Flipkart_Amazon_Match.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f62b0e11",
   "metadata": {},
   "source": [
    "## Flipkart_Amazon_Matching Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a54fe08c",
   "metadata": {},
   "source": [
    "For this problem, I will be approaching it using NLP (Natural language processing). While downloading the pictures of each product and clustering similar pictures would work and likely be more accurate, it is more computationally expensive. This is why I will be opting out for a more strightforward and simple process. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ea22f68c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer as tf_idf\n",
    "import nltk\n",
    "from nltk.corpus import wordnet\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from difflib import SequenceMatcher"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ff62634",
   "metadata": {},
   "source": [
    "## Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "19d5f84b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading necessary dataframes\n",
    "amazon = pd.read_csv('amz_com-ecommerce_sample.csv')\n",
    "flipkart = pd.read_csv('flipkart_com-ecommerce_sample.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db70f6aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['uniq_id', 'crawl_timestamp', 'product_url', 'product_name',\n",
       "       'product_category_tree', 'pid', 'retail_price', 'discounted_price',\n",
       "       'image', 'is_FK_Advantage_product', 'description', 'product_rating',\n",
       "       'overall_rating', 'brand', 'product_specifications'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amazon.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "99e8a266",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['uniq_id', 'crawl_timestamp', 'product_url', 'product_name',\n",
       "       'product_category_tree', 'pid', 'retail_price', 'discounted_price',\n",
       "       'image', 'is_FK_Advantage_product', 'description', 'product_rating',\n",
       "       'overall_rating', 'brand', 'product_specifications'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flipkart.columns #both of their columns have identical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3b811d22",
   "metadata": {},
   "outputs": [],
   "source": [
    "#introducing a new column which shows if an item purchase was from amazon or flipkart\n",
    "amazon['flipkart or amazon']='amazon'\n",
    "flipkart['flipkart or amazon']='flipkart'\n",
    "dfs=[flipkart,amazon]\n",
    "df=pd.concat(dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1e4d8ebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping unnecessary columns\n",
    "df = df[['product_name','description','product_specifications','retail_price','discounted_price','flipkart or amazon']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "15391669",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 40000 entries, 0 to 19999\n",
      "Data columns (total 6 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   product_name            40000 non-null  object \n",
      " 1   description             39996 non-null  object \n",
      " 2   product_specifications  39972 non-null  object \n",
      " 3   retail_price            39922 non-null  float64\n",
      " 4   discounted_price        39922 non-null  float64\n",
      " 5   flipkart or amazon      40000 non-null  object \n",
      "dtypes: float64(2), object(4)\n",
      "memory usage: 2.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2eb8fd69",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True) #dropping missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8cb1e649",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True) #dropping duplicated values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d650394b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39538, 6)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4fefaf2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_name</th>\n",
       "      <th>description</th>\n",
       "      <th>product_specifications</th>\n",
       "      <th>retail_price</th>\n",
       "      <th>discounted_price</th>\n",
       "      <th>flipkart or amazon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19995</th>\n",
       "      <td>WALLDESIGN SMALL VINYL STICKER</td>\n",
       "      <td>Buy WallDesign Small Vinyl Sticker for Rs.730 ...</td>\n",
       "      <td>{\"product_specification\"=&gt;[{\"key\"=&gt;\"Number of ...</td>\n",
       "      <td>1498.0</td>\n",
       "      <td>876.0</td>\n",
       "      <td>amazon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>WALLMANTRA LARGE VINYL STICKERS STICKER</td>\n",
       "      <td>Buy Wallmantra Large Vinyl Stickers Sticker fo...</td>\n",
       "      <td>{\"product_specification\"=&gt;[{\"key\"=&gt;\"Number of ...</td>\n",
       "      <td>1415.0</td>\n",
       "      <td>1424.0</td>\n",
       "      <td>amazon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>ELITE COLLECTION MEDIUM ACRYLIC STICKER</td>\n",
