:33:53.328619Z",
     "iopub.status.idle": "2022-03-22T12:33:53.337907Z",
     "shell.execute_reply": "2022-03-22T12:33:53.337225Z",
     "shell.execute_reply.started": "2022-03-22T12:33:53.329197Z"
    }
   },
   "outputs": [],
   "source": [
    "# split data into output and input\n",
    "X = ecomm_final.iloc[:,:-1] # inputs\n",
    "Y = ecomm_final['Customer Rating'] # outputs\n",
    "\n",
    "# split data into train data and test data\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size=0.25,shuffle=True) # test data is of 25% of the total records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-22T12:33:53.372369Z",
     "iopub.status.busy": "2022-03-22T12:33:53.371630Z",
     "iopub.status.idle": "2022-03-22T12:33:53.391310Z",
     "shell.execute_reply": "2022-03-22T12:33:53.390300Z",
     "shell.execute_reply.started": "2022-03-22T12:33:53.372308Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[KNeighborsClassifier(metric='euclidean', n_neighbors=8),\n",
       " DecisionTreeClassifier(criterion='entropy', max_depth=11, random_state=42)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# append different classification models into classifiers array \n",
    "classifiers=[]\n",
    "KNN_model = KNeighborsClassifier(n_neighbors=8, metric='euclidean')\n",
    "classifiers.append(KNN_model)\n",
    "DT_model = DT(criterion = 'entropy',max_depth=11, random_state=42)\n",
    "classifiers.append(DT_model)\n",
    "classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-22T12:33:53.394028Z",
     "iopub.status.busy": "2022-03-22T12:33:53.393029Z",
     "iopub.status.idle": "2022-03-22T12:34:07.387255Z",
     "shell.execute_reply": "2022-03-22T12:34:07.386470Z",
     "shell.execute_reply.started": "2022-03-22T12:33:53.393976Z"
    }
   },
   "outputs": [],
   "source": [
    "accuracy_train = []\n",
    "accuracy_test = []\n",
    "diff=[]\n",
    "for clf in classifiers:\n",
    "    clf.fit(X_train, Y_train)\n",
    "    pred_train = clf.predict(X_train)\n",
    "    pred_test = clf.predict(X_test)\n",
    "    acc_train = accuracy_score(Y_train, pred_train)\n",
    "    acc_test = accuracy_score(Y_test, pred_test)\n",
    "    accuracy_train.append(acc_train)\n",
    "    accuracy_test.append(acc_test)\n",
    "    #print(\"Accuracy of %s is %s\"%(clf, acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-22T12:34:07.389515Z",
     "iopub.status.busy": "2022-03-22T12:34:07.388992Z",
     "iopub.status.idle": "2022-03-22T12:34:07.404995Z",
     "shell.execute_reply": "2022-03-22T12:34:07.404045Z",
     "shell.execute_reply.started": "2022-03-22T12:34:07.389478Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Training Accuracy</th>\n",
       "      <th>Testing Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.804704</td>\n",
       "      <td>0.776000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.815129</td>\n",
       "      <td>0.771273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Model  Training Accuracy  Testing Accuracy\n",
       "0            KNN           0.804704          0.776000\n",
       "1  Decision Tree           0.815129          0.771273"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_result = pd.DataFrame(data={'Model':['KNN','Decision Tree'],\n",
    "                                     'Training Accuracy':accuracy_train,\n",
    "                                     'Testing Accuracy':accuracy_test})\n",
    "accuracy_result.sort_values('Testing Accuracy',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 📌 Selected Model : K-Neighbors Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-22T12:34:09.041088Z",
     "iopub.status.busy": "2022-03-22T12:34:09.040735Z",
     "iopub.status.idle": "2022-03-22T12:34:09.069621Z",
     "shell.execute_reply": "2022-03-22T12:34:09.068478Z",
     "shell.execute_reply.started": "2022-03-22T12:34:09.041042Z"
    }
   },
   "outputs": [],
   "source": [
