data_engineering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "upK_Y3nNXhe-"
   },
   "source": [
    "# Description\n",
    "\n",
    "* Dataset of products from different online fashion retailers\n",
    "* Below are tasks performed on this data:\n",
    "    1. Handling null values.\n",
    "    2. Discard records with invalid IDs.\n",
    "    3. Feature extraction from columns 'price', 'brand' and 'media'.\n",
    "    4. Data pre-processing on new features.\n",
    "    5. Removing product with offer price greater than regular price.\n",
    "    6. Check on outliers and dropping them.\n",
    "    7. Removing records with invalid URLs.\n",
    "    8. Data visualization.\n",
    "    9. NLP on product description.\n",
    "    10. Unsupervised clustering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VG-aFtnpaN2D"
   },
   "source": [
    "### Importing required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "B5IOv3VPY360",
    "outputId": "933f5ad4-d12f-44a4-a1ed-f5c1ade33649"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas.io.json import json_normalize\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import urllib\n",
    "from urllib.parse import urlparse\n",
    "\n",
    "import nltk\n",
    "nltk.download('punkt')\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "\n",
    "from wordcloud import WordCloud\n",
    "from IPython.display import Image\n",
    "from IPython.core.display import HTML\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.cluster import DBSCAN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wQ967EL4aYWh"
   },
   "source": [
    "### Mount google drive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "6viTsl8DaW0Z",
    "outputId": "1d5b76bd-4733-419c-b85d-f0ee41daa881"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/gdrive')\n",
    "path = '/content/gdrive/My Drive/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Nl10DM28awPD"
   },
   "source": [
    "### Importing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 530
    },
    "colab_type": "code",
    "id": "EeOqnvTEa5gt",
    "outputId": "46b251b0-d045-4afb-fab7-706103ca62cf"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (4) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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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>_id</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "      <th>website_id</th>\n",
       "      <th>sku</th>\n",
       "      <th>url</th>\n",
       "      <th>brand</th>\n",
       "      <th>media</th>\n",
       "      <th>description_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5d0b8aca0db7220b86cb4035</td>\n",
       "      <td>Joules Top Dog Underwear Three Pack</td>\n",
       "      <td>{'offer_price': {'currency': 'GBP', 'value': 3...</td>\n",
       "      <td>5cff5e7fe40f4900046735fa</td>\n",
       "      <td>312838</td>\n",
       "      <td>www.next.co.uk/style/st355408#312838</td>\n",
       "      <td>{'name': 'joules', 'sub_brand': ''}</td>\n",
       "      <td>{'standard': [{'order': 1, 'url': 'https://xcd...</td>\n",
       "      <td>95% Cotton, 5% Elastane.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5d0b8aca0db7220b86cb4036</td>\n",
       "      <td>Figleaves Cheetah Satin Pyjama Set</td>\n",
       "      <td>{'offer_price': {'currency': 'GBP', 'value': 2...</td>\n",
       "      <td>5cff5e7fe40f4900046735fa</td>\n",
       "      <td>319571</td>\n",
       "      <td>https://www.next.co.uk/style/st324987#319571</td>\n",
       "      <td>{'name': 'figleaves', 'sub_brand': ''}</td>\n",
       "      <td>{'standard': [{'order': 1, 'url': 'https://xcd...</td>\n",
       "      <td>100% Polyester.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5d0b8aca0db7220b86cb4037</td>\n",
       "      <td>Nike Solid 4\" Swim Short</td>\n",
       "      <td>{'offer_price': {'currency': 'GBP', 'value': 1...</td>\n",
       "      <td>5cff5e7fe40f4900046735fa</td>\n",
       "      <td>335026</td>\n",
       "      <td>https://www.next.co.uk/style/st400645#335026</td>\n",
       "      <td>{'name': 'nike', 'sub_brand': ''}</td>\n",
       "      <td>{'standard': [{'order': 1, 'url': 'https://xcd...</td>\n",
       "      <td>Nike Swim Boys' Solid Lap 4 Volley Short is a ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5d0b8aca0db7220b86cb4038</td>\n",
       "      <td>Collection Luxe Orchid</td>\n",
       "      <td>{'offer_price': {'currency': 'GBP', 'value': 1...</td>\n",
       "      <td>5cff5e7fe40f4900046735fa</td>\n",
       "      <td>552266</td>\n",
       "      <td>https://www.next.co.uk/style/st262195#552266</td>\n",
       "      <td>{'name': 'next', 'sub_brand': ''}</td>\n",
       "      <td>{'standard': [{'order': 1, 'url': 'https://xcd...</td>\n",
       "      <td>Height 85cm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5d0b8aca0db7220b86cb4039</td>\n",
       "      <td>River Island White Sleeveless Blazer</td>\n",
       "      <td>{'offer_price': {'currency': 'GBP', 'value': 5...</td>\n",
       "      <td>5cff5e7fe40f4900046735fa</td>\n",
       "      <td>680971</td>\n",
       "      <td>https://www.next.co.uk/style/st440132#680971</td>\n",
       "      <td>{'name': 'river island', 'sub_brand': ''}</td>\n",
       "      <td>{'standard': [{'order': 1, 'url': 'https://xcd...</td>\n",
       "      <td>100% Polyester.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        _id  ...                                   description_text\n",
       "0  5d0b8aca0db7220b86cb4035  ...                           95% Cotton, 5% Elastane.\n",
       "1  5d0b8aca0db7220b86cb4036  ...                                    100% Polyester.\n",
       "2  5d0b8aca0db7220b86cb4037  ...  Nike Swim Boys' Solid Lap 4 Volley Short is a ...\n",
       "3  5d0b8aca0db7220b86cb4038  ...                                        Height 85cm\n",
       "4  5d0b8aca0db7220b86cb4039  ...                                    100% Polyester.\n",
       "\n",
       "[5 rows x 9 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(path+'data_engineering_assignment.txt', sep='|', error_bad_lines=False, warn_bad_lines=False)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EOtdVCkT4TUL"
   },
   "source": [
    "### Check for Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "K0FXpr-oMGc4",
    "outputId": "aca5b529-1358-446b-e715-cde1c2536352"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "_id                    0\n",
       "name                5163\n",
       "price               5163\n",
       "website_id          5163\n",
       "sku                 5576\n",
       "url                 5163\n",
       "brand               5163\n",
       "media               5163\n",
       "description_text    5631\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QNXjeHxY4hN2"
   },
   "source": [
    "#### Invalid IDs values and all missing values for other features of it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "-BCQSIgsI8B-",
    "outputId": "2378811e-45af-4451-b278-2279080df02c"
   },
   "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>_id</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "      <th>website_id</th>\n",
       "      <th>sku</th>\n",
       "      <th>url</th>\n",
       "      <th>brand</th>\n",
       "      <th>media</th>\n",
       "      <th>description_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Canvas upper with TOMS toe-stitch, and elastic...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>TOMS classic suede insole with cushion for com...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Latex arch insert for added support</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>One-piece outsole for flexibility and durability</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>With every pair of shoes you purchase, TOMS wi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  _id  ... description_text\n",
       "16  Canvas upper with TOMS toe-stitch, and elastic...  ...              NaN\n",
       "17  TOMS classic suede insole with cushion for com...  ...              NaN\n",
       "18                Latex arch insert for added support  ...              NaN\n",
       "19   One-piece outsole for flexibility and durability  ...              NaN\n",
       "20  With every pair of shoes you purchase, TOMS wi...  ...              NaN\n",
       "\n",
       "[5 rows x 9 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.website_id.isna()].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7aOmKaFG49hl"
   },
   "source": [
    "* Dropping rows with invalid IDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "NAl8WB7tMfvV",
    "outputId": "0b7959fe-8e5d-496f-d966-d0fcd1d948a0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "_id                   0\n",
       "name                  0\n",
       "price                 0\n",
       "website_id            0\n",
       "sku                 413\n",
       "url                   0\n",
       "brand                 0\n",
       "media                 0\n",
       "description_text    468\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(data[data.website_id.isna()].index, axis=0, inplace=True)\n",
    "data = data.reset_index()\n",
    "data.drop('index', axis=1, inplace=True)\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PJg2F6zB5aRN"
   },
   "source": [
    "#### Null values for SKU codes and Product description\n",
    "* Still we see few null values for SKU Codes and Product description.\n",
    "* We can replace them with value 'No SKU Code' and 'No description' and retain other feature for products for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "emNPJHXnTq3r",
    "outputId": "dc489bc3-aace-475c-fd89-60219ec8c474"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "_id                 0\n",
       "name                0\n",
       "price               0\n",
       "website_id          0\n",
       "sku                 0\n",
       "url                 0\n",
       "brand               0\n",
       "media               0\n",
       "description_text    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data.sku.isna(), 'sku'] = 'No SKU Code'\n",
    "data.loc[data.description_text.isna(), 'description_text'] = 'No description'\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7EQNqbNF6Zf-"
   },
   "source": [