       "      <td>Buy Elite Collection Medium Acrylic Sticker fo...</td>\n",
       "      <td>{\"product_specification\"=&gt;[{\"key\"=&gt;\"Number of ...</td>\n",
       "      <td>1284.0</td>\n",
       "      <td>1196.0</td>\n",
       "      <td>amazon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>ELITE COLLECTION MEDIUM ACRYLIC STICKER</td>\n",
       "      <td>Buy Elite Collection Medium Acrylic Sticker fo...</td>\n",
       "      <td>{\"product_specification\"=&gt;[{\"key\"=&gt;\"Number of ...</td>\n",
       "      <td>1492.0</td>\n",
       "      <td>1364.0</td>\n",
       "      <td>amazon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>ELITE COLLECTION MEDIUM ACRYLIC STICKER</td>\n",
       "      <td>Buy Elite Collection Medium Acrylic Sticker fo...</td>\n",
       "      <td>{\"product_specification\"=&gt;[{\"key\"=&gt;\"Number of ...</td>\n",
       "      <td>1484.0</td>\n",
       "      <td>1247.0</td>\n",
       "      <td>amazon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  product_name  \\\n",
       "19995           WALLDESIGN SMALL VINYL STICKER   \n",
       "19996  WALLMANTRA LARGE VINYL STICKERS STICKER   \n",
       "19997  ELITE COLLECTION MEDIUM ACRYLIC STICKER   \n",
       "19998  ELITE COLLECTION MEDIUM ACRYLIC STICKER   \n",
       "19999  ELITE COLLECTION MEDIUM ACRYLIC STICKER   \n",
       "\n",
       "                                             description  \\\n",
       "19995  Buy WallDesign Small Vinyl Sticker for Rs.730 ...   \n",
       "19996  Buy Wallmantra Large Vinyl Stickers Sticker fo...   \n",
       "19997  Buy Elite Collection Medium Acrylic Sticker fo...   \n",
       "19998  Buy Elite Collection Medium Acrylic Sticker fo...   \n",
       "19999  Buy Elite Collection Medium Acrylic Sticker fo...   \n",
       "\n",
       "                                  product_specifications  retail_price  \\\n",
       "19995  {\"product_specification\"=>[{\"key\"=>\"Number of ...        1498.0   \n",
       "19996  {\"product_specification\"=>[{\"key\"=>\"Number of ...        1415.0   \n",
       "19997  {\"product_specification\"=>[{\"key\"=>\"Number of ...        1284.0   \n",
       "19998  {\"product_specification\"=>[{\"key\"=>\"Number of ...        1492.0   \n",
       "19999  {\"product_specification\"=>[{\"key\"=>\"Number of ...        1484.0   \n",
       "\n",
       "       discounted_price flipkart or amazon  \n",
       "19995             876.0             amazon  \n",
       "19996            1424.0             amazon  \n",
       "19997            1196.0             amazon  \n",
       "19998            1364.0             amazon  \n",
       "19999            1247.0             amazon  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37d654bf",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "69adb493",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting the two neccessary columns to list\n",
    "df['product_name']=df['product_name'].apply(lambda x:x.split(' '))\n",
    "df['description']=df['description'].apply(lambda x:x.split(' '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "90090f4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing spaces\n",
    "df['description']=df['description'].apply(lambda x: [i.replace(\" \",\"\") for i in x]) \n",
    "df['product_name']=df['product_name'].apply(lambda x: [i.replace(\" \",\"\") for i in x]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c8a3938a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating new column which consists of product's desciption and product's name\n",
    "df['Total']=df['description'] + df['product_name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "375f094b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting lists back to string\n",
    "df['product_name']=df['product_name'].apply(lambda x: ' '.join(x))\n",
    "df['Total']=df['Total'].apply(lambda x: ' '.join(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3288fd35",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "10283aff",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(['index'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ad122663",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_wordnet_pos(word):\n",
    "    \"\"\"\n",
    "    This funtion uses the pos_tag to return the part of speech of words in sentences.\n",
    "    \n",
    "    word: string\n",
    "    The word to check for the part of speech\n",
    "    \n",
    "    return\n",
    "    -------\n",
    "    returns the part of speech of word. \n",
    "    \"\"\"\n",
    "    tag = nltk.pos_tag([word])[0][1][0].upper()\n",
    "    tag_dict = {\"J\": wordnet.ADJ,\n",
    "                \"N\": wordnet.NOUN,\n",
    "                \"V\": wordnet.VERB,\n",
    "                \"R\": wordnet.ADV}\n",
    "\n",
    "    return tag_dict.get(tag, wordnet.NOUN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b6987ff2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lemmatizer_(word):\n",
    "    \"\"\"\n",
    "    This funtion uses the lemmatizes words while considering their part of speech\n",
    "    \n",
    "    word: string\n",
