MIDAS NLP Task-3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center><font size = 5>TASK-3</font></center>\n",
    "### <center><font size = 3>MIDAS Summer Internship 2021 </font></center>\n",
    "### <center><font size = 3>Research Track: NLP </font></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Varun Khurana\n",
    "CSE, Indraprastha Institute of Information Technology, Delhi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[varun19124@iiitd.ac.in](mailto:varun19124@iiitd.ac.in)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "***Description of Task and Dataset***\n",
    "<br>\n",
    "We are given a dataset containing information about some products sold on an e-commerce site. Our task is to predict product categories by extracting features from the product descriptions. In essence, it is a classification problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Approach/Flow of work***\n",
    "<br>\n",
    "We will proceed to solve the classification problem as follows:\n",
    "- Cleaning the data\n",
    "- We will follow the 'Bag of Words' model. We will carry out Count Vectorisation followed by computing Term Frequency-Inverse Document Frequency.\n",
    "- Different Classification algorithms will be tried to determine which one of them gives the highest accuracy when run on testing data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us import the required libraries..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-2-65fd1826b13d>:8: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.config.list_physical_devices('GPU')` instead.\n",
      "True\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import keras\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "print(tf.test.is_gpu_available(\n",
    "    cuda_only=False, min_cuda_compute_capability=None\n",
    "))\n",
    "tf.config.list_physical_devices('GPU')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us fetch the dataset..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uniq_id</th>\n",
       "      <th>crawl_timestamp</th>\n",
       "      <th>product_url</th>\n",
       "      <th>product_name</th>\n",
       "      <th>product_category_tree</th>\n",
       "      <th>pid</th>\n",
       "      <th>retail_price</th>\n",
       "      <th>discounted_price</th>\n",
       "      <th>image</th>\n",
       "      <th>is_FK_Advantage_product</th>\n",
       "      <th>description</th>\n",
       "      <th>product_rating</th>\n",
       "      <th>overall_rating</th>\n",
       "      <th>brand</th>\n",
       "      <th>product_specifications</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c2d766ca982eca8304150849735ffef9</td>\n",
       "      <td>2016-03-25 22:59:23 +0000</td>\n",
       "      <td>http://www.flipkart.com/alisha-solid-women-s-c...</td>\n",
       "      <td>Alisha Solid Women's Cycling Shorts</td>\n",
       "      <td>[\"Clothing &gt;&gt; Women's Clothing &gt;&gt; Lingerie, Sl...</td>\n",
       "      <td>SRTEH2FF9KEDEFGF</td>\n",
       "      <td>999.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>[\"http://img5a.flixcart.com/image/short/u/4/a/...</td>\n",
       "      <td>False</td>\n",
       "      <td>Key Features of Alisha Solid Women's Cycling S...</td>\n",
       "      <td>No rating available</td>\n",
       "      <td>No rating available</td>\n",
       "      <td>Alisha</td>\n",
       "      <td>{\"product_specification\"=&gt;[{\"key\"=&gt;\"Number of ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7f7036a6d550aaa89d34c77bd39a5e48</td>\n",
       "      <td>2016-03-25 22:59:23 +0000</td>\n",
       "      <td>http://www.flipkart.com/fabhomedecor-fabric-do...</td>\n",
       "      <td>FabHomeDecor Fabric Double Sofa Bed</td>\n",
       "      <td>[\"Furniture &gt;&gt; Living Room Furniture &gt;&gt; Sofa B...</td>\n",
       "      <td>SBEEH3QGU7MFYJFY</td>\n",
       "      <td>32157.0</td>\n",
       "      <td>22646.0</td>\n",
       "      <td>[\"http://img6a.flixcart.com/image/sofa-bed/j/f...</td>\n",
       "      <td>False</td>\n",
       "      <td>FabHomeDecor Fabric Double Sofa Bed (Finish Co...</td>\n",
       "      <td>No rating available</td>\n",
       "      <td>No rating available</td>\n",
       "      <td>FabHomeDecor</td>\n",
       "      <td>{\"product_specification\"=&gt;[{\"key\"=&gt;\"Installati...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>f449ec65dcbc041b6ae5e6a32717d01b</td>\n",
       "      <td>2016-03-25 22:59:23 +0000</td>\n",
       "      <td>http://www.flipkart.com/aw-bellies/p/itmeh4grg...</td>\n",
       "      <td>AW Bellies</td>\n",
       "      <td>[\"Footwear &gt;&gt; Women's Footwear &gt;&gt; Ballerinas &gt;...</td>\n",
       "      <td>SHOEH4GRSUBJGZXE</td>\n",
       "      <td>999.0</td>\n",
       "      <td>499.0</td>\n",
       "      <td>[\"http://img5a.flixcart.com/image/shoe/7/z/z/r...</td>\n",
       "      <td>False</td>\n",
       "      <td>Key Features of AW Bellies Sandals Wedges Heel...</td>\n",
       "      <td>No rating available</td>\n",
       "      <td>No rating available</td>\n",
       "      <td>AW</td>\n",
       "      <td>{\"product_specification\"=&gt;[{\"key\"=&gt;\"Ideal For\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0973b37acd0c664e3de26e97e5571454</td>\n",
       "      <td>2016-03-25 22:59:23 +0000</td>\n",
       "      <td>http://www.flipkart.com/alisha-solid-women-s-c...</td>\n",
       "      <td>Alisha Solid Women's Cycling Shorts</td>\n",
       "      <td>[\"Clothing &gt;&gt; Women's Clothing &gt;&gt; Lingerie, Sl...</td>\n",
       "      <td>SRTEH2F6HUZMQ6SJ</td>\n",
       "      <td>699.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>[\"http://img5a.flixcart.com/image/short/6/2/h/...</td>\n",
       "      <td>False</td>\n",
       "      <td>Key Features of Alisha Solid Women's Cycling S...</td>\n",
       "      <td>No rating available</td>\n",
       "      <td>No rating available</td>\n",
       "      <td>Alisha</td>\n",
       "      <td>{\"product_specification\"=&gt;[{\"key\"=&gt;\"Number of ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bc940ea42ee6bef5ac7cea3fb5cfbee7</td>\n",
       "      <td>2016-03-25 22:59:23 +0000</td>\n",
       "      <td>http://www.flipkart.com/sicons-all-purpose-arn...</td>\n",
       "      <td>Sicons All Purpose Arnica Dog Shampoo</td>\n",
       "      <td>[\"Pet Supplies &gt;&gt; Grooming &gt;&gt; Skin &amp; Coat Care...</td>\n",
       "      <td>PSOEH3ZYDMSYARJ5</td>\n",
       "      <td>220.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>[\"http://img5a.flixcart.com/image/pet-shampoo/...</td>\n",
       "      <td>False</td>\n",
       "      <td>Specifications of Sicons All Purpose Arnica Do...</td>\n",
       "      <td>No rating available</td>\n",
       "      <td>No rating available</td>\n",