    "* A glance at shape of data we have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "5GAmgNwhyZnR",
    "outputId": "d4e659c5-8608-4723-836b-2d2948528408"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows : 318278 Columns : 9\n"
     ]
    }
   ],
   "source": [
    "print('Rows :', data.shape[0], 'Columns :', data.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gi8iW0qG7W9c"
   },
   "source": [
    "### Feature extraction from column 'price', 'brand', and 'media'\n",
    "* Data in this column are in JSON Objects\n",
    "* So, we can extract those key-values pairs and add them to features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yEnjXkPA84rU"
   },
   "source": [
    "#### Extracting from column 'price'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "XL1pQIhAHa2k",
    "outputId": "f20499dc-e4f5-4274-d0c1-d2c42b64ae71"
   },
   "outputs": [
    {
     "data": {
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       "        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>offer_price.currency</th>\n",
       "      <th>offer_price.value</th>\n",
       "      <th>regular_price.currency</th>\n",
       "      <th>regular_price.value</th>\n",
       "      <th>basket_price.value</th>\n",
       "      <th>basket_price.currency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GBP</td>\n",
       "      <td>35.0</td>\n",
       "      <td>GBP</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GBP</td>\n",
       "      <td>25.0</td>\n",
       "      <td>GBP</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GBP</td>\n",
       "      <td>15.0</td>\n",
       "      <td>GBP</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GBP</td>\n",
       "      <td>150.0</td>\n",
       "      <td>GBP</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GBP</td>\n",
       "      <td>55.0</td>\n",
       "      <td>GBP</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  offer_price.currency  ...  basket_price.currency\n",
       "0                  GBP  ...                    NaN\n",
       "1                  GBP  ...                    NaN\n",
       "2                  GBP  ...                    NaN\n",
       "3                  GBP  ...                    NaN\n",
       "4                  GBP  ...                    NaN\n",
       "\n",
       "[5 rows x 6 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['price'] = data['price'].apply(lambda x : eval(x))\n",
    "price_data = json_normalize(data['price'])\n",
    "data.drop('price', axis=1, inplace=True)\n",
    "price_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "ciRythXaNXTS",
    "outputId": "f00dda30-935b-4b4a-c216-958f4f6e1597"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['GBP'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_data['offer_price.currency'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CdxuILEZOn-x"
   },
   "source": [
    "* Either Currency is GBP or NaN so dropping currency columns from price_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "mxNskAhkNnSP",
    "outputId": "ac437403-7503-4d0c-bf8a-33e7aaa7efd7"
   },
   "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>offer_price.value</th>\n",
       "      <th>regular_price.value</th>\n",
       "      <th>basket_price.value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   offer_price.value  regular_price.value  basket_price.value\n",
       "0               35.0                 35.0                 NaN\n",
       "1               25.0                 25.0                 NaN\n",
       "2               15.0                 15.0                 NaN\n",
       "3              150.0                150.0                 NaN\n",
       "4               55.0                 55.0                 NaN"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop = ['offer_price.currency', 'regular_price.currency', 'basket_price.currency']\n",
    "for col in drop:\n",
    "  price_data.drop(col, axis=1, inplace=True)\n",
    "price_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "V2FO4VuX9jqu"
   },
   "source": [
    "#### Extracting from column 'brand'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "PJ3U1eRg0Zz2",
    "outputId": "5918d3e1-0f55-40ec-ee87-c1c4684cf31d"
   },
   "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>name</th>\n",
       "      <th>sub_brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>joules</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>figleaves</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>nike</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>next</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>river island</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name sub_brand\n",
       "0        joules          \n",
       "1     figleaves          \n",
       "2          nike          \n",
       "3          next          \n",
       "4  river island          "
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['brand'] = data['brand'].apply(lambda x : eval(x))\n",
    "brand_data = json_normalize(data['brand'])\n",
    "data.drop('brand', axis=1, inplace=True)\n",
    "brand_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "i05dpZIC9zlV"
   },
   "source": [
    "* We got some missing values in form of empty strings\n",
    "* Replacing them with 'No brand name' for name and 'No sub-brand name' for sub_brand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "Vi1dpny0Qvod",
    "outputId": "c4b5ca78-a92b-4b92-9029-aca65873c547"
   },
   "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>name</th>\n",
       "      <th>sub_brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>joules</td>\n",
       "      <td>No sub-brand name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>figleaves</td>\n",
       "      <td>No sub-brand name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>nike</td>\n",
       "      <td>No sub-brand name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>next</td>\n",
       "      <td>No sub-brand name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>river island</td>\n",
       "      <td>No sub-brand name</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name          sub_brand\n",
       "0        joules  No sub-brand name\n",
       "1     figleaves  No sub-brand name\n",
       "2          nike  No sub-brand name\n",
       "3          next  No sub-brand name\n",
       "4  river island  No sub-brand name"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brand_data.loc[brand_data.name.values == '', 'name'] = 'No brand name'\n",
    "brand_data.loc[brand_data.sub_brand.values == '', 'sub_brand'] = 'No sub-brand name'\n",
    "brand_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "28fGjQ3W-PFF"
   },
   "source": [
    "* We already got a column with 'name' for product name\n",
    "* So renaming brand columns with 'brand_name', 'sub_brand_name'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hjWum7sJW9v4"
   },
   "outputs": [],
   "source": [
    "brand_data.columns = ['brand_name', 'sub_brand_name']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9JnNa6sW-mOy"
   },
   "source": [
    "#### Extracting from column 'media'\n",
    "* This contains three keys standard, thumbnail and large.\n",
    "* Extracting URL values for those and creating columns in data for it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Qk8IjaNK62dN"
   },
   "outputs": [],
   "source": [
    "data['media'] = data['media'].apply(lambda x : eval(x))\n",
    "standard_url = []\n",
    "thumbnail_url = []\n",
    "large_url = []\n",
    "for val in data['media']:\n",
    "  try:\n",
    "      standard_url.append(val['standard'][0]['url'])\n",
    "  except IndexError:\n",
    "      standard_url.append('No media URL')\n",
    "\n",
    "  try:\n",
    "      thumbnail_url.append(val['thumbnail'][0]['url'])\n",
    "  except (IndexError, KeyError):\n",
    "      thumbnail_url.append('No media URL')\n",
    "\n",
    "  try:\n",
    "      large_url.append(val['large'][0]['url'])\n",
    "  except (IndexError, KeyError):\n",
    "      large_url.append('No media URL')\n",
    "\n",
    "data['standard_media_url'] = standard_url\n",
    "data['thumbnail_media_url'] = thumbnail_url\n",
    "data['large_media_url'] = large_url\n",
    "\n",
    "data.drop('media', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ALS23JLl_NTD"
   },
   "source": [
    "* Again a check on shape before concatenating them with original data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "cv3GU1MxZjoM",
    "outputId": "2d083b5a-7546-4af0-8564-0040392b9b78"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((318278, 9), (318278, 3), (318278, 2))"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape, price_data.shape, brand_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HOO7B3Ia_jn3"
   },
   "source": [
    "#### Concatenating all feature obtained with original data\n",
    "* Also a quick glance on our data with new feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 496
    },
    "colab_type": "code",
    "id": "npoJQmlrUfI5",
    "outputId": "13f18c9c-10ae-4c1f-b9e9-251e53d09494"
   },
   "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>_id</th>\n",
       "      <th>name</th>\n",
       "      <th>website_id</th>\n",
       "      <th>sku</th>\n",
       "      <th>url</th>\n",
       "      <th>description_text</th>\n",
       "      <th>standard_media_url</th>\n",
       "      <th>thumbnail_media_url</th>\n",
       "      <th>large_media_url</th>\n",
       "      <th>offer_price.value</th>\n",
       "      <th>regular_price.value</th>\n",
       "      <th>basket_price.value</th>\n",
       "      <th>brand_name</th>\n",
       "      <th>sub_brand_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5d0b8aca0db7220b86cb4035</td>\n",
       "      <td>Joules Top Dog Underwear Three Pack</td>\n",
       "      <td>5cff5e7fe40f4900046735fa</td>\n",
       "      <td>312838</td>\n",
       "      <td>www.next.co.uk/style/st355408#312838</td>\n",
       "      <td>95% Cotton, 5% Elastane.</td>\n",
       "      <td>https://xcdn.next.co.uk/Common/Items/Default/D...</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>joules</td>\n",
       "      <td>No sub-brand name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5d0b8aca0db7220b86cb4036</td>\n",
       "      <td>Figleaves Cheetah Satin Pyjama Set</td>\n",
       "      <td>5cff5e7fe40f4900046735fa</td>\n",