    "KNN_model = KNeighborsClassifier(n_neighbors=11, metric='euclidean')\n",
    "KNN_model.fit(X_train,Y_train)\n",
    "pred_train_KNN = KNN_model.predict(X_train)\n",
    "pred_test_KNN = KNN_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-22T12:34:10.352085Z",
     "iopub.status.busy": "2022-03-22T12:34:10.351773Z",
     "iopub.status.idle": "2022-03-22T12:34:10.646697Z",
     "shell.execute_reply": "2022-03-22T12:34:10.645599Z",
     "shell.execute_reply.started": "2022-03-22T12:34:10.352052Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "group_names = ['True Neg', 'False Pos', 'False Neg', 'True Pos']\n",
    "train_cnf_matrix = confusion_matrix(Y_train,pred_train_KNN)\n",
    "train_counts = [\"{0:0.0f}\".format(value) for value in train_cnf_matrix.flatten()]\n",
    "train_percentage = [\"{0:.2%}\".format(value) for value in train_cnf_matrix .flatten()/np.sum(train_cnf_matrix)]\n",
    "train_labels = [f\"{v1}\\n{v2}\\n{v3}\" for v1, v2, v3 in zip(group_names,train_counts,train_percentage)]\n",
    "train_labels = np.asarray(train_labels).reshape(2,2)\n",
    "plt.figure(figsize = (16,5))\n",
    "sns.heatmap(train_cnf_matrix, annot=train_labels, fmt='', cmap='coolwarm')\n",
    "plt.title('Confusion matrix for train data (KNN model)',fontsize=16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-22T12:34:10.648344Z",
     "iopub.status.busy": "2022-03-22T12:34:10.648099Z",
     "iopub.status.idle": "2022-03-22T12:34:10.942573Z",
     "shell.execute_reply": "2022-03-22T12:34:10.941313Z",
     "shell.execute_reply.started": "2022-03-22T12:34:10.648313Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "group_names = ['True Neg', 'False Pos', 'False Neg', 'True Pos']\n",
    "test_cnf_matrix = confusion_matrix(Y_test,pred_test_KNN)\n",
    "test_counts = [\"{0:0.0f}\".format(value) for value in test_cnf_matrix.flatten()]\n",
    "test_percentage = [\"{0:.2%}\".format(value) for value in test_cnf_matrix .flatten()/np.sum(test_cnf_matrix)]\n",
    "test_labels = [f\"{v1}\\n{v2}\\n{v3}\" for v1, v2, v3 in zip(group_names,test_counts,test_percentage)]\n",
    "test_labels = np.asarray(test_labels).reshape(2,2)\n",
    "plt.figure(figsize = (16,5))\n",
    "sns.heatmap(test_cnf_matrix, annot=test_labels, fmt='', cmap='Blues')\n",
    "plt.title('Confusion matrix for test data (KNN model)',fontsize=16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-22T12:34:10.984676Z",
     "iopub.status.busy": "2022-03-22T12:34:10.984120Z",
     "iopub.status.idle": "2022-03-22T12:34:11.010005Z",
     "shell.execute_reply": "2022-03-22T12:34:11.009137Z",
     "shell.execute_reply.started": "2022-03-22T12:34:10.984633Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.99      0.89      6592\n",
      "           1       0.59      0.03      0.06      1657\n",
      "\n",
      "    accuracy                           0.80      8249\n",
      "   macro avg       0.70      0.51      0.48      8249\n",
      "weighted avg       0.76      0.80      0.72      8249\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"{classification_report(Y_train,pred_train_KNN)}\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-22T12:34:11.012457Z",
     "iopub.status.busy": "2022-03-22T12:34:11.011317Z",
     "iopub.status.idle": "2022-03-22T12:34:11.028443Z",
     "shell.execute_reply": "2022-03-22T12:34:11.026974Z",
     "shell.execute_reply.started": "2022-03-22T12:34:11.012410Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.98      0.88      2172\n",
      "           1       0.13      0.01      0.02       578\n",
      "\n",
      "    accuracy                           0.78      2750\n",
      "   macro avg       0.46      0.50      0.45      2750\n",
      "weighted avg       0.65      0.78      0.70      2750\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"{classification_report(Y_test,pred_test_KNN)}\");"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}