       "      <td>Sicons</td>\n",
       "      <td>{\"product_specification\"=&gt;[{\"key\"=&gt;\"Pet Type\",...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            uniq_id            crawl_timestamp  \\\n",
       "0  c2d766ca982eca8304150849735ffef9  2016-03-25 22:59:23 +0000   \n",
       "1  7f7036a6d550aaa89d34c77bd39a5e48  2016-03-25 22:59:23 +0000   \n",
       "2  f449ec65dcbc041b6ae5e6a32717d01b  2016-03-25 22:59:23 +0000   \n",
       "3  0973b37acd0c664e3de26e97e5571454  2016-03-25 22:59:23 +0000   \n",
       "4  bc940ea42ee6bef5ac7cea3fb5cfbee7  2016-03-25 22:59:23 +0000   \n",
       "\n",
       "                                         product_url  \\\n",
       "0  http://www.flipkart.com/alisha-solid-women-s-c...   \n",
       "1  http://www.flipkart.com/fabhomedecor-fabric-do...   \n",
       "2  http://www.flipkart.com/aw-bellies/p/itmeh4grg...   \n",
       "3  http://www.flipkart.com/alisha-solid-women-s-c...   \n",
       "4  http://www.flipkart.com/sicons-all-purpose-arn...   \n",
       "\n",
       "                            product_name  \\\n",
       "0    Alisha Solid Women's Cycling Shorts   \n",
       "1    FabHomeDecor Fabric Double Sofa Bed   \n",
       "2                             AW Bellies   \n",
       "3    Alisha Solid Women's Cycling Shorts   \n",
       "4  Sicons All Purpose Arnica Dog Shampoo   \n",
       "\n",
       "                               product_category_tree               pid  \\\n",
       "0  [\"Clothing >> Women's Clothing >> Lingerie, Sl...  SRTEH2FF9KEDEFGF   \n",
       "1  [\"Furniture >> Living Room Furniture >> Sofa B...  SBEEH3QGU7MFYJFY   \n",
       "2  [\"Footwear >> Women's Footwear >> Ballerinas >...  SHOEH4GRSUBJGZXE   \n",
       "3  [\"Clothing >> Women's Clothing >> Lingerie, Sl...  SRTEH2F6HUZMQ6SJ   \n",
       "4  [\"Pet Supplies >> Grooming >> Skin & Coat Care...  PSOEH3ZYDMSYARJ5   \n",
       "\n",
       "   retail_price  discounted_price  \\\n",
       "0         999.0             379.0   \n",
       "1       32157.0           22646.0   \n",
       "2         999.0             499.0   \n",
       "3         699.0             267.0   \n",
       "4         220.0             210.0   \n",
       "\n",
       "                                               image  is_FK_Advantage_product  \\\n",
       "0  [\"http://img5a.flixcart.com/image/short/u/4/a/...                    False   \n",
       "1  [\"http://img6a.flixcart.com/image/sofa-bed/j/f...                    False   \n",
       "2  [\"http://img5a.flixcart.com/image/shoe/7/z/z/r...                    False   \n",
       "3  [\"http://img5a.flixcart.com/image/short/6/2/h/...                    False   \n",
       "4  [\"http://img5a.flixcart.com/image/pet-shampoo/...                    False   \n",
       "\n",
       "                                         description       product_rating  \\\n",
       "0  Key Features of Alisha Solid Women's Cycling S...  No rating available   \n",
       "1  FabHomeDecor Fabric Double Sofa Bed (Finish Co...  No rating available   \n",
       "2  Key Features of AW Bellies Sandals Wedges Heel...  No rating available   \n",
       "3  Key Features of Alisha Solid Women's Cycling S...  No rating available   \n",
       "4  Specifications of Sicons All Purpose Arnica Do...  No rating available   \n",
       "\n",
       "        overall_rating         brand  \\\n",
       "0  No rating available        Alisha   \n",
       "1  No rating available  FabHomeDecor   \n",
       "2  No rating available            AW   \n",
       "3  No rating available        Alisha   \n",
       "4  No rating available        Sicons   \n",
       "\n",
       "                              product_specifications  \n",
       "0  {\"product_specification\"=>[{\"key\"=>\"Number of ...  \n",
       "1  {\"product_specification\"=>[{\"key\"=>\"Installati...  \n",
       "2  {\"product_specification\"=>[{\"key\"=>\"Ideal For\"...  \n",
       "3  {\"product_specification\"=>[{\"key\"=>\"Number of ...  \n",
       "4  {\"product_specification\"=>[{\"key\"=>\"Pet Type\",...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"datasets/flipkart_com-ecommerce_sample.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for null values..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "uniq_id                       0\n",
       "crawl_timestamp               0\n",
       "product_url                   0\n",
       "product_name                  0\n",
       "product_category_tree         0\n",
       "pid                           0\n",
       "retail_price                 78\n",
       "discounted_price             78\n",
       "image                         3\n",
       "is_FK_Advantage_product       0\n",
       "description                   2\n",
       "product_rating                0\n",
       "overall_rating                0\n",
       "brand                      5864\n",
       "product_specifications       14\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use product description as the main feature to predict the product category. We will drop the rows having null values in description."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna(how='any', subset=['description'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>description</th>\n",
       "      <th>product_category_tree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Key Features of Alisha Solid Women's Cycling S...</td>\n",
       "      <td>[\"Clothing &gt;&gt; Women's Clothing &gt;&gt; Lingerie, Sl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FabHomeDecor Fabric Double Sofa Bed (Finish Co...</td>\n",
       "      <td>[\"Furniture &gt;&gt; Living Room Furniture &gt;&gt; Sofa B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Key Features of AW Bellies Sandals Wedges Heel...</td>\n",
       "      <td>[\"Footwear &gt;&gt; Women's Footwear &gt;&gt; Ballerinas &gt;...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Key Features of Alisha Solid Women's Cycling S...</td>\n",
       "      <td>[\"Clothing &gt;&gt; Women's Clothing &gt;&gt; Lingerie, Sl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Specifications of Sicons All Purpose Arnica Do...</td>\n",
       "      <td>[\"Pet Supplies &gt;&gt; Grooming &gt;&gt; Skin &amp; Coat Care...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         description  \\\n",
       "0  Key Features of Alisha Solid Women's Cycling S...   \n",
       "1  FabHomeDecor Fabric Double Sofa Bed (Finish Co...   \n",
       "2  Key Features of AW Bellies Sandals Wedges Heel...   \n",
       "3  Key Features of Alisha Solid Women's Cycling S...   \n",
       "4  Specifications of Sicons All Purpose Arnica Do...   \n",
       "\n",
       "                               product_category_tree  \n",
       "0  [\"Clothing >> Women's Clothing >> Lingerie, Sl...  \n",