       "      <td>319571</td>\n",
       "      <td>https://www.next.co.uk/style/st324987#319571</td>\n",
       "      <td>100% Polyester.</td>\n",
       "      <td>https://xcdn.next.co.uk/Common/Items/Default/D...</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>figleaves</td>\n",
       "      <td>No sub-brand name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5d0b8aca0db7220b86cb4037</td>\n",
       "      <td>Nike Solid 4\" Swim Short</td>\n",
       "      <td>5cff5e7fe40f4900046735fa</td>\n",
       "      <td>335026</td>\n",
       "      <td>https://www.next.co.uk/style/st400645#335026</td>\n",
       "      <td>Nike Swim Boys' Solid Lap 4 Volley Short is a ...</td>\n",
       "      <td>https://xcdn.next.co.uk/Common/Items/Default/D...</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nike</td>\n",
       "      <td>No sub-brand name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5d0b8aca0db7220b86cb4038</td>\n",
       "      <td>Collection Luxe Orchid</td>\n",
       "      <td>5cff5e7fe40f4900046735fa</td>\n",
       "      <td>552266</td>\n",
       "      <td>https://www.next.co.uk/style/st262195#552266</td>\n",
       "      <td>Height 85cm</td>\n",
       "      <td>https://xcdn.next.co.uk/Common/Items/Default/D...</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>next</td>\n",
       "      <td>No sub-brand name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5d0b8aca0db7220b86cb4039</td>\n",
       "      <td>River Island White Sleeveless Blazer</td>\n",
       "      <td>5cff5e7fe40f4900046735fa</td>\n",
       "      <td>680971</td>\n",
       "      <td>https://www.next.co.uk/style/st440132#680971</td>\n",
       "      <td>100% Polyester.</td>\n",
       "      <td>https://xcdn.next.co.uk/Common/Items/Default/D...</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>river island</td>\n",
       "      <td>No sub-brand name</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        _id  ...     sub_brand_name\n",
       "0  5d0b8aca0db7220b86cb4035  ...  No sub-brand name\n",
       "1  5d0b8aca0db7220b86cb4036  ...  No sub-brand name\n",
       "2  5d0b8aca0db7220b86cb4037  ...  No sub-brand name\n",
       "3  5d0b8aca0db7220b86cb4038  ...  No sub-brand name\n",
       "4  5d0b8aca0db7220b86cb4039  ...  No sub-brand name\n",
       "\n",
       "[5 rows x 14 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.concat([data, price_data, brand_data], axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bWeXd14xEhGb"
   },
   "source": [
    "### Data description\n",
    "* '_id' : Product IDs\n",
    "* 'name' : Product Names\n",
    "* 'website_id' : IDs for company websites\n",
    "* 'sku' : SKU codes for particular products\n",
    "* 'url' : URLs for product listed on websites\n",
    "* 'description_text' : Product descriptions\n",
    "* 'media': URLs for Standard , Large and Thumbnails media\n",
    "* 'price': Product price in GBP (Offer and Regular prices)\n",
    "* 'brand': Brand name of product"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-f-bXSWEDHjt"
   },
   "source": [
    "### Data pre-processing on newly obtained data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2x3NThDx_5WQ"
   },
   "source": [
    "#### Again null values check with new columns, checking with percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 272
    },
    "colab_type": "code",
    "id": "sdhxG7ihgRdt",
    "outputId": "be7aa7c0-099f-404f-8757-148e70163253"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "_id                     0.000000\n",
       "name                    0.000000\n",
       "website_id              0.000000\n",
       "sku                     0.000000\n",
       "url                     0.000000\n",
       "description_text        0.000000\n",
       "standard_media_url      0.000000\n",
       "thumbnail_media_url     0.000000\n",
       "large_media_url         0.000000\n",
       "offer_price.value       0.104311\n",
       "regular_price.value     0.104311\n",
       "basket_price.value     65.510340\n",
       "brand_name              0.000000\n",
       "sub_brand_name          0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()/data.shape[0] * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "C4648X8CAJ_6"
   },
   "source": [
    "* 65.51% of 'basket_price.value' are null values, hence we can drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Lzgx6YeFqb8c"
   },
   "outputs": [],
   "source": [
    "data.drop('basket_price.value', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XkVbv37HCUFD"
   },
   "source": [
    "* Also 'offer_price.value' and 'regular_price.value' are just 0.10% null. So, we can drop that paticular rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "colab_type": "code",
    "id": "ESUL50tVra51",
    "outputId": "9e5b64a4-e098-4a62-878c-dee2f8f349e1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "_id                    0\n",
       "name                   0\n",
       "website_id             0\n",
       "sku                    0\n",
       "url                    0\n",
       "description_text       0\n",
       "standard_media_url     0\n",
       "thumbnail_media_url    0\n",
       "large_media_url        0\n",
       "offer_price.value      0\n",
       "regular_price.value    0\n",
       "brand_name             0\n",
       "sub_brand_name         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(data[data['regular_price.value'].isna()].index, axis=0, inplace=True)\n",
    "data = data.reset_index()\n",
    "data.drop('index', axis=1, inplace=True)\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KV7CGUFHDuPo"
   },
   "source": [
    "#### Quick check on stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 444
    },
    "colab_type": "code",
    "id": "mIhp9Basr-44",
    "outputId": "95d2642c-b5b4-4ff4-cd41-67fab08d469f"
   },
   "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>_id</th>\n",
       "      <th>name</th>\n",
       "      <th>website_id</th>\n",
       "      <th>sku</th>\n",
       "      <th>url</th>\n",
       "      <th>description_text</th>\n",
       "      <th>standard_media_url</th>\n",
       "      <th>thumbnail_media_url</th>\n",
       "      <th>large_media_url</th>\n",
       "      <th>offer_price.value</th>\n",
       "      <th>regular_price.value</th>\n",
       "      <th>brand_name</th>\n",
       "      <th>sub_brand_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>317946</td>\n",
       "      <td>317946</td>\n",
       "      <td>317946</td>\n",
       "      <td>317946</td>\n",
       "      <td>317946</td>\n",
       "      <td>317946</td>\n",
       "      <td>317946</td>\n",
       "      <td>317946</td>\n",
       "      <td>317946</td>\n",
       "      <td>3.179460e+05</td>\n",
       "      <td>3.179460e+05</td>\n",
       "      <td>317946</td>\n",
       "      <td>317946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>317946</td>\n",
       "      <td>257399</td>\n",
       "      <td>4</td>\n",
       "      <td>299797</td>\n",
       "      <td>317946</td>\n",
       "      <td>275543</td>\n",
       "      <td>311110</td>\n",
       "      <td>238074</td>\n",
       "      <td>108855</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4776</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>5d49a619a3ecbbd90d63ff98</td>\n",
       "      <td>logo print T-shirt</td>\n",
       "      <td>5d0cc7b68a66a100014acdb0</td>\n",
       "      <td>No SKU Code</td>\n",
       "      <td>https://www.farfetch.com/uk/shopping/men/eleve...</td>\n",
       "      <td>100% Cotton.</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>next</td>\n",
       "      <td>No sub-brand name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>450</td>\n",
       "      <td>109773</td>\n",
       "      <td>412</td>\n",
       "      <td>1</td>\n",
       "      <td>2213</td>\n",
       "      <td>58</td>\n",
       "      <td>78689</td>\n",
       "      <td>208173</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25154</td>\n",
       "      <td>317946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.239519e+02</td>\n",
       "      <td>2.643545e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.580080e+03</td>\n",
       "      <td>3.272166e+03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.600000e+01</td>\n",
       "      <td>4.500000e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.930000e+02</td>\n",
       "      <td>2.400000e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.026484e+06</td>\n",
       "      <td>1.466406e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             _id  ...     sub_brand_name\n",
       "count                     317946  ...             317946\n",
       "unique                    317946  ...                  1\n",
       "top     5d49a619a3ecbbd90d63ff98  ...  No sub-brand name\n",
       "freq                           1  ...             317946\n",
       "mean                         NaN  ...                NaN\n",
       "std                          NaN  ...                NaN\n",
       "min                          NaN  ...                NaN\n",
       "25%                          NaN  ...                NaN\n",
       "50%                          NaN  ...                NaN\n",
       "75%                          NaN  ...                NaN\n",
       "max                          NaN  ...                NaN\n",
       "\n",
       "[11 rows x 13 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uf729VyfD5PP"
   },
   "source": [
    "* 'sub_brand_name' has 1 unique value, so dropping that column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2fzDiB3tscbZ"
   },
   "outputs": [],
   "source": [
    "data.drop('sub_brand_name', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4F0DjVPW3Rod"
   },
   "source": [
    "* 'website_id' has 4 uniques IDs, so we can extract website name from associated URLs for it and create a column for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CuQ1ZHBi8g8N"
   },
   "outputs": [],
   "source": [
    "data['website_name'] = data['url'].apply(lambda link : urlparse(link).netloc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0nrBRewb3TNT"
   },
   "source": [
    "#### Checking for rows with offer price greater than regular price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "FarfX_JOssiX",