    "    The word to lemmatize\n",
    "    \n",
    "    return\n",
    "    -------\n",
    "    returns the lemmatized version of the word\n",
    "    \"\"\"\n",
    "    lemmatizer=WordNetLemmatizer()\n",
    "    return lemmatizer.lemmatize(word,get_wordnet_pos(word))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f779f77c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Total']=df['Total'].apply(lemmatizer_) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4f3a8347",
   "metadata": {},
   "outputs": [],
   "source": [
    "#A tf_idf vectorizer is used to convert words to vectors based on their frequency and importance.\n",
    "#Fit transform will basically fit the words into what we've set for tfidf and the toarray returns an array of vectors\n",
    "\n",
    "vectorizer=tf_idf(max_features=3000,stop_words='english',lowercase=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1193937e",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectors=vectorizer.fit_transform(df['Total']).toarray() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5a4d5ba1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39538, 3000)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectors.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82b5b5b0",
   "metadata": {},
   "source": [
    "The cosine similarity will measure the similarity between each feature words and product title. Indices of Products with highest similarity will be returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "42291be1",
   "metadata": {},
   "outputs": [],
   "source": [
    "similarity=cosine_similarity(vectors) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "eb5eb77e",
   "metadata": {},
   "outputs": [
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       " 0.15872366952944492,\n",
       " ...]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#for example the similarity of product with index three sorted in decending order\n",
    "sorted(similarity[3],reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3dcf9136",
   "metadata": {},
   "outputs": [],
   "source": [
    "def search(word,product_name):\n",
    "    \"\"\"\n",
    "    This funtion checks for the similarity between two words and return the word if the similarity is greater than 0.8\n",
    "    word: A string\n",
    "    word 'A' to be compared\n",
    "    \n",
    "    product_name: string\n",
    "    word 'B' to compare word 'A' with\n",
    "    \n",
    "    return\n",
    "    -------\n",
    "    A word 'A' if word 'A' and 'B' ae 0.8 similar\n",
    "    \"\"\"\n",
    "    if SequenceMatcher(a = word.lower(),b = product_name.lower()).ratio() > 0.8:\n",
    "        return word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fee446f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def match(Product_name):\n",
    "    \"\"\"\n",
    "    This funtion matches a name with another in the dataframe that has the highest similarity to it. \n",
    "    Product_name: string\n",
    "    Name of product to be matched\n",
    "    \n",
    "    return\n",
    "    -------\n",
    "    A dataframe showing the the prices in both amazon and flipkart and the names in both places.\n",
    "    \"\"\"\n",
    "    Product_name = Product_name\n",
    "    df_= pd.DataFrame() # a temp dataframe that holds result of the search function\n",
    "    df_['product_name'] = df['product_name'].apply(search,product_name=Product_name) #applying search funtion\n",
    "    df_['flipkart or amazon']=df['flipkart or amazon']\n",
    "    df_.dropna(axis=0,inplace=True)\n",
    "    df_new=pd.DataFrame()\n",
    "    try: #incase the product does not exist in flipkart\n",
    "        if Product_name in list(df_['product_name'].where(df_['flipkart or amazon']=='flipkart').dropna()):\n",
    "            product_index_flipkart=df[df['product_name'] == Product_name].where(df['flipkart or amazon']=='flipkart').dropna().index[0]\n",
    "        else:\n",
    "            Product_name = list(df_['product_name'].where(df['flipkart or amazon']=='flipkart').dropna())[0]\n",
    "            product_index_flipkart=df[df['product_name'] == Product_name].where(df['flipkart or amazon']=='flipkart').dropna().index[0]\n",
    "    except:\n",
    "        df_new['Product name on Flipkart'] = ['Product Not in flipkart']\n",
    "        df_new['Retail Price in Flipkart'] = ['Product Not in flipkart']\n",
    "        df_new['Discounted Price in Flipkart'] = ['Product Not in flipkart']\n",
    "        try: #in case the product doesn't exist in amazon\n",
    "            if Product_name in list(df_['product_name'].where(df_['flipkart or amazon']=='amazon').dropna()):\n",
    "                product_index_amazon=df[df['product_name'] == Product_name].where(df['flipkart or amazon']=='amazon').dropna().index[0]\n",
    "            else:\n",
    "                Product_name = list(df_['product_name'].where(df['flipkart or amazon']=='amazon').dropna())[0]\n",
    "                product_index_amazon=df[df['product_name'] == Product_name].where(df['flipkart or amazon']=='amazon').dropna().index[0]\n",