       "1  [\"Furniture >> Living Room Furniture >> Sofa B...  \n",
       "2  [\"Footwear >> Women's Footwear >> Ballerinas >...  \n",
       "3  [\"Clothing >> Women's Clothing >> Lingerie, Sl...  \n",
       "4  [\"Pet Supplies >> Grooming >> Skin & Coat Care...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df[['description', 'product_category_tree']]\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the product category tree we will find the primary product category i.e. the root of the product category tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_primary_category(tree):\n",
    "    tree_list = tree.split('>>')\n",
    "    category = tree_list[0]\n",
    "    return category\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>description</th>\n",
       "      <th>product_category_tree</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Key Features of Alisha Solid Women's Cycling S...</td>\n",
       "      <td>[\"Clothing &gt;&gt; Women's Clothing &gt;&gt; Lingerie, Sl...</td>\n",
       "      <td>[\"Clothing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FabHomeDecor Fabric Double Sofa Bed (Finish Co...</td>\n",
       "      <td>[\"Furniture &gt;&gt; Living Room Furniture &gt;&gt; Sofa B...</td>\n",
       "      <td>[\"Furniture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Key Features of AW Bellies Sandals Wedges Heel...</td>\n",
       "      <td>[\"Footwear &gt;&gt; Women's Footwear &gt;&gt; Ballerinas &gt;...</td>\n",
       "      <td>[\"Footwear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Key Features of Alisha Solid Women's Cycling S...</td>\n",
       "      <td>[\"Clothing &gt;&gt; Women's Clothing &gt;&gt; Lingerie, Sl...</td>\n",
       "      <td>[\"Clothing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Specifications of Sicons All Purpose Arnica Do...</td>\n",
       "      <td>[\"Pet Supplies &gt;&gt; Grooming &gt;&gt; Skin &amp; Coat Care...</td>\n",
       "      <td>[\"Pet Supplies</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         description  \\\n",
       "0  Key Features of Alisha Solid Women's Cycling S...   \n",
       "1  FabHomeDecor Fabric Double Sofa Bed (Finish Co...   \n",
       "2  Key Features of AW Bellies Sandals Wedges Heel...   \n",
       "3  Key Features of Alisha Solid Women's Cycling S...   \n",
       "4  Specifications of Sicons All Purpose Arnica Do...   \n",
       "\n",
       "                               product_category_tree         category  \n",
       "0  [\"Clothing >> Women's Clothing >> Lingerie, Sl...      [\"Clothing   \n",
       "1  [\"Furniture >> Living Room Furniture >> Sofa B...     [\"Furniture   \n",
       "2  [\"Footwear >> Women's Footwear >> Ballerinas >...      [\"Footwear   \n",
       "3  [\"Clothing >> Women's Clothing >> Lingerie, Sl...      [\"Clothing   \n",
       "4  [\"Pet Supplies >> Grooming >> Skin & Coat Care...  [\"Pet Supplies   "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_df = df2.loc[:]\n",
    "product_df['category'] = df2['product_category_tree'].apply(filter_primary_category)\n",
    "product_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "description              0\n",
       "product_category_tree    0\n",
       "category                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for presence of null values, if any\n",
    "product_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>description</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Key Features of Alisha Solid Women's Cycling S...</td>\n",
       "      <td>[\"Clothing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FabHomeDecor Fabric Double Sofa Bed (Finish Co...</td>\n",
       "      <td>[\"Furniture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Key Features of AW Bellies Sandals Wedges Heel...</td>\n",
       "      <td>[\"Footwear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Key Features of Alisha Solid Women's Cycling S...</td>\n",
       "      <td>[\"Clothing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Specifications of Sicons All Purpose Arnica Do...</td>\n",
       "      <td>[\"Pet Supplies</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         description         category\n",
       "0  Key Features of Alisha Solid Women's Cycling S...      [\"Clothing \n",
       "1  FabHomeDecor Fabric Double Sofa Bed (Finish Co...     [\"Furniture \n",
       "2  Key Features of AW Bellies Sandals Wedges Heel...      [\"Footwear \n",
       "3  Key Features of Alisha Solid Women's Cycling S...      [\"Clothing \n",
       "4  Specifications of Sicons All Purpose Arnica Do...  [\"Pet Supplies "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping redundant column \"product_category_tree\"\n",
    "product_df = product_df[['description', 'category']]\n",
    "product_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We wil now further clean the data by:\n",
    "1. Removing punctuation\n",
    "2. Converting text data to lower case\n",
    "3. Stripping leading and trailing whitespaces\n",
    "4. Removing stopwords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Removing Punctuation\n",
    "This will remove special punctuation characters that are redundant in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string\n",
    "\n",
    "def remove_punctuation_marks(text):\n",
    "    text_clean = \"\"\n",
    "    for i in text:\n",
    "        if i not in string.punctuation:\n",
    "            text_clean += i\n",
    "    return text_clean\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>description</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>key features of alisha solid womens cycling sh...</td>\n",
       "      <td>clothing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fabhomedecor fabric double sofa bed finish col...</td>\n",
       "      <td>furniture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>key features of aw bellies sandals wedges heel...</td>\n",
       "      <td>footwear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>key features of alisha solid womens cycling sh...</td>\n",
       "      <td>clothing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>specifications of sicons all purpose arnica do...</td>\n",
       "      <td>pet supplies</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         description      category\n",
       "0  key features of alisha solid womens cycling sh...      clothing\n",
       "1  fabhomedecor fabric double sofa bed finish col...     furniture\n",
       "2  key features of aw bellies sandals wedges heel...      footwear\n",
       "3  key features of alisha solid womens cycling sh...      clothing\n",
       "4  specifications of sicons all purpose arnica do...  pet supplies"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_df['category'] = product_df['category'].apply(remove_punctuation_marks)\n",