    "outputId": "d9c02786-dd1e-4881-937e-070927fa17c0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total records with offer price greater than regular price:  14\n"
     ]
    }
   ],
   "source": [
    "print('Total records with offer price greater than regular price: ',data.loc[data['offer_price.value'] > data['regular_price.value']].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "colab_type": "code",
    "id": "J6sk9clouPXy",
    "outputId": "924e39f9-47be-472b-f5fc-a223d7310891"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>_id</th>\n",
       "      <th>name</th>\n",
       "      <th>website_id</th>\n",
       "      <th>sku</th>\n",
       "      <th>url</th>\n",
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       "      <th>offer_price.value</th>\n",
       "      <th>regular_price.value</th>\n",
       "      <th>brand_name</th>\n",
       "      <th>website_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78687</th>\n",
       "      <td>5d09dc3365a89b368ffe6920</td>\n",
       "      <td>Black Lace Bardot Bodysuit</td>\n",
       "      <td>5cff5e30e40f4900046735f8</td>\n",
       "      <td>cmd4463</td>\n",
       "      <td>www.prettylittlething.com/black-lace-bardot-bo...</td>\n",
       "      <td>This bodysuit is perfect for any weekend occas...</td>\n",
       "      <td>https://cdn-img.prettylittlething.com/e/3/c/b/...</td>\n",
       "      <td>https://cdn-img.prettylittlething.com/e/3/c/b/...</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>18.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>GLOBALLE</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88687</th>\n",
       "      <td>5d09e34a65a89b368ffe9031</td>\n",
       "      <td>Black Lace Gold Button Blazer Dress</td>\n",
       "      <td>5cff5e30e40f4900046735f8</td>\n",
       "      <td>clw9088</td>\n",
       "      <td>www.prettylittlething.com/black-lace-gold-butt...</td>\n",
       "      <td>Switch your simple LBD for the lace blazer dre...</td>\n",
       "      <td>https://cdn-img.prettylittlething.com/c/a/7/9/...</td>\n",
       "      <td>https://cdn-img.prettylittlething.com/c/a/7/9/...</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>40.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>UNIQUE21</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98685</th>\n",
       "      <td>5d09e86665a89b368ffeb741</td>\n",
       "      <td>Petite Brown Snake Print Floaty Shorts</td>\n",
       "      <td>5cff5e30e40f4900046735f8</td>\n",
       "      <td>clv7163</td>\n",
       "      <td>www.prettylittlething.com/petite-brown-snake-p...</td>\n",
       "      <td>We are loving print this season girl, and this...</td>\n",
       "      <td>https://cdn-img.prettylittlething.com/0/4/3/9/...</td>\n",
       "      <td>https://cdn-img.prettylittlething.com/0/4/3/9/...</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4FASHION</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108682</th>\n",
       "      <td>5d35a2cdd41118b4e396d9ff</td>\n",
       "      <td>Red Strappy Thick Lace Frill Hem Midi Dress</td>\n",
       "      <td>5cff5e30e40f4900046735f8</td>\n",
       "      <td>clz7400</td>\n",
       "      <td>www.prettylittlething.com/red-strappy-thick-la...</td>\n",
       "      <td>Get that jaw-dropping look with this killer mi...</td>\n",
       "      <td>https://cdn-img.prettylittlething.com/7/8/7/7/...</td>\n",
       "      <td>https://cdn-img.prettylittlething.com/7/8/7/7/...</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>45.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4fashion</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168048</th>\n",
       "      <td>5d49ce8aa3ecbbd90d649907</td>\n",
       "      <td>Dinky crossbody bag</td>\n",
       "      <td>5d0cc7b68a66a100014acdb0</td>\n",
       "      <td>11538699</td>\n",
       "      <td>www.farfetch.com/uk/shopping/women/coach-dinky...</td>\n",
       "      <td>Black leather Dinky crossbody bag from Coach.</td>\n",
       "      <td>https://cdn-images.farfetch-contents.com/11/53...</td>\n",
       "      <td>https://cdn-images.farfetch-contents.com/11/53...</td>\n",
       "      <td>https://cdn-images.farfetch-contents.com/11/53...</td>\n",
       "      <td>458.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>coach</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             _id  ... website_name\n",
       "78687   5d09dc3365a89b368ffe6920  ...             \n",
       "88687   5d09e34a65a89b368ffe9031  ...             \n",
       "98685   5d09e86665a89b368ffeb741  ...             \n",
       "108682  5d35a2cdd41118b4e396d9ff  ...             \n",
       "168048  5d49ce8aa3ecbbd90d649907  ...             \n",
       "\n",
       "[5 rows x 13 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data['offer_price.value'] > data['regular_price.value']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BxIR1co84SxW"
   },
   "source": [
    "* Removing records with offer price value greater than regular price value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "TS4KdeusuU2v",
    "outputId": "984e048f-a5f9-4d9c-e078-0c22de0c0bd4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total records with offer price greater than regular price:  0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(317932, 13)"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(data.loc[data['offer_price.value'] > data['regular_price.value']].index, axis=0, inplace=True)\n",
    "data = data.reset_index()\n",
    "data.drop('index', axis=1, inplace=True)\n",
    "print('Total records with offer price greater than regular price: ',data.loc[data['offer_price.value'] > data['regular_price.value']].shape[0])\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "r8MiR7nd4fPY"
   },
   "source": [
    "#### Functions for BoxPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Peq3T3AOxtri"
   },
   "outputs": [],
   "source": [
    "def boxPlot_single(numColumn):\n",
    "  plt.rcParams['figure.figsize'] = (10.0, 5.0)\n",
    "  plt.title('Box plot for '+numColumn)\n",
    "  sns.set(style=\"whitegrid\")\n",
    "  ax = sns.boxplot(data[numColumn])\n",
    "\n",
    "def boxPlot_multi(numColumn, catColumn):\n",
    "  plt.rcParams['figure.figsize'] = (20.0, 10.0)\n",
    "  plt.title('Box plot for ' +numColumn+ ' per '+catColumn)\n",
    "  sns.set(style=\"whitegrid\")\n",
    "  ax = sns.boxplot(x=catColumn, y=numColumn, data=data)\n",
    "\n",
    "  for i,artist in enumerate(ax.artists):\n",
    "      col = artist.get_facecolor()\n",
    "      artist.set_edgecolor(col)\n",
    "      artist.set_facecolor('None')\n",
    "\n",
    "      for j in range(i*6,i*6+6):\n",
    "          line = ax.lines[j]\n",
    "          line.set_color(col)\n",
    "          line.set_mfc(col)\n",
    "          line.set_mec(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LKW_KqGr5JXP"
   },
   "source": [
    "#### Checking and for outliers in price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 351
    },
    "colab_type": "code",
    "id": "INk5Cw0F-WS5",
    "outputId": "2fd310fb-3ef3-40c7-fba0-3907e5132748"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kOfhzt912G1sGYMsttxxr32effTjkkENIwqGHHsq22277hD622267aZeZap2XvvSlHHro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\nYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxPlot_single('offer_price.value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mppyKPTUAJDS"
   },
   "outputs": [],
   "source": [
    "def removeOutliers(price, data):\n",
    "  print('Total records with offer price greater than ',price,': ',data.loc[data['offer_price.value'] > price].shape[0])\n",
    "  print('Removing records...')\n",
    "  data.drop(data.loc[data['offer_price.value'] > price].index, axis=0, inplace=True)\n",
    "  data = data.reset_index()\n",
    "  data.drop('index', axis=1, inplace=True)\n",
    "  print('Total records with offer price greater than ',price,': ',data.loc[data['offer_price.value'] > price].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "RADS9JkRAyR9",
    "outputId": "40fa2026-25e0-4041-d760-3264900e61b6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total records with offer price greater than  5000 :  706\n",
      "Removing records...\n",
      "Total records with offer price greater than  5000 :  0\n"
     ]
    }
   ],
   "source": [
    "removeOutliers(5000, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 355
    },
    "colab_type": "code",
    "id": "cDQJmpmOBEH5",
    "outputId": "2102ea4c-ac23-41dd-8d8e-56a5372d7859"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EDwAAMB7BAwAAjEfwAAAA4xE8AADAeAQPAAAwHsEDAACMR/AAAADjETwAAMB4BA8AADAe\nwQMAAIxH8AAAAOMRPAAAwHgEDwAAMB7BAwAAjEfwAAAA4xE8AADAeAQPAAAwHsEDAACMR/AAAADj\nETwAAMB4BA8AADAewQMAAIxH8AAAAOMRPAAAwHgEDwAAMB7BAwAAjEfwAAAA4xE8AADAeAQPAAAw\nnsuDJy4uTmlpaa6exi2Ji4tTXFycq6cBAABKyOXBs2PHjrsueHbs2KEdO3a4ehoAAKCEXB48AAAA\ntxvBAwAAjEfwAAAA4xE8AADAeAQPAAAwHsEDAACMR/AAAADjETwAAMB4BA8AADAewQMAAIxH8AAA\nAOMRPAAAwHgEDwAAMB7BAwAAjEfwAAAA4xE8AADAeAQPAAAwHsEDAACMR/AAAADjETwAAMB4BA8A\nADAewQMAAIxH8AAAAOMRPAAAwHgEDwAAMB7BAwAAjEfwAAAA4xE8AADAeAQPAAAwHsEDAACMR/AA\nAADjETwAAMB4BA8AADAewQMAAIxH8AAAAOMRPAAAwHgEDwAAMB7BAwAAjEfwAAAA4xE8AADAeAQP\nAAAwHsEDAACMR/AAAADjETwAAMB4BA8AADAewQMAAIxH8AAAAOMRPAAAwHgEDwAAMJ7N1RO4Wx0+\nfFhdu3Z19TQA4I7w8PBQbm5ugeVubm6yLOu2vW5x4/v6+io1NbXEY3l6eqpPnz5asWKFPD09Va1a\nNaWkpDjXZWdnO59buXJlpaen59vey8tL//d//6f58+crMzPTuTwwMFCpqanKy8vLN4YkRUZGatOm\nTYW+Hzc3Nz377LNauHChPDw8lJaWJjc3N9lsNmVnZxf73v38/GS325WYmJhvWWpqar5tRo4cqW3b\ntunUqVPKysqSJFWtWlWXLl0qMKbNZlNOTk6BOd97770aPHiwXnnlFWVmZsput8vLy0uXL192PsfX\n11dXrlyRj4+PkpKSFBAQoF9//VU5OTn5XqN///564oknCn1Pt5ubVcTezMzM1OHDh9WoUSN5eXnd\ntglMmTJFx44dU45HVeeySve2K9VYV05+oQZ1auq1114rq+kVasqUKTp8+PBtfQ0AQPlzLQrK63g3\nKqsg9fb2VlpaWhnMSNq4cWOZjHOjm3ULl7RKgdgBgD+mso6T2xk7ksrs7FtZxY4krVmzpszGuhUu\nv6R14cKF306zeWbI3Vbhd42Vl5OhX375RVOmTCmj2QEAgLL0/vvvu+SyFmd4AACA8Vx+hsfHx+e3\nG5s8ft/ZHUlyt1VQnTtwDw83KwMAcHfhDA8AALhj+vfv75LXJXhKoVGjRq6eAgDABWy2sr0wUtbj\n3cjNza1MxvH29i6TcSS57NvSCR4AwE15eHgUurysvqAWpbjxfX19b2ksT09P9e3b1/nnGjVq5Ft3\nvcqVKxfY3svLSxMmTCjwLc+BgYHy8vIqMIb028/hud7178fNzU3jx49XxYoVnUHh5ubmHKe49+7n\n56fAwMACy27cZsSIEapbt67sdrtzWdWqVVWYa/F145zvvfdePffcc873bbfbVaVKlXzP8fX1VYUK\nFRQQECA3NzcFBgYWGnOuOrsjlYN7eO5WjRo1uu33CrnSd999p5CQEFdPA9fhmJRPHJfy52bHpGfP\nnr9r/JYtW97S84cNG1bs+latWv2e6dxU586db3mbwua8evXq3zUPV39WOMMDAACMR/AAAADjETwA\nAMB4BA8AADAewQMAAIxH8AAAAOMRPAAAwHgEDwAAMB