    "        except:\n",
    "            df_new['Product name on Amazon'] = ['Product Not in amazon']\n",
    "            df_new['Retail Price in Amazon'] = ['Product Not in amazon']\n",
    "            df_new['Discounted Price in Amazon'] = ['Product Not in amazon']\n",
    "        else:\n",
    "            distances_amazon =similarity[product_index_amazon]\n",
    "            amazon_product=sorted(list(enumerate(distances_amazon)),reverse=True,key=lambda x:x[1])[0]\n",
    "            df_new['Product name on Amazon'] = [df.iloc[amazon_product[0]].product_name]\n",
    "            df_new['Retail Price in Amazon'] = [df.iloc[amazon_product[0]].retail_price]\n",
    "            df_new['Discounted Price in Amazon'] = [df.iloc[amazon_product[0]].discounted_price]\n",
    "    else:\n",
    "        try: #in case the product doesn't exist in amazon\n",
    "            if Product_name in list(df_['product_name'].where(df_['flipkart or amazon']=='amazon').dropna()):\n",
    "                product_index_amazon=df[df['product_name'] == Product_name].where(df['flipkart or amazon']=='amazon').dropna().index[0]\n",
    "            else:\n",
    "                Product_name = list(df_['product_name'].where(df['flipkart or amazon']=='amazon').dropna())[0]\n",
    "                product_index_amazon=df[df['product_name'] == Product_name].where(df['flipkart or amazon']=='amazon').dropna().index[0]\n",
    "        except:\n",
    "            product_index_flipkart=df[df['product_name']== Product_name].where(df['flipkart or amazon']=='flipkart').dropna().index[0]\n",
    "            df_new['Product name on Amazon'] = ['Product Not in amazon']\n",
    "            df_new['Retail Price in Amazon'] = ['Product Not in amazon']\n",
    "            df_new['Discounted Price in Amazon'] = ['Product Not in amazon']\n",
    "            distances_flipkart=similarity[product_index_flipkart]\n",
    "            flipkart_product=sorted(list(enumerate(distances_flipkart)),reverse=True,key=lambda x:x[1])[0]\n",
    "            df_new['Product name on Flipkart'] = [df.iloc[flipkart_product[0]].product_name]\n",
    "            df_new['Retail Price in Flipkart'] = [df.iloc[flipkart_product[0]].retail_price]\n",
    "            df_new['Discounted Price in Flipkart'] = [df.iloc[flipkart_product[0]].discounted_price]\n",
    "        else: \n",
    "            distances_amazon =similarity[product_index_amazon] \n",
    "            distances_flipkart=similarity[product_index_flipkart]\n",
    "            flipkart_product=sorted(list(enumerate(distances_flipkart)),reverse=True,key=lambda x:x[1])[0]\n",
    "            amazon_product=sorted(list(enumerate(distances_amazon)),reverse=True,key=lambda x:x[1])[0]\n",
    "            df_new['Product name on Flipkart'] = [df.iloc[flipkart_product[0]].product_name]\n",
    "            df_new['Retail Price in Flipkart'] = [df.iloc[flipkart_product[0]].retail_price]\n",
    "            df_new['Discounted Price in Flipkart'] = [df.iloc[flipkart_product[0]].discounted_price]\n",
    "            df_new['Product name on Amazon'] = [df.iloc[amazon_product[0]].product_name]\n",
    "            df_new['Retail Price in Amazon'] = [df.iloc[amazon_product[0]].retail_price]\n",
    "            df_new['Discounted Price in Amazon'] = [df.iloc[amazon_product[0]].discounted_price]    \n",
    "    return df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f6403bc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product name on Flipkart</th>\n",
       "      <th>Retail Price in Flipkart</th>\n",
       "      <th>Discounted Price in Flipkart</th>\n",
       "      <th>Product name on Amazon</th>\n",
       "      <th>Retail Price in Amazon</th>\n",
       "      <th>Discounted Price in Amazon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDT Women's Leggings</td>\n",
       "      <td>699.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>FDT WOMEN'S Leggings Pants</td>\n",
       "      <td>698.0</td>\n",
       "      <td>362.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product name on Flipkart  Retail Price in Flipkart  \\\n",
       "0     FDT Women's Leggings                     699.0   \n",
       "\n",
       "   Discounted Price in Flipkart      Product name on Amazon  \\\n",
       "0                         309.0  FDT WOMEN'S Leggings Pants   \n",
       "\n",
       "   Retail Price in Amazon  Discounted Price in Amazon  \n",
       "0                   698.0                       362.0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match(\"FDT Women's Leggings\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57610ab7",
   "metadata": {},
   "source": [
    "## In conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "579e708d",
   "metadata": {},
   "source": [
    "The solution to this problem is probably not the most logical but given the number of dataset, training a model with pictures would probably take more time so I recommend this text processing approach. "
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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