    "product_df['description'] = product_df['description'].apply(remove_punctuation_marks)\n",
    "\n",
    "# For the sake of uniformity, we will convert column entries to lower case\n",
    "product_df['category'] = product_df['category'].str.lower()\n",
    "product_df['description'] = product_df['description'].str.lower()\n",
    "\n",
    "# Removing trailing and leading whitespaces, if any\n",
    "product_df['category'] = product_df['category'].str.strip()\n",
    "product_df['description'] = product_df['description'].str.strip()\n",
    "\n",
    "product_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Removing Stopwords\n",
    "This will ensure redundant words are removed and we are left with only meaningful words which give some information.\n",
    "For this we will use the corpus of English language stopwords from the Natural Language Toolkit (NLTK)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\varun\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "nltk.download('stopwords')\n",
    "from nltk.corpus import stopwords\n",
    "# stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_stopwords(text):\n",
    "    word_list = []\n",
    "    for i in text.split():\n",
    "        if i not in stopwords.words('english'):\n",
    "            word_list.append(i)\n",
    "    text_clean = \" \".join(word_list)\n",
    "    return text_clean\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "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>description</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>key features alisha solid womens cycling short...</td>\n",
       "      <td>clothing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fabhomedecor fabric double sofa bed finish col...</td>\n",
       "      <td>furniture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>key features aw bellies sandals wedges heel ca...</td>\n",
       "      <td>footwear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>key features alisha solid womens cycling short...</td>\n",
       "      <td>clothing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>specifications sicons purpose arnica dog shamp...</td>\n",
       "      <td>pet supplies</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         description      category\n",
       "0  key features alisha solid womens cycling short...      clothing\n",
       "1  fabhomedecor fabric double sofa bed finish col...     furniture\n",
       "2  key features aw bellies sandals wedges heel ca...      footwear\n",
       "3  key features alisha solid womens cycling short...      clothing\n",
       "4  specifications sicons purpose arnica dog shamp...  pet supplies"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_df['description'] = product_df['description'].apply(remove_stopwords)\n",
    "product_df['category'] = product_df['category'].apply(remove_stopwords)\n",
    "product_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "description    17503\n",
       "category         265\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_df.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, as can be seen above, there are hundreds of product categories. But a large percentage of them are very infrequent and occur rarely.\n",
    "<br>\n",
    "<br>\n",
    "Let us identify the most frequent product categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clothing                                          6197\n",
      "jewellery                                         3531\n",
      "footwear                                          1227\n",
      "mobiles accessories                               1099\n",
      "automotive                                        1012\n",
      "                                                  ... \n",
      "kraft seeds dill herb seed 50 per packet             1\n",
      "prime printed 8 seater table cover multicolor        1\n",
      "areon luxurious fragrance long lasting carhome       1\n",
      "planet waves woodstock strap multicolor              1\n",
      "anuradha art stylish hair clip black                 1\n",
      "Name: category, Length: 265, dtype: int64 \n",
      "\n",
      "\n",
      "FREQUENT CATEGORIES:\n",
      " ['clothing', 'jewellery', 'footwear', 'mobiles accessories', 'automotive', 'home decor festive needs', 'beauty personal care', 'home furnishing', 'kitchen dining', 'computers', 'watches', 'baby care', 'tools hardware', 'toys school supplies', 'pens stationery', 'bags wallets belts', 'furniture', 'sports fitness', 'cameras accessories', 'home improvement', 'health personal care appliances', 'sunglasses', 'gaming', 'pet supplies', 'home kitchen', 'home entertainment', 'ebooks', 'eyewear'] \n",
      "\n",
      "length = 28\n"
     ]
    }
   ],
   "source": [
    "category_count = product_df['category'].value_counts()\n",
    "print(category_count, \"\\n\\n\")\n",
    "\n",
    "frequent_category_list = []\n",
    "\n",
    "for i in zip(category_count.values, category_count.index):\n",
    "    if i[0] >= 10:\n",
    "        frequent_category_list.append(i[1])\n",
    "        \n",
    "print(\"FREQUENT CATEGORIES:\\n\", frequent_category_list, \"\\n\\nlength =\", len(frequent_category_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several product categories that occur with very little frequency in our data. This can cause class imbalance problem. So, let us remove the entries with less frequent product categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 19666 entries, 0 to 19999\n",
      "Data columns (total 2 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   description  19666 non-null  object\n",
      " 1   category     19666 non-null  object\n",
      "dtypes: object(2)\n",
      "memory usage: 460.9+ KB\n"
     ]
    }
   ],
   "source": [
    "product_df_clean = product_df[(product_df['category']).isin(frequent_category_list)]\n",
    "\n",
    "product_df_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Let us plot a **Word Cloud** to visualise the common product categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from wordcloud import WordCloud\n",
    "\n",
    "category_count = product_df_clean['category'].value_counts()\n",
    "\n",
    "category_string = \"\"\n",
    "for i in range(len(frequent_category_list)):\n",
    "    category_string += (frequent_category_list[i] + \" \" )*(category_count.values[i])\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.title(\"WORDCLOUD of common product categories\")\n",
    "plt.imshow(WordCloud(background_color='white', collocations=False).generate(category_string))\n",
    "plt.savefig('assets/category_wordcloud.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use a **Bar Graph** to visualise the distribution of common product categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "plt.ylabel(\"PRODUCT CATEGORIES\")\n",