7BAwAAjEfwAAAA4xE8AADAeAQPAAAwHsED\nAACMR/AAAADjETwAAMB4BA8AADAewQMAAIxH8AAAAOMRPAAAwHgEDwAAMB7BAwAAjEfwAAAA4xE8\nAADAeAQPAAAwHsEDAACMR/AAAADjETwAAMB4BA8AADAewQMAAIxH8AAAAOMRPAAAwHgEDwAAMB7B\nAwAAjEfwAAAA4xE8AADAeAQPAAAwHsEDAACMR/AAAADjETwAAMB4BA8AADAewQMAAIxH8AAAAOMR\nPAAAwHgEDwAAMB7BAwAAjEfwAAAA4xE8AADAeAQPAAAwHsEDAACMZ3P1BDp06KDExERduOrqmZRc\nhw4dXD0FAABwC1x+hic8PFze3t6unsYtCQ8PV3h4uKunAQAASsjlwQMAAHC7ETwAAMB4BA8AADAe\nwQMAAIxH8AAAAOMRPAAAwHgEDwAAMB7BAwAAjEfwAAAA4xE8AADAeAQPAAAwHsEDAACMR/AAAADj\nETwAAMB4BA8AADAewQMAAIxH8AAAAOMRPAAAwHgEDwAAMB7BAwAAjEfwAAAA4xE8AADAeAQPAAAw\nHsEDAACMR/AAAADjETwAAMB4BA8AADAewQMAAIxH8AAAAOMRPAAAwHgEDwAAMB7BAwAAjEfwAAAA\n4xE8AADAeAQPAAAwHsEDAACMR/AAAADjETwAAMB4BA8AADAewQMAAIxH8AAAAOMRPAAAwHgEDwAA\nMB7BAwAAjEfwAAAA4xE8AADAeAQPAAAwHsEDAACMR/AAAADjETwAAMB4BA8AADCezdUTuCY344Lz\nz1dOfvE7xqhZRjMCAACmKBfBExAQoCpVquj8+fOSpOrVq5dypJqqU6dO2U0MAAAYoVwET0REhEJC\nQlw9DQAAYCju4QEAAMYjeAAAgPEIHgAAYDyCBwAAGI/gAQAAxiN4AACA8QgeAABgPIIHAAAYj+AB\nAADGI3gAAIDxCB4AAGA8ggcAABiP4AEAAMYjeAAAgPEIHgAAYDyCBwAAGI/gAQAAxiN4AACA8Qge\nAABgPIIHAAAYj+ABAADGI3gAAIDxCB4AAGA8ggcAABjPVtQKy7IkSVlZWXdkIpmZmXfkdVByHJPy\nh2NSPnFcyh+OSfl0O4/LtV651i83crOKWHP58mUdPXr0tk0MAACgrNWrV09VqlQpsLzI4MnLy1N6\nero8PT3l5uZ22ycIAABQWpZlKTs7W5UrV5a7e8E7dooMHgAAAFNw0zIAADAewQMAAIxH8AAAAOMR\nPAAAwHgEDwAAMB7BAwAAjEfwAAAA47k0eE6cOKFevXqpU6dO6tWrl/7zn/+4cjrGmj17tsLDw1W/\nfv18Pz27uP1f2nUomfPnz2vIkCHq1KmTunbtqlGjRik1NVWSdPDgQUVFRalTp04aOHCgzp0759yu\ntOtQciNGjFBUVJS6d++up59+WkeOHJHE56U8WLRoUb5/x/isuFZ4eLg6d+6sbt26qVu3btq9e7ek\ncnxcLBeKjo621q1bZ1mWZa1bt86Kjo525XSMtX//fsvhcFiPPvqo9e9//9u5vLj9X9p1KJnz589b\n33zzjfPxrFmzrClTpli5ublW+/btrf3791uWZVmLFy+2Jk+ebFmWVep1uDWXLl1y/nnHjh1W9+7d\nLcvi8+Jqhw8ftgYNGuT8d4zPiuvd+DXFskq/7+/EcXFZ8KSkpFghISFWTk6OZVmWlZOTY4WEhFjn\nzp1z1ZSMd/1fzuL2f2nXofS2bdtm9evXzzp06JD12GOPOZefO3fOeuihhyzLskq9DqX32WefWT16\n9ODz4mKZmZnWU089ZZ06dcr57xifFdcrLHjK83Ep8rel326JiYmqWbOmPDw8JEkeHh7y9/dXYmKi\nfH19XTWtP4zi9r9lWaVax3Ernby8PH388ccKDw9XYmKigoKCnOt8fX2Vl5enCxculHqdj4/PHX0/\nJnj++ee1Z88eWZalZcuW8XlxsTfffFNRUVGqXbu2cxmflfJh4sSJsixLISEhGj9+fLk+Lty0DLjY\njBkzVKlSJT3zzDOungr+v1dffVW7du3SuHHjNGfOHFdP5w8tISFBhw8f1tNPP+3qqeAGH330kTZs\n2KA1a9bIsixNnz7d1VMqlsuCJzAwUElJScrNzZUk5ebmKjk5WYGBga6a0h9Kcfu/tOtw62bPnq2T\nJ09q/vz5cnd3V2BgoBwOh3N9amqq3N3d5ePjU+p1KL3u3bsrPj5eAQEBfF5cZP/+/Tp+/LjatWun\n8PBwnT17VoMGDdLJkyf5rLjYtb/HdrtdTz/9tL7//vty/W+Yy4LHz89PDRo00KZNmyRJmzZtUoMG\nDTjNe4cUt/9Luw635o033tDhw4e1ePFi2e12SVKjRo2UkZGhAwcOSJJWrVqlzp07/651KLn09HQl\nJiY6H8fFxalatWp8Xlxo6NCh+vrrrxUXF6e4uDgFBARo+fLlGjx4MJ8VF7py5YouX74sSbIsS1u2\nbFGDBg3K9b9hbpZlWWU64i04fvy4Jk+erEuXLqlq1aqaPXu26tSp46rpGOuVV17R9u3blZKSourV\nq8vHx0ebN28udv+Xdh1K5tixY4qMjNR9992nChUqSJJq166txYsX6/vvv9fLL7+szMxM1apVS3Pn\nzlWNGjUkqdTrUDIpKSkaMWKErl69Knd3d1WrVk3PPfecGjZsyOelnAgPD9fSpUtVr149PisudOrU\nKY0ePVq5ubnKy8tT3bp19cILL8jf37/cHheXBg8AAMCdwE3LAADAeAQPAAAwHsEDAACMR/AAAADj\nETwAAMB4BA9giIyMDMXExCgkJERjxoyRJM2bN09NmjRRixYtXDo3h8Oh4OBg5w/gc7WFCxdq4sSJ\nrp4GgDvIZb9LC0DZ2rZtm1JSUhQfHy+bzSaHw6H33ntPO3fulJ+fn0vnFhQUpISEBJfOAcAfG2d4\nAEM4HA7dd999stlszsc+Pj6lih3LspSXl1cm88rJySmTcQDg9yB4gLvM8ePHFR0drdDQUD322GP6\n4osvtGDBAi1ZskRbt25VcHCwVq1apYEDByo5OVnBwcGaPHmyJOngwYPq3bu3QkNDFRUVpfj4eOe4\n0dHRmjdvnnr37q3GjRvr1KlTRc4hOjpar7/+up588kk9/PDDGj58uC5cuCBJOn36tOrXr69PP/1U\nbdu2Vb9+/ZzLrsXPhQsXNGXKFLVs2VJhYWEaMWKEc+ydO3eqW7duCg0NVe/evfXTTz8VOofBgwdr\n5cqV+ZZFRUVp+/btkn77CeNt2rTRww8/rMcff9z5I+tvFB8fr9atW+dbFh4err1790r67bfZv/32\n22rfvr2aNGmisWPHOt8rgLsHwQPcRbKzsxUTE6MWLVpo7969euGFFzRx4kRFRkZq2LBhioiIUEJC\ngnr37q133nlH/v7+SkhI0KxZs5SUlKRhw4Zp+PDh+vbbb/Xcc89pzJgxSk1NdY6/fv16zZgxQ99/\n/72CgoKKncu6des0c+ZMff3117LZbHrllVfyrd+/f7+2bNmi5cuXF9h20qRJunr1qjZv3qy9e/eq\nf//+kqR//etfmjp1qqZPn674+Hj16tVLI0aMUFZWVoExIiMjnb+jSpJ+/vlnORwOtW3bVpL0t7/9\nTevWrdO3336ryMhIjR07VpmZmSXd1U4ffvihYmNjtXLlSu3evVvVqlUr978VGkBBBA9wFzl06JCu\nXLmioUOHym63q1mzZnr00Ue1efPmm267fv16tW7dWm3atJG7u7tatGihRo0a6csvv3Q+p0ePHnrg\ngQdks9nk6elZ7HjdunVTvXr1VKlSJY0dO1bbtm3Ld1Py6NGjValSJefvCrsmOTlZX331laZNm6Zq\n1arJ09NTjzzyiCTpn//8p3r16qXGjRvLw8NDPXr0kKenpw4ePFjg9du3b6+ffvpJZ86ckSRt3LhR\nHTp0cP4i1m7duql69eqy2WwaOHCgsrKydOLEiZvupxutWrVK48aNU0BAgOx2u0aNGqXPP/+cS3XA\nXYabloG7SHJysgICAuTu/r//qwQFBSkpKUk1a9YsdluHw6Ft27Zp586dzmU5OTlq0qSJ83FgYGCJ\n53L9c4OCgpSdna3z5887lwUEBBS63dmzZ1WtWjVVq1at0DmuW7cu36Wq7OxsJScnF3iut7e32rRp\no82bN2vo0KHatGlTvrNMy5cv1+rVq5WcnCw3NzelpaXlm19JORwOjRw5Mt8+d3d317lz5266zwGU\nHwQPcBfx9/fX2bNnlZeX5/wCnJiYqPvuu++m2wYGBqpbt24FLj1dz83NrcRzSUxMzPdnT09PVa9e\n3bm8qLECAgJ08eJF528Pv3GOMTExGj58eInmEBkZqUWLFiksLEyZmZnOeDtw4ICWLVum999/Xw88\n8IDc3d0VFhamwn5XcsWKFZWRkeF8nJubm+8yX0BAgGbOnKmQkJASzQlA+cQlLeAu8uCDD6pChQpa\ntmyZsrOzFR8fr7i4OHXp0ly86GUAAAH2SURBVOWm20ZFRWnnzp3avXu3cnNzlZmZqfj4eJ09e7ZU\nc9mwYYN+/vlnXb16VW+++aY6deokDw+Pm27n7++v1q1ba9q0abp48aKys7O1f/9+SVLPnj21atUq\nHTp0SJZl6cqVK9q1a5fS0tIKHatNmzZyOBxasGCBunTp4ozA9PR0eXh4yNfXVzk5OVq0aFGRY9x/\n//3KzMzUrl27lJ2drX/84x/57hnq06eP5s+f77x0lpqaqtjY2FvaVwBcj+AB7iJ2u11Lly7VV199\npaZNm2ratGmaM2eO6tate9NtAwMDtWTJEr311ltq1qyZ2rRpo+XLl5f628+7deumyZMnq0WLFsrK\nytLzzz9f4m3nzJkjm82miIgINW/eXB988IGk3240njFjhqZPn66wsDB17NhRa9eudW43ePBgLV26\n1PnYbrerQ4cO2rt3ryIjI53LW7ZsqVatWqlTp04KDw+Xl5dXkZfrqlSpopdfflkvvPCCWrdurYoV\nK+a7HNe3b1+Fh4dr4MCBCg4O1lNPPaUffvihxO8VQPngZhV2jhcAihEdHa2oqCj17NnT1VMBgBLh\nDA8AADAeNy0DKFRwcHChy9955507PBMA+P24pAUAAIzHJS0AAGA8ggcAABiP4AEAAMYjeAAAgPEI\nHgAAYDyCBwAAGO//AaNRnQbCF1YUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxPlot_single('offer_price.value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "a5EAYj5wH2FE",
    "outputId": "e98a5965-703d-4063-a5b5-e484399eee08"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total records with offer price greater than  200 :  76756\n",
      "Removing records...\n",
      "Total records with offer price greater than  200 :  0\n"
     ]
    }
   ],
   "source": [
    "removeOutliers(200, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 355
    },
    "colab_type": "code",
    "id": "HWLqGKA5H_lA",
    "outputId": "1417cee7-b3bd-4e33-d493-a69ea23a7bc5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+/XpNnz5dR44cUWBgYKljWbdunWbMmKEvv/xSNptNb7zxRqGfJyYmavPmzVq2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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxPlot_single('offer_price.value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "jDUifzjtPB9o",
    "outputId": "851de7cb-1159-4824-fce1-3e86b0249867"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Records left after removing outliers (offer price greater than 200 GBP):  240470\n",
      "Records dropped in precentage : 24.36\n"
     ]
    }
   ],
   "source": [
    "print('Records left after removing outliers (offer price greater than 200 GBP): ', data.shape[0])\n",
    "print('Records dropped in precentage :', round((1 - data.shape[0]/317932) * 100, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "z2lynn9Dhl80"
   },
   "source": [
    "* After removing most of the outliers, Still distribution is left skewed.\n",
    "* Data is dropped by 24.36%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 372
    },
    "colab_type": "code",
    "id": "rzCQ9TGMPKBg",
    "outputId": "d60ad807-f079-4e09-e8cf-41316f8eb310"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe919bd56a0>"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y1OWJzZ6yzDQrBVNSNdlfRERkHCiUxaBYHb6EviHMI6ddhMPanFxERGQsKZTF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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Offer Price Distribution')\n",
    "sns.distplot(data['offer_price.value'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NF9DTwV1h-cK"
   },
   "source": [
    "#### BoxPlot for offer price by each website"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 627
    },
    "colab_type": "code",
    "id": "FONLdAF7_9fy",
    "outputId": "c3a590ab-2cd1-4c8b-9b12-a08f31156497"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxPlot_multi('offer_price.value', 'website_name')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZO2nl1YJif9l"
   },
   "source": [
    "#### Dropping records with invalid URLs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QTAXJSXIY1eg"
   },
   "outputs": [],
   "source": [
    "def validate_url(url):\n",
    "  try:\n",
    "    result = urlparse(url)\n",
    "    return all([result.scheme, result.netloc])\n",
    "  except ValueError:\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fnM0qGgIf_6n"
   },
   "outputs": [],
   "source": [
    "data['valid_url'] = data['url'].apply(lambda link : validate_url(link))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "PY2Hf0N5gXL3",
    "outputId": "cf615215-dd72-475d-f3a0-f495c5530c6f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total records with invalid URLs :  16\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Int64Index([     0,   9809,  19609,  29455,  39327,  49116,  58949,  68781,\n",
       "            138077, 148065, 158049, 178027, 217981, 257936, 267936, 307932],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Total records with invalid URLs : ',data[data['valid_url'] == False].shape[0])\n",
    "data[data['valid_url'] == False].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "-P8QixRvhggJ",
    "outputId": "b965b011-93ab-457e-bbb7-7c04e9ea6c39"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total records with invalid URLs :  0\n"
     ]
    }
   ],
   "source": [
    "data.drop(data[data['valid_url'] == False].index, axis=0, inplace=True)\n",
    "data = data.reset_index()\n",
    "data.drop('index', axis=1, inplace=True)\n",
    "print('Total records with invalid URLs : ',data[data['valid_url'] == False].shape[0])\n",
    "data.drop('valid_url', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "X9PJ-CKvnw-J"
   },
   "source": [
    "### Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "A61xNjhTjsC_"
   },
   "source": [
    "#### Bar graphs for top categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Xe_dxkqjHj5o"
   },
   "outputs": [],
   "source": [
    "def barGraph_count(catColumn, top=5):\n",
    "  temp = data[catColumn].value_counts()\n",
    "  temp = temp.reset_index()\n",
    "  temp.columns = [catColumn, 'count']\n",
    "  temp = temp.head(top)\n",
    "  temp = pd.DataFrame(temp)\n",
    "  ax = sns.barplot(x=catColumn, y='count', data=temp)\n",
    "  del temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "c1OrIuipj3Jl"
   },
   "source": [
    "* Top 10 product name\n",
    "* 'logo print T-shirt' and 'logo T-shirt' topping chart with around 375 and 195 products respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "colab_type": "code",
    "id": "J14p4Wpyo_dD",
    "outputId": "a2252945-e6c8-4481-f7e9-6f6c7f3cfeae"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DH6S5uTk//OEP88wzz2TatGk5/vjju68sqLW+fN5WKpUsWLAgDz74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\nDKfvBAyhSMcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barGraph_count('name', top=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UmeTFxadkBjx"
   },
   "source": [
    "* Top 10 brand names\n",
    "* 'next' on top with around 22500 products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "colab_type": "code",
    "id": "WbfhxKN1qdRD",
    "outputId": "6945c38e-9a31-44d3-ec1e-4f32653ecda4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3X5u2Jds2Jf67FxHl+/n7V0qlPH577dMsXz40KaU8fpX77FuybVPaY5cqykq2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\nBagAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barGraph_count('brand_name', top=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vTKRj9I0kLGc"
   },
   "source": [
    "* Top 5 websites\n",
    "* www.asos.com with around 95000 products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "colab_type": "code",
    "id": "KG2g5VS2rI_3",
    "outputId": "02e6032b-0a03-44b0-b270-e197d6451caf"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AaZNnTn0aZWVl0atXryguLo6CgoLc0wEAAADY5aWUYsOGDVFWtuWZ9W0yTnXv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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barGraph_count('website_name')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EX2GOaU8kTyu"
   },
   "source": [
    "#### Bar graphs for top discounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qsSsoeobHmmc"
   },
   "outputs": [],
   "source": [
    "def barGraph_discount(catColumn, top = 10, percent=50):\n",
    "  temp = data.groupby(catColumn)['offer_price.value', 'regular_price.value'].mean()\n",
    "  temp = temp.reset_index()\n",
    "  temp['discount_percent'] = round((1 - temp['offer_price.value']/temp['regular_price.value']) * 100, 2)\n",
    "  temp.sort_values(by=['discount_percent'], inplace=True, ascending=False)\n",
    "  temp = temp.loc[:,[catColumn, 'discount_percent']].head(top)\n",
    "  ax = sns.barplot(x=catColumn, y='discount_percent', data=temp,  palette=\"Blues_d\")\n",
    "  del temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zCTpBgiUktFt"
   },
   "source": [
    "* Top discounts by brand name\n",
    "* Top 10: projectt, seeker, kowtow, parka london and other are with discounts around 77-84% "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "colab_type": "code",
    "id": "xZGOv1vKH3c4",
    "outputId": "fd9c07ab-954a-4fda-cc9f-5a1884afd254"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1q2lvbx/oMXYJ48aNy/r16/v9vP/0T/+U1atX9/t5eXVtbW254YYb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\nGGOMMcYY08j/AywsAgoKqOloAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barGraph_discount('brand_name', top=10, percent=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XVZUhprQk-jY"
   },
   "source": [
    "* Top discounts by website name\n",
    "* www.next.co.uk with no discounts at all\n",
    "* www.prettylittlethings with high discounts for around 40%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "colab_type": "code",
    "id": "xizz1TAWKNVA",
    "outputId": "a32341c6-08b3-430f-ab62-38ef30622d3e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CBQs6fqHr06dPvvnNb2bw4MEvetwFCxZk0aJFGTBgQFatWpXGxsZM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\nIiIiIov8HxLDofQ6IB2JAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barGraph_discount('website_name', top=6, percent=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fBRBkBronaIy"
   },
   "source": [
    "### Text pre-processing on 'description_text'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "n2kmMgSFoG1R"
   },
   "source": [
    "* Function to remove punctuation, stopwords and lemmatization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Nlg7MUcEN43N"
   },
   "outputs": [],
   "source": [
    "def remove_punctuation(text):\n",
    "  return [word for word in text if word.isalpha()]\n",
    "\n",
    "def remove_punctuation_from_word(text):\n",
    "  token = []\n",
    "  for word in text:\n",
    "    if word[-1].isalpha():\n",
    "      token.append(word)\n",
    "    else:\n",
    "      token.append(word[:-1]) \n",
    "  return token\n",
    "\n",
    "stop_words = stopwords.words('english')\n",
    "def remove_stopword(text):\n",
    "  return [w for w in text if not w in stop_words]\n",
    "    \n",
    "def lemmatizing(text):\n",
    "  lemmatizer = WordNetLemmatizer()\n",
    "  return [lemmatizer.lemmatize(word) for word in text]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cbkx1RTXoSie"
   },
   "source": [
    "* Here we perform processing on 'description_text'\n",
    "* Convert to lowercase -> tokenization -> removing punctuation -> removing stop words -> Lemmatization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "Y4sWRBqSzSZw",
    "outputId": "ca50ef83-7cab-4cc3-920d-4346555988e3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting to lower case...\n",
      "Done\n",
      "Tokenizing...\n",
      "Done\n",
      "Removing punctuation...\n",
      "Done\n",
      "Removing Stop words...\n",
      "Done\n",
      "Lemmatizing...\n",
      "Text processing Done\n"
     ]
    }
   ],
   "source": [
    "data['processed_text'] = data['description_text']\n",
    "\n",
    "print('Converting to lower case...')\n",
    "data['processed_text'] = [text.strip().lower() for text in data['processed_text']]\n",
    "print('Done')\n",
    "\n",
    "print('Tokenizing...')\n",
    "data['processed_text'] = [word_tokenize(text) for text in data['processed_text']]\n",
    "print('Done')\n",
    "\n",
    "print('Removing punctuation...')\n",
    "data['processed_text'] = [remove_punctuation(text) for text in data['processed_text']]\n",
    "data['processed_text'] = [remove_punctuation_from_word(text) for text in data['processed_text']]\n",
    "print('Done')\n",
    "\n",
    "print('Removing Stop words...')\n",
    "data['processed_text'] = [remove_stopword(text) for text in data['processed_text']]\n",
    "print('Done')\n",
    "\n",
    "print('Lemmatizing...')\n",
    "data['processed_text'] = [lemmatizing(text) for text in data['processed_text']]\n",
    "print('Text processing Done')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ChmR5ptdo3M-"
   },
   "source": [
    "* Quick look at processed text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 359
    },
    "colab_type": "code",
    "id": "hmcmRnNoNi3h",
    "outputId": "4a7706d3-8192-4820-d3d7-e6004259ea01"
   },
   "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_text</th>\n",
       "      <th>processed_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100% Polyester.</td>\n",
       "      <td>[polyester]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nike Swim Boys' Solid Lap 4 Volley Short is a ...</td>\n",
       "      <td>[nike, swim, boy, solid, lap, volley, short, d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Height 85cm</td>\n",
       "      <td>[height]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100% Polyester.</td>\n",
       "      <td>[polyester]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>In a leopard print this tie up heel.</td>\n",
       "      <td>[leopard, print, tie, heel]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70% Viscose, 26% Nylon, 4% Elastane.</td>\n",
       "      <td>[viscose, nylon, elastane]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Wish your loved one a Hoppy Birthday with this...</td>\n",
       "      <td>[wish, loved, one, hoppy, birthday, beer, hawk...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>With adjustable strap fastening.</td>\n",
       "      <td>[adjustable, strap, fastening]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18 carat gold-plated sterling silver. Comes in...</td>\n",
       "      <td>[carat, sterling, silver, come, gift, box]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>With blackout lining.</td>\n",
       "      <td>[blackout, lining]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    description_text                                     processed_text\n",
       "0                                    100% Polyester.                                        [polyester]\n",
       "1  Nike Swim Boys' Solid Lap 4 Volley Short is a ...  [nike, swim, boy, solid, lap, volley, short, d...\n",
       "2                                        Height 85cm                                           [height]\n",
       "3                                    100% Polyester.                                        [polyester]\n",
       "4               In a leopard print this tie up heel.                        [leopard, print, tie, heel]\n",
       "5               70% Viscose, 26% Nylon, 4% Elastane.                         [viscose, nylon, elastane]\n",
       "6  Wish your loved one a Hoppy Birthday with this...  [wish, loved, one, hoppy, birthday, beer, hawk...\n",
       "7                   With adjustable strap fastening.                     [adjustable, strap, fastening]\n",
       "8  18 carat gold-plated sterling silver. Comes in...         [carat, sterling, silver, come, gift, box]\n",
       "9                              With blackout lining.                                 [blackout, lining]"
      ]
     },
     "execution_count": 50,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[:,['description_text','processed_text']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1XHoDcS2o9Va"
   },
   "source": [
    "#### Word Cloud with processed text and Standard media URL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rNLRZDCFDVIr"
   },
   "outputs": [],
   "source": [
    "def img_with_wordcloud(row):\n",
    "  word_cloud = WordCloud(max_words=5, background_color='white').generate(' '.join(data['processed_text'][row]))\n",
    "  plt.figure(figsize=(5,5),facecolor = 'white', edgecolor='blue')\n",
    "  plt.imshow(word_cloud)\n",
    "  plt.axis('off')\n",
    "  plt.tight_layout(pad=0)\n",
    "  plt.show()\n",
    "  display(Image(url= data['standard_media_url'][row]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 687
    },
    "colab_type": "code",
    "id": "bqRzzDUvu90d",