    "plt.xlabel(\"FREQUENCY\", labelpad=20)\n",
    "bar_graph = sns.barplot(y=category_count.index, x=category_count.values)\n",
    "plt.savefig('assets/category_barplot.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us replace each product category (in text form) with a corresponding numerical label i.e. each category is mapped to a unique integer. This will help our model to perform classification on the categorical attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_category(category_text):\n",
    "    idx = frequent_category_list.index(category_text)\n",
    "    return idx\n",
    "\n",
    "df_final = product_df_clean.loc[:]\n",
    "df_final['# category'] = df_final['category'].apply(convert_category)\n",
    "\n",
    "df_final.to_csv('datasets/clean_data.csv')\n",
    "\n",
    "df_final = df_final[['description', '# category']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>description</th>\n",
       "      <th># category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>key features alisha solid womens cycling short...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fabhomedecor fabric double sofa bed finish col...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>key features aw bellies sandals wedges heel ca...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>key features alisha solid womens cycling short...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>specifications sicons purpose arnica dog shamp...</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         description  # category\n",
       "0  key features alisha solid womens cycling short...           0\n",
       "1  fabhomedecor fabric double sofa bed finish col...          16\n",
       "2  key features aw bellies sandals wedges heel ca...           2\n",
       "3  key features alisha solid womens cycling short...           0\n",
       "4  specifications sicons purpose arnica dog shamp...          23"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, that we are done with cleaning the text data, let us move ahead to extract features from the given text data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Perform Count Vectorisation followed by Term-Frequency and Inverse Document Frequency (TF-IDF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Here, our goal is to effectively extract features from the text product description so that a product category can be predicted.\n",
    "- We first obtain the sentence vectors by applying count vectorisation.\n",
    "- Thereafter, we compute the TF-IDF to get obtain weighted vectors such that more weight is given to particular words that form a distinguishing feature for a category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19666, 31954)\n",
      "(19666, 31954)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "\n",
    "count_vectorizer = CountVectorizer()\n",
    "product_count = count_vectorizer.fit_transform(df_final['description'])\n",
    "print(product_count.shape)\n",
    "\n",
    "tfidf_transformer = TfidfTransformer()\n",
    "product_tfidf = tfidf_transformer.fit_transform(product_count)\n",
    "\n",
    "print(product_tfidf.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19666, 31954)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.DataFrame(product_tfidf.toarray())\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19666,)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df_final['# category']\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, all pre-processing steps are complete and we are all set to build our model for text classification."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will first split the data into training and testing sets, keeping the test set ratio = 30%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hereafter, we will try using different classification approaches and compare the accuracy attained with each of them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_model = MultinomialNB()\n",
    "nb_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's test our classification model by predicting product categories for the test set description. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5900,)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat = nb_model.predict(X_test)\n",
    "y_hat.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below are the perfomance metrics of our classification model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 precision    recall  f1-score   support\n",
      "\n",
      "                       clothing       0.80      1.00      0.89      1859\n",
      "                      jewellery       0.66      1.00      0.79      1081\n",
      "                       footwear       1.00      0.78      0.87       369\n",
      "            mobiles accessories       0.90      0.91      0.90       334\n",
      "                     automotive       0.92      0.95      0.94       303\n",
      "       home decor festive needs       0.96      0.86      0.91       282\n",
      "           beauty personal care       1.00      0.39      0.56       218\n",
      "                home furnishing       0.93      0.69      0.79       188\n",
      "                 kitchen dining       0.99      0.61      0.76       206\n",
      "                      computers       1.00      0.58      0.73       164\n",
      "                        watches       1.00      0.88      0.93       161\n",
      "                      baby care       0.96      0.34      0.51       145\n",
      "                 tools hardware       1.00      0.70      0.82       129\n",
      "           toys school supplies       0.75      0.04      0.07        79\n",
      "                pens stationery       1.00      0.20      0.34        88\n",
      "             bags wallets belts       1.00      0.03      0.05        76\n",
      "                      furniture       1.00      0.03      0.06        61\n",
      "                 sports fitness       0.00      0.00      0.00        42\n",
      "            cameras accessories       0.00      0.00      0.00        24\n",
      "               home improvement       0.00      0.00      0.00        24\n",
      "health personal care appliances       0.00      0.00      0.00        18\n",
      "                     sunglasses       0.00      0.00      0.00        13\n",
      "                         gaming       0.00      0.00      0.00        11\n",
      "                   pet supplies       0.00      0.00      0.00         8\n",
      "                   home kitchen       0.00      0.00      0.00         7\n",
      "             home entertainment       0.00      0.00      0.00         7\n",