    "outputId": "1d5efd8c-32cc-4ad9-d977-6a1e211ade98"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zEuKbaOkegTuiYtg7Lb5/vhf58eEREHY8fzBMSr3eb5+Tvf/Fe7XfQ6VuW1j5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\nnUAgEJKM/wem5Dqn4EO9OQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://xcdn.next.co.uk/COMMON/Items/Default/Default/Publications/G52/shotview-315x472/146/915-749s.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_with_wordcloud(4123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 687
    },
    "colab_type": "code",
    "id": "5n6TAJOzwEFO",
    "outputId": "44c1d6dd-27f0-4dc3-bdd4-57ea2ea09b49"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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X7dSqtbi353NQVCsRHCrDhpz3IJaK8MjgF40RLIUiAb47swwRsc4HDOMbFxVX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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://xcdn.next.co.uk/Common/Items/Default/Default/ItemImages/AltItemShot/315x472/316250.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_with_wordcloud(2575)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 687
    },
    "colab_type": "code",
    "id": "w9bhLix-LvWd",
    "outputId": "5ab58e88-a1b6-4014-a27b-5fe89b1d5428"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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X8bhYdst8zM9MN1/LiYlyW3lMuueZh/p448dFdyA9JNgt5Xx7x62YHBfrlrwJ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\nYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://xcdn.next.co.uk/Common/Items/Default/Default/ItemImages/AltItemShot/315x472/335026.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_with_wordcloud(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 687
    },
    "colab_type": "code",
    "id": "3-C7PFTHH1hW",
    "outputId": "a90c5e39-e4a1-445c-cbd0-fd5b5433bfc3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mUQh7YnIYJk1LpWx/rZCdf78hJUXUK/bIqACrfO8tgQi7ATEMNmG2hJm5Yh0I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\niLATCASCi/H/j3Z4yzgaJYMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://xcdn.next.co.uk/Common/Items/Default/Default/ItemImages/AltItemShot/315x472/L03697.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_with_wordcloud(2745)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 687
    },
    "colab_type": "code",
    "id": "K5-EnUtyHibJ",
    "outputId": "cf08faa6-cf9f-421d-b6e1-06968a64f482"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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abQEa9eXQmBTQmpQwMnqYGAOMZr3jBtqQ8sKc/p66KkrsLhoVnYRJcanYz1KS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\nwFBiJ4SQAEOJnRBCAsz/A1Fe2evZnP/cAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://xcdn.next.co.uk/Common/Items/Default/Default/ItemImages/AltItemShot/315x472/588320.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_with_wordcloud(564)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zCRrSzGvqG6H"
   },
   "source": [
    "#### Creating Tags for products with processed text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tT0psdLlpPBp"
   },
   "source": [
    "* As we see processed text are almost can be used as tags for image\n",
    "* So, creating tag columns with top 3 words from processed text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mYbIyKeYvRlQ"
   },
   "outputs": [],
   "source": [
    "def get_tag(text):\n",
    "  tag = {}\n",
    "  try:\n",
    "    tagDict = WordCloud(max_words=3).generate(' '.join(text)).words_\n",
    "    tag['tag_1'] = list(tagDict.keys())[0]\n",
    "    try:\n",
    "      tag['tag_2'] = list(tagDict.keys())[1]\n",
    "    except IndexError:\n",
    "      tag['tag_2'] = ''\n",
    "    try:\n",
    "      tag['tag_3'] = list(tagDict.keys())[2]\n",
    "    except IndexError:\n",
    "      tag['tag_3'] = ''\n",
    "    return tag\n",
    "  except ValueError:\n",
    "    return {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "SXISFHXM6x_V",
    "outputId": "3288da08-fc63-4e97-e602-02e34a8b2900"
   },
   "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>tag_1</th>\n",
       "      <th>tag_2</th>\n",
       "      <th>tag_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>polyester</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lap</td>\n",
       "      <td>short</td>\n",
       "      <td>swimming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>height</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>polyester</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>leopard</td>\n",
       "      <td>print</td>\n",
       "      <td>tie</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       tag_1  tag_2     tag_3\n",
       "0  polyester    NaN       NaN\n",
       "1        lap  short  swimming\n",
       "2     height    NaN       NaN\n",
       "3  polyester    NaN       NaN\n",
       "4    leopard  print       tie"
      ]
     },
     "execution_count": 58,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tags = [get_tag(x) for x in data['processed_text']]\n",
    "# tags = pd.DataFrame(tags)\n",
    "# tags.to_csv(path+'tags.csv',index=False)\n",
    "tags = pd.read_csv(path+'tags.csv', header=0)\n",
    "tags.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NEinvugiLm2N"
   },
   "source": [
    "#### Bar graph for tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2ehdjTzasANr"
   },
   "outputs": [],
   "source": [
    "def barGraph_tags(top = 5):\n",
    "  temp = pd.concat([tags['tag_1'],tags['tag_2'],tags['tag_3']]).value_counts()\n",
    "  temp = pd.DataFrame(temp)\n",
    "  temp = temp.reset_index()\n",
    "  temp = temp.head(10)\n",
    "  ax = sns.barplot(x='index', y=0, data=temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nuJPQAntL0yC"
   },
   "source": [
    "* 'Cotton' is most used word to describe, followed by 'look', 'design' and 'black'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "colab_type": "code",
    "id": "fOMEeYQxvCbb",
    "outputId": "60b91374-208a-4540-af4b-c72378a82ed7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barGraph_tags(top=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-25qzLGVMSWN"
   },
   "source": [
    "* Adding column tag_1, tag_2 and tag_3 to data\n",
    "* Dropping column 'processed_text'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Efz59LfEkwnz"
   },
   "outputs": [],
   "source": [
    "data.drop('processed_text', axis = 1, inplace = True)\n",
    "tags = tags.replace(np.nan, '')\n",
    "data = pd.concat([data, tags], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 462
    },
    "colab_type": "code",
    "id": "A5DT-FmhX4vT",
    "outputId": "cf2dc7bb-3385-49f3-eae1-f9d670000ff1"
   },
   "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>_id</th>\n",
       "      <th>name</th>\n",
       "      <th>website_id</th>\n",
       "      <th>sku</th>\n",
       "      <th>url</th>\n",
       "      <th>description_text</th>\n",
       "      <th>standard_media_url</th>\n",
       "      <th>thumbnail_media_url</th>\n",
       "      <th>large_media_url</th>\n",
       "      <th>offer_price.value</th>\n",
       "      <th>regular_price.value</th>\n",
       "      <th>brand_name</th>\n",
       "      <th>website_name</th>\n",
       "      <th>tag_1</th>\n",
       "      <th>tag_2</th>\n",
       "      <th>tag_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5d0b8aca0db7220b86cb4036</td>\n",
       "      <td>Figleaves Cheetah Satin Pyjama Set</td>\n",
       "      <td>5cff5e7fe40f4900046735fa</td>\n",
       "      <td>319571</td>\n",
       "      <td>https://www.next.co.uk/style/st324987#319571</td>\n",
       "      <td>100% Polyester.</td>\n",
       "      <td>https://xcdn.next.co.uk/Common/Items/Default/D...</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>figleaves</td>\n",
       "      <td>www.next.co.uk</td>\n",
       "      <td>polyester</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5d0b8aca0db7220b86cb4037</td>\n",
       "      <td>Nike Solid 4\" Swim Short</td>\n",
       "      <td>5cff5e7fe40f4900046735fa</td>\n",
       "      <td>335026</td>\n",
       "      <td>https://www.next.co.uk/style/st400645#335026</td>\n",
       "      <td>Nike Swim Boys' Solid Lap 4 Volley Short is a ...</td>\n",
       "      <td>https://xcdn.next.co.uk/Common/Items/Default/D...</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>nike</td>\n",
       "      <td>www.next.co.uk</td>\n",
       "      <td>lap</td>\n",
       "      <td>short</td>\n",
       "      <td>swimming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5d0b8aca0db7220b86cb4038</td>\n",
       "      <td>Collection Luxe Orchid</td>\n",
       "      <td>5cff5e7fe40f4900046735fa</td>\n",
       "      <td>552266</td>\n",
       "      <td>https://www.next.co.uk/style/st262195#552266</td>\n",
       "      <td>Height 85cm</td>\n",
       "      <td>https://xcdn.next.co.uk/Common/Items/Default/D...</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>next</td>\n",
       "      <td>www.next.co.uk</td>\n",
       "      <td>height</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5d0b8aca0db7220b86cb4039</td>\n",
       "      <td>River Island White Sleeveless Blazer</td>\n",
       "      <td>5cff5e7fe40f4900046735fa</td>\n",
       "      <td>680971</td>\n",
       "      <td>https://www.next.co.uk/style/st440132#680971</td>\n",
       "      <td>100% Polyester.</td>\n",
       "      <td>https://xcdn.next.co.uk/Common/Items/Default/D...</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>river island</td>\n",
       "      <td>www.next.co.uk</td>\n",
       "      <td>polyester</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5d0b8aca0db7220b86cb403a</td>\n",
       "      <td>Faith Animal Print Heel</td>\n",
       "      <td>5cff5e7fe40f4900046735fa</td>\n",
       "      <td>L07550</td>\n",
       "      <td>https://www.next.co.uk/style/esl07550#l07550</td>\n",
       "      <td>In a leopard print this tie up heel.</td>\n",
       "      <td>https://xcdn.next.co.uk/Common/Items/Default/D...</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>No media URL</td>\n",
       "      <td>52.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>faith</td>\n",
       "      <td>www.next.co.uk</td>\n",
       "      <td>leopard</td>\n",
       "      <td>print</td>\n",
       "      <td>tie</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        _id  ...     tag_3\n",