      "                         ebooks       0.00      0.00      0.00         1\n",
      "                        eyewear       0.00      0.00      0.00         2\n",
      "\n",
      "                       accuracy                           0.81      5900\n",
      "                      macro avg       0.57      0.36      0.39      5900\n",
      "                   weighted avg       0.83      0.81      0.78      5900\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\varun\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_hat, target_names=frequent_category_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Naive Bayes Classifier model = 81.31 %\n"
     ]
    }
   ],
   "source": [
    "nb_accuracy = accuracy_score(y_test, y_hat)\n",
    "print(\"The accuracy of Naive Bayes Classifier model = %.2f\" %(nb_accuracy*100), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try using other classification algorithms to check if we can achieve higher accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "### Logistic Regression\n",
    "<br>\n",
    "This will give the probability of a product belonging to each category. The category with the highest probability will get selected as the predicted category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.5)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr_model = LogisticRegression(C=0.5)\n",
    "lr_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat = lr_model.predict(X_test)\n",
    "y_hat_probability = lr_model.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 precision    recall  f1-score   support\n",
      "\n",
      "                       clothing       0.93      1.00      0.96      1859\n",
      "                      jewellery       0.97      1.00      0.98      1081\n",
      "                       footwear       0.96      0.98      0.97       369\n",
      "            mobiles accessories       0.93      0.95      0.94       334\n",
      "                     automotive       0.96      0.97      0.97       303\n",
      "       home decor festive needs       0.89      0.96      0.93       282\n",
      "           beauty personal care       0.97      0.91      0.94       218\n",
      "                home furnishing       0.90      0.95      0.92       188\n",
      "                 kitchen dining       0.95      0.87      0.91       206\n",
      "                      computers       0.92      0.91      0.91       164\n",
      "                        watches       1.00      0.99      0.99       161\n",
      "                      baby care       0.92      0.70      0.79       145\n",
      "                 tools hardware       1.00      0.86      0.92       129\n",
      "           toys school supplies       0.78      0.77      0.78        79\n",
      "                pens stationery       0.98      0.65      0.78        88\n",
      "             bags wallets belts       0.94      0.87      0.90        76\n",
      "                      furniture       0.96      0.90      0.93        61\n",
      "                 sports fitness       1.00      0.57      0.73        42\n",
      "            cameras accessories       1.00      0.67      0.80        24\n",
      "               home improvement       1.00      0.29      0.45        24\n",
      "health personal care appliances       0.00      0.00      0.00        18\n",
      "                     sunglasses       1.00      0.85      0.92        13\n",
      "                         gaming       1.00      0.45      0.62        11\n",
      "                   pet supplies       1.00      0.50      0.67         8\n",
      "                   home kitchen       0.00      0.00      0.00         7\n",
      "             home entertainment       0.00      0.00      0.00         7\n",
      "                         ebooks       0.00      0.00      0.00         1\n",
      "                        eyewear       0.00      0.00      0.00         2\n",
      "\n",
      "                       accuracy                           0.94      5900\n",
      "                      macro avg       0.78      0.66      0.70      5900\n",
      "                   weighted avg       0.94      0.94      0.94      5900\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\varun\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_hat, target_names=frequent_category_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Logistic Regression model = 94.17 %\n"
     ]
    }
   ],
   "source": [
    "lr_accuracy = accuracy_score(y_test, y_hat)\n",
    "print(\"The accuracy of Logistic Regression model = %.2f\" %(lr_accuracy*100), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "### Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "svm_model = LinearSVC(C=0.25)\n",
    "svm_model.fit(X_train, y_train)\n",
    "\n",
    "y_hat = svm_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 precision    recall  f1-score   support\n",
      "\n",
      "                       clothing       0.98      1.00      0.99      1859\n",
      "                      jewellery       0.99      1.00      1.00      1081\n",
      "                       footwear       1.00      0.99      0.99       369\n",
      "            mobiles accessories       0.96      0.98      0.97       334\n",
      "                     automotive       0.98      0.99      0.99       303\n",
      "       home decor festive needs       0.97      0.99      0.98       282\n",
      "           beauty personal care       0.99      0.98      0.98       218\n",
      "                home furnishing       0.92      0.98      0.95       188\n",
      "                 kitchen dining       0.95      0.92      0.94       206\n",
      "                      computers       0.95      0.98      0.96       164\n",
      "                        watches       1.00      1.00      1.00       161\n",
      "                      baby care       0.91      0.79      0.85       145\n",
      "                 tools hardware       1.00      0.94      0.97       129\n",
      "           toys school supplies       0.77      0.89      0.82        79\n",
      "                pens stationery       0.95      0.72      0.82        88\n",
      "             bags wallets belts       0.93      0.99      0.96        76\n",
      "                      furniture       0.95      0.98      0.97        61\n",
      "                 sports fitness       0.95      0.83      0.89        42\n",
      "            cameras accessories       1.00      0.96      0.98        24\n",
      "               home improvement       0.95      0.79      0.86        24\n",
      "health personal care appliances       1.00      1.00      1.00        18\n",
      "                     sunglasses       1.00      1.00      1.00        13\n",
      "                         gaming       0.83      0.45      0.59        11\n",