       "0  5d0b8aca0db7220b86cb4036  ...          \n",
       "1  5d0b8aca0db7220b86cb4037  ...  swimming\n",
       "2  5d0b8aca0db7220b86cb4038  ...          \n",
       "3  5d0b8aca0db7220b86cb4039  ...          \n",
       "4  5d0b8aca0db7220b86cb403a  ...       tie\n",
       "\n",
       "[5 rows x 16 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nuo-8meNMduq"
   },
   "source": [
    "#### Let's check if we can cluster data based on brand_name, website_name, and tags columns for products"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ue04k3PQAzNu"
   },
   "source": [
    "* Taking subset of data with those columns in cluster_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "h2LIzPIcyj-C",
    "outputId": "678706e7-ad4d-497c-ee78-b343e54fba03"
   },
   "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>brand_name</th>\n",
       "      <th>website_name</th>\n",
       "      <th>tag_1</th>\n",
       "      <th>tag_2</th>\n",
       "      <th>tag_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>figleaves</td>\n",
       "      <td>www.next.co.uk</td>\n",
       "      <td>polyester</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nike</td>\n",
       "      <td>www.next.co.uk</td>\n",
       "      <td>lap</td>\n",
       "      <td>short</td>\n",
       "      <td>swimming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>next</td>\n",
       "      <td>www.next.co.uk</td>\n",
       "      <td>height</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>river island</td>\n",
       "      <td>www.next.co.uk</td>\n",
       "      <td>polyester</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>faith</td>\n",
       "      <td>www.next.co.uk</td>\n",
       "      <td>leopard</td>\n",
       "      <td>print</td>\n",
       "      <td>tie</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     brand_name    website_name      tag_1  tag_2     tag_3\n",
       "0     figleaves  www.next.co.uk  polyester                 \n",
       "1          nike  www.next.co.uk        lap  short  swimming\n",
       "2          next  www.next.co.uk     height                 \n",
       "3  river island  www.next.co.uk  polyester                 \n",
       "4         faith  www.next.co.uk    leopard  print       tie"
      ]
     },
     "execution_count": 63,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_data = data.loc[:,['brand_name', 'website_name', 'tag_1', 'tag_2', 'tag_3']]\n",
    "cluster_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0-_F_cXqA7Bd"
   },
   "source": [
    "* Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "20RPSX_JjHX9",
    "outputId": "f7a9591e-c1d2-498c-fc7b-160a2dbf5030"
   },
   "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>brand_name</th>\n",
       "      <th>website_name</th>\n",
       "      <th>tag_1</th>\n",
       "      <th>tag_2</th>\n",
       "      <th>tag_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1399</td>\n",
       "      <td>4</td>\n",
       "      <td>4028</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2561</td>\n",
       "      <td>4</td>\n",
       "      <td>2903</td>\n",
       "      <td>5761</td>\n",
       "      <td>7308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2550</td>\n",
       "      <td>4</td>\n",
       "      <td>2407</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3011</td>\n",
       "      <td>4</td>\n",
       "      <td>4028</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1350</td>\n",
       "      <td>4</td>\n",
       "      <td>2973</td>\n",
       "      <td>4992</td>\n",
       "      <td>7529</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   brand_name  website_name  tag_1  tag_2  tag_3\n",
       "0        1399             4   4028      0      0\n",
       "1        2561             4   2903   5761   7308\n",
       "2        2550             4   2407      0      0\n",
       "3        3011             4   4028      0      0\n",
       "4        1350             4   2973   4992   7529"
      ]
     },
     "execution_count": 64,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = preprocessing.LabelEncoder()\n",
    "for c in cluster_data.columns:\n",
    "  cluster_data[c] = le.fit_transform(cluster_data[c])\n",
    "cluster_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bg5gGQbQBHE9"
   },
   "source": [
    "* Applying DBSCAN, as we don't know how many categories of product do we have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "H1CzR6A5GRew",
    "outputId": "5235e58b-8de3-40c2-ccdd-92488fb75346"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clusters:  61\n"
     ]
    }
   ],
   "source": [
    "db = DBSCAN(eps=1, min_samples=100).fit(cluster_data) \n",
    "core_samples_mask = np.zeros_like(db.labels_, dtype=bool) \n",
    "core_samples_mask[db.core_sample_indices_] = True\n",
    "labels = db.labels_ \n",
    "  \n",
    "# Number of clusters in labels, ignoring noise if present. \n",
    "n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0) \n",
    "print('Number of clusters: ',n_clusters_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vhHzK0jXBjxL"
   },
   "source": [
    "* Creating column cluster number for each product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "G71fr_Kxn8gp"
   },
   "outputs": [],
   "source": [
    "data['clusters'] = labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TY1C0UJIBsT7"
   },
   "source": [
    "#### And a check on random cluster number and standard media URL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bIWrJOw1o0ML"
   },
   "outputs": [],
   "source": [
    "def checkClusters(clusterNumber):\n",
    "  temp = data[data['clusters'] == clusterNumber].head()\n",
    "  for url in temp['standard_media_url'].values:\n",
    "    display(Image(url= url))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ufmPHvXgB0Es"
   },
   "source": [
    "* Cluster with adult footwears"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "4E7YPAXO1jYR",
    "outputId": "30e7bc35-2fa8-409a-800f-5c9190160c83"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://xcdn.next.co.uk/Common/Items/Default/Default/ItemImages/AltItemShot/315x472/313005.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://xcdn.next.co.uk/COMMON/Items/Default/Default/Publications/G72/shotview-315x472/2538/520-615s.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://xcdn.next.co.uk/Common/Items/Default/Default/ItemImages/AltItemShot/315x472/329662.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://xcdn.next.co.uk/Common/Items/Default/Default/ItemImages/AltItemShot/315x472/303072.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://xcdn.next.co.uk/Common/Items/Default/Default/ItemImages/AltItemShot/315x472/563793.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "checkClusters(24)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6KkK6E7HB8u7"
   },
   "source": [
    "* Cluster with women wears with some specific type of dress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "oZm3U1oBCCw5",
    "outputId": "7013e641-6d7b-4fc9-8311-8058c9be7554"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://xcdn.next.co.uk/Common/Items/Default/Default/ItemImages/AltItemShot/315x472/506484.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://xcdn.next.co.uk/Common/Items/Default/Default/ItemImages/AltItemShot/315x472/506484.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://xcdn.next.co.uk/Common/Items/Default/Default/ItemImages/AltItemShot/315x472/320936.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://xcdn.next.co.uk/Common/Items/Default/Default/ItemImages/AltItemShot/315x472/557048.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://xcdn.next.co.uk/Common/Items/Default/Default/ItemImages/AltItemShot/315x472/955086.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "checkClusters(25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nmpkfP-cCgfG"
   },
   "source": [
    "* Cluster with bedsheet and pillow cover "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "mMxElM902Ez-",
    "outputId": "07f4c411-f8d9-4f04-d685-01d4f26d525f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://xcdn.next.co.uk/COMMON/Items/Default/Default/Publications/G52/shotview-315x472/10170/600-993s.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://xcdn.next.co.uk/COMMON/Items/Default/Default/Publications/G82/shotview-315x472/10025/645-366s.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://xcdn.next.co.uk/COMMON/Items/Default/Default/Publications/G52/shotview-315x472/8184/150-263s.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://xcdn.next.co.uk/COMMON/Items/Default/Default/Publications/G82/shotview-315x472/10030/702-934s.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://xcdn.next.co.uk/COMMON/Items/Default/Default/Publications/G52/shotview-315x472/10161/852-324s.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "checkClusters(26)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "N4AmCNe7Cm6Q"
   },
   "source": [
    "* Cluster with kids top wears"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "Ul4F950j2L1m",
    "outputId": "1986d716-106c-4b87-d712-f0ef48981486"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://xcdn.next.co.uk/Common/Items/Default/Default/ItemImages/AltItemShot/315x472/613114.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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    "Cluster with kids footwears"
   ]
  },
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    "id": "cZFYHpdt2YG8",
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   "source": [
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  },
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    "* Cluster with home decors or specific to flowers"
   ]
  },
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   "execution_count": 0,
   "metadata": {
    "colab": {
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    "id": "jUI5a2cV2xKP",
    "outputId": "a76f384b-809f-442b-bf2a-da52ca3d4edd"
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   "outputs": [
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   "source": [
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  },
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   "metadata": {
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   ]
  }
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