      "                   pet supplies       0.88      0.88      0.88         8\n",
      "                   home kitchen       0.80      0.57      0.67         7\n",
      "             home entertainment       0.50      0.29      0.36         7\n",
      "                         ebooks       0.00      0.00      0.00         1\n",
      "                        eyewear       1.00      1.00      1.00         2\n",
      "\n",
      "                       accuracy                           0.97      5900\n",
      "                      macro avg       0.90      0.85      0.87      5900\n",
      "                   weighted avg       0.97      0.97      0.97      5900\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\varun\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_hat, target_names=frequent_category_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Support Vector Machine model = 97.36 %\n"
     ]
    }
   ],
   "source": [
    "svm_accuracy = accuracy_score(y_test, y_hat)\n",
    "print(\"The accuracy of Support Vector Machine model = %.2f\" %(svm_accuracy*100), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "### Classification Using Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# input layer nodes\n",
    "num_cols = X.shape[1]\n",
    "\n",
    "# output layer nodes\n",
    "num_classes = len(frequent_category_list)\n",
    "\n",
    "# One-hot encoding for target variable y i.e. product category\n",
    "y_train_cat = to_categorical(y_train)\n",
    "y_test_cat = to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us build a neural network with 2 hidden layers!\n",
    "<br>\n",
    "- We will use the ***ReLU (Rectified Linear Unit)*** as the activation function.\n",
    "- The output layer consists of as many number of nodes as the number of product categories.\n",
    "- In the output layer, the ***softmax function*** is used as the activation function so that each node gives the probability of a product belonging to that category. The category with the highest probability is selected as the predicted category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining function to construct neural network\n",
    "def classify_model():\n",
    "    # creating model\n",
    "    model = Sequential()\n",
    "    model.add(Dense(50, activation='relu', input_shape=(num_cols,)))\n",
    "    model.add(Dense(100, activation='relu'))\n",
    "    model.add(Dense(num_classes, activation='softmax'))\n",
    "    \n",
    "    # compiling model\n",
    "    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "431/431 - 13s - loss: 1.0550 - accuracy: 0.7617 - val_loss: 0.2830 - val_accuracy: 0.9414\n",
      "Epoch 2/15\n",
      "431/431 - 6s - loss: 0.1406 - accuracy: 0.9702 - val_loss: 0.1500 - val_accuracy: 0.9653\n",
      "Epoch 3/15\n",
      "431/431 - 5s - loss: 0.0414 - accuracy: 0.9918 - val_loss: 0.1197 - val_accuracy: 0.9720\n",
      "Epoch 4/15\n",
      "431/431 - 6s - loss: 0.0157 - accuracy: 0.9976 - val_loss: 0.1131 - val_accuracy: 0.9731\n",
      "Epoch 5/15\n",
      "431/431 - 6s - loss: 0.0074 - accuracy: 0.9988 - val_loss: 0.1082 - val_accuracy: 0.9737\n",
      "Epoch 6/15\n",
      "431/431 - 7s - loss: 0.0039 - accuracy: 0.9990 - val_loss: 0.1076 - val_accuracy: 0.9744\n",
      "Epoch 7/15\n",
      "431/431 - 7s - loss: 0.0032 - accuracy: 0.9990 - val_loss: 0.1064 - val_accuracy: 0.9749\n",
      "Epoch 8/15\n",
      "431/431 - 5s - loss: 0.0018 - accuracy: 0.9996 - val_loss: 0.1085 - val_accuracy: 0.9751\n",
      "Epoch 9/15\n",
      "431/431 - 5s - loss: 0.0017 - accuracy: 0.9993 - val_loss: 0.1130 - val_accuracy: 0.9744\n",
      "Epoch 10/15\n",
      "431/431 - 5s - loss: 0.0016 - accuracy: 0.9993 - val_loss: 0.1131 - val_accuracy: 0.9749\n",
      "Epoch 11/15\n",
      "431/431 - 6s - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.1154 - val_accuracy: 0.9744\n",
      "Epoch 12/15\n",
      "431/431 - 5s - loss: 9.9995e-04 - accuracy: 0.9996 - val_loss: 0.1148 - val_accuracy: 0.9753\n",
      "Epoch 13/15\n",
      "431/431 - 6s - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.1195 - val_accuracy: 0.9747\n",
      "Epoch 14/15\n",
      "431/431 - 5s - loss: 9.7451e-04 - accuracy: 0.9997 - val_loss: 0.1189 - val_accuracy: 0.9753\n",
      "Epoch 15/15\n",
      "431/431 - 5s - loss: 0.0011 - accuracy: 0.9996 - val_loss: 0.1164 - val_accuracy: 0.9758\n"
     ]
    }
   ],
   "source": [
    "nnet_model = classify_model()\n",
    "\n",
    "history = nnet_model.fit(X_train.values, y_train_cat, validation_data=(X_test.values, y_test_cat), epochs=15, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat_cat = nnet_model.predict(X_test)\n",
    "y_hat = np.argmax(y_hat_cat, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Neural Network Classification model = 97.5763 %\n"
     ]
    }
   ],
   "source": [
    "result = nnet_model.evaluate(X_test, y_test_cat, verbose=0)\n",
    "nnet_accuracy = result[1]\n",
    "print(\"The accuracy of Neural Network Classification model = %.4f\" %(nnet_accuracy*100), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history_dict = history.history\n",
    "history_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot accuracy curves\n",
    "\n",
    "plt.figure(figsize = (10,5))\n",
    "plt.plot(history_dict['accuracy'], '-.', color='red')\n",
    "plt.plot(history_dict['val_accuracy'], color='blue')\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"Accuracy Curves for the Model\")\n",
    "plt.legend(['Training Accuracy', 'Validation Accuracy'], loc='lower right')\n",
    "plt.savefig('assets/nnet_accuracy_curve.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot loss curves\n",
    "\n",
    "plt.figure(figsize = (10,5))\n",
    "plt.plot(history_dict['loss'], '-.', color='red')\n",
    "plt.plot(history_dict['val_loss'], color='blue')\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.title(\"Loss Curves for the Model\")\n",
    "plt.legend(['Training Loss', 'Validation Loss'], loc='upper right')\n",
    "plt.savefig('assets/nnet_loss_curve.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualise the accuracy of our model using a **confusion matrix** as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c_matrix = confusion_matrix(y_test, y_hat)\n",
    "\n",
    "# Converting to dataframe\n",
    "df_cm = pd.DataFrame(c_matrix)\n",
    "df_cm.index.name = 'TRUE PRODUCT CATEGORIES'\n",
    "df_cm.columns.name = 'PREDICTED PRODUCT CATEGORIES'\n",
    "cm_center = 0.35 * (df_cm.values.max() - df_cm.values.min())\n",
    "\n",
    "plt.figure(figsize=(20,15))\n",
    "plt.title(\"CONFUSION MATRIX\")\n",
    "cm_plot = sns.heatmap(df_cm, annot=True, linewidths=.005, cmap= \"coolwarm\", center=cm_center)\n",
    "\n",
    "for _, spine in cm_plot.spines.items():\n",
    "    spine.set_visible(True)\n",
    "\n",
    "plt.savefig('assets/nnet_confusion_matrix.png', dpi=500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 precision    recall  f1-score   support\n",
      "\n",
      "                       clothing       0.99      1.00      0.99      1859\n",
      "                      jewellery       1.00      1.00      1.00      1081\n",
      "                       footwear       0.99      0.99      0.99       369\n",
      "            mobiles accessories       0.98      0.98      0.98       334\n",
      "                     automotive       0.98      0.99      0.99       303\n",
      "       home decor festive needs       0.97      0.99      0.98       282\n",
      "           beauty personal care       0.96      0.96      0.96       218\n",
      "                home furnishing       0.94      0.98      0.96       188\n",
      "                 kitchen dining       0.93      0.94      0.94       206\n",
      "                      computers       0.95      0.98      0.96       164\n",
      "                        watches       1.00      0.99      1.00       161\n",
      "                      baby care       0.92      0.85      0.88       145\n",
      "                 tools hardware       0.99      0.94      0.96       129\n",
      "           toys school supplies       0.86      0.82      0.84        79\n",
      "                pens stationery       0.86      0.83      0.84        88\n",
      "             bags wallets belts       0.95      0.97      0.96        76\n",
      "                      furniture       1.00      0.95      0.97        61\n",
      "                 sports fitness       0.90      0.88      0.89        42\n",
      "            cameras accessories       1.00      0.96      0.98        24\n",
      "               home improvement       0.91      0.83      0.87        24\n",
      "health personal care appliances       0.95      1.00      0.97        18\n",
      "                     sunglasses       1.00      1.00      1.00        13\n",
      "                         gaming       0.86      0.55      0.67        11\n",
      "                   pet supplies       1.00      0.88      0.93         8\n",
      "                   home kitchen       0.62      0.71      0.67         7\n",
      "             home entertainment       0.67      0.57      0.62         7\n",
      "                         ebooks       0.00      0.00      0.00         1\n",
      "                        eyewear       1.00      1.00      1.00         2\n",
      "\n",
      "                       accuracy                           0.98      5900\n",
      "                      macro avg       0.90      0.88      0.89      5900\n",
      "                   weighted avg       0.98      0.98      0.98      5900\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_hat, target_names=frequent_category_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## Summary of Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us visualise the relative performance of each classification model in the form of a graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_name = ['Naive Bayes', 'Logistic Regression', 'Support Vector Machine', 'Neural Network']\n",
    "model_accuracy = [nb_accuracy, lr_accuracy, svm_accuracy, nnet_accuracy]\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(model_name, model_accuracy, linestyle='dashed', marker='s', color='blue', markerfacecolor='red', ms='10')\n",
    "plt.xlabel(\"CLASSIFICATION MODEL\", labelpad=20)\n",
    "plt.ylabel(\"ACCURACY\", labelpad=20)\n",
    "plt.title(\"Comparison of model performance\")\n",
    "plt.savefig(\"assets/model_comparison.png\", dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The performance of all the classification approaches used above can be summarised as follows:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Classification Approach | Accuracy ( % ) |\n",
    "| :-: | :-: |\n",
    "| Naive Bayes | 81.31 |\n",
    "| Logistic Regression | 94.17 |\n",
    "| Support Vector Machine| 97.36 |\n",
    "| **Neural Network** | ***97.58*** |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly, the highest accuracy for predicting product categories is obtained by the ***Neural Network model***.\n",
    "<br><br>\n",
    "Let us save this model for future reference for quickly predicting product categories, without having to train our model again. This help save time and computational resources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "nnet_model.save('nnet_product_classifier_model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All functions used for cleaning text data can be combined together as follows (for reference):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Text cleaning pipeline - all functions used above combined together\n",
    "\n",
    "# Cleaning product description text\n",
    "def clean_description(text):\n",
    "    # Remove punctuation marks\n",
    "    text_clean = remove_punctuation_marks(text)\n",
    "    \n",
    "    # Remove stopwords\n",
    "    text_clean = remove_stopwords(text_clean)\n",
    "    \n",
    "    return text_clean\n",
    "    \n",
    "    \n",
    "# Cleaning product category\n",
    "def clean_category(text):\n",
    "    # Filter out the primary category from the tree\n",
    "    text_clean = filter_primary_category(text)\n",
    "    \n",
    "    # Remove punctuation marks\n",
    "    text_clean = remove_punctuation_marks(text_clean)\n",
    "    \n",
    "    # Convert to lower case and strip trailing or leading whitespaces\n",
    "    text_clean = text_clean.lower().strip()\n",
    "    \n",
    "    # Remove stopwords\n",
    "    text_clean = remove_stopwords(text_clean)\n",
    "    \n",
    "    return text_clean\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "- Official documentations of all major library functions used in the notebook\n",
    "- https://machinelearningmastery.com/how-to-configure-the-number-of-layers-and-nodes-in-a-neural-network/\n",
    "- https://www.youtube.com/watch?v=km7pxKy4UHU\n",
    "- https://machinelearnings.co/text-classification-using-neural-networks-f5cd7b8765c6\n",
    "- https://stackoverflow.com/questions/61550026/valueerror-shapes-none-1-and-none-3-are-incompatible\n",
    "- https://www.geeksforgeeks.org/multiclass-classification-using-scikit-learn/\n",
    "- https://medium.com/machine-learning-101/chapter-2-svm-support-vector-machine-theory-f0812effc72#:~:text=called%20kernel%20trick-,Regularization,avoid%20misclassifying%20each%20training%20example.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For generating requirements.txt file\n",
    "!pip freeze -l > requirements.txt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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