Content_Based_reccomendation_system (2).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Group-3 Jyoti (20231849) , Aditi (202318005) , Garvika (202318061)"
      ],
      "metadata": {
        "id": "xIJZPmefDqVh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#1. Data Preprocessing"
      ],
      "metadata": {
        "id": "m72NbToRN7QX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Uq5OVHP-YuYy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "28790fa0-feeb-4736-8544-4b9cb1bb4aa8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SnktRIewG0I1"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import gzip\n",
        "import json\n",
        "import pandas as pd\n",
        "import gzip\n",
        "import json\n",
        "import pandas as pd\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "import regex as re\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.decomposition import TruncatedSVD\n",
        "from sklearn.metrics.pairwise import cosine_similarity"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "import string\n",
        "import re\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "# from google.colab import drive\n",
        "\n",
        "import spacy\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import PorterStemmer\n",
        "from collections import Counter\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.decomposition import TruncatedSVD\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from wordcloud import WordCloud\n",
        "from PIL import Image\n",
        "\n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "nltk.download(\"punkt\", quiet=True)\n",
        "nltk.download(\"stopwords\", quiet=True)\n",
        "stop_words = set(stopwords.words(\"english\"))\n",
        "punctuations = string.punctuation.replace(\"'\", \"\")\n",
        "\n",
        "\n",
        "from google.colab import drive"
      ],
      "metadata": {
        "id": "8U3Fta7gBolw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IJEzb0uyGv3T"
      },
      "outputs": [],
      "source": [
        "def parse(path):\n",
        "  g = gzip.open(path, 'r')\n",
        "  for l in g:\n",
        "    yield json.loads(l)\n",
        "\n",
        "def parse(path):\n",
        "    try:\n",
        "        with gzip.open(path, 'rt') as file:\n",
        "            for line in file:\n",
        "                yield json.loads(line)\n",
        "    except EOFError as e:\n",
        "        print(f\"Error reading file: {e}\")\n",
        "        print(f\"Please check the integrity and format of the file: {path}\")\n",
        "\n",
        "def getDF(path):\n",
        "    try:\n",
        "        data = [d for d in parse(path)]\n",
        "        return pd.DataFrame(data)\n",
        "    except Exception as e:\n",
        "        print(f\"Error processing file: {e}\")\n",
        "        print(f\"Please check the integrity and format of the file: {path}\")\n",
        "\n",
        "# Example usage\n",
        "df= getDF('/content/drive/MyDrive/RecSys/All_Beauty.json.gz')\n",
        "df1= getDF('/content/drive/MyDrive/RecSys/meta_All_Beauty.json.gz')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "comp_data=df\n",
        "comp_data=df.merge(df1, on=\"asin\")"
      ],
      "metadata": {
        "id": "3CSy0UlCZdky"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Comment -2 - Classification of data\n",
        "Classification 1 - on basis of negetive and positive sentiments by analysing the ratings < 3 as 0 (negetive) and rating > 3(positive) and dropping the products which were rated negetively.\n",
        "\n",
        "Classification 2 - on the basis of reviewTime and dropping the products which were reviewd before 2016."
      ],
      "metadata": {
        "id": "xbLqTkgiN_1D"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aIgWT_6RG4PC"
      },
      "outputs": [],
      "source": [
        "df['bin_rate'] = 1\n",
        "df.loc[df['overall'] <= 3, 'bin_rate'] = 0\n",
        "dropped_neg = df[df['bin_rate'] == 0]\n",
        "df = df[df['bin_rate'] != 0]\n",
        "#\n",
        "df['reviewTime'] = pd.to_datetime(df['reviewTime'], )\n",
        "newdata = df[~(df['reviewTime'] < '2016-01-01')]\n",
        "#\n",
        "data= newdata.merge(df1, on=\"asin\")\n",
        "data1= pd.DataFrame(data, columns=[\"reviewerID\",\"reviewText\",\"summary\",\"asin\",\"price\",\"description\",\"title\",\"brand\",\"also_view\"])\n",
        "data2=pd.DataFrame(df1,columns=['description','title','brand'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "iCJH1OGpTy0_",
        "outputId": "3d9d667a-763f-4cd2-c470-2b38dec79746"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        overall  verified reviewTime      reviewerID        asin  \\\n",
              "0           5.0      True 2016-03-10   A5TA1NJOC0PJ5  7414204790   \n",
              "1           5.0      True 2018-03-02  A3RSWSUNBBH49Y  7414204790   \n",
              "2           5.0      True 2017-09-09  A1NNFQIJE08G74  7414204790   \n",
              "3           4.0      True 2017-04-12  A1TZ1QV2QD7QZV  7414204790   \n",
              "4           5.0      True 2017-04-07  A3U25L8AA0LKZP  7414204790   \n",
              "...         ...       ...        ...             ...         ...   \n",
              "152238      5.0      True 2017-05-09   A7WHO9ZFPHU2B  B01HJ84SGM   \n",
              "152239      5.0      True 2017-10-28  A338U2R53FLF9U  B01HJEGTYK   \n",
              "152240      4.0      True 2017-07-22  A3EN87RJFCIORB  B01HJEGTYK   \n",
              "152241      5.0      True 2017-03-16  A3FSOR5IJOFIBE  B01HJEGTYK   \n",
              "152242      5.0      True 2017-03-01  A1B5DK6CTP2P24  B01HJEGTYK   \n",
              "\n",
              "             reviewerName                                         reviewText  \\\n",
              "0         Amazon Customer                                Excellent very good   \n",
              "1                   Jenny  I have OILY skin. I have tried quite a few mat...   \n",
              "2                    niro  I like this one then green one I don't know wh...   \n",
              "3                   Surya  Product delivered was different flavour instea...   \n",
              "4               Moitrayee                         my favorite beauty product   \n",
              "...                   ...                                                ...   \n",
              "152238  David P. Birtwell  I think D-Mannose, Nature's Herbs, Cranberry W...   \n",
              "152239              Gayle  love! love! love! these what a Time Saver for ...   \n",
              "152240           Cyndi P.                         Good quality, a bit small.   \n",
              "152241                TYW  I was skeptical about buying this.  Worried it...   \n",
              "152242     Norma Jennings                           Makes me look good fast.   \n",
              "\n",
              "                           summary  unixReviewTime vote  ... feature  \\\n",
              "0                       Five Stars      1457568000  NaN  ...      []   \n",
              "1                         Balanced      1519948800  NaN  ...      []   \n",
              "2                 This thing works      1504915200  NaN  ...      []   \n",
              "3                       Four Stars      1491955200  NaN  ...      []   \n",
              "4       my favorite beauty product      1491523200  NaN  ...      []   \n",
              "...                            ...             ...  ...  ...     ...   \n",
              "152238               Lifesavers...      1494288000  NaN  ...      []   \n",
              "152239                  love! love      1509148800  NaN  ...      []   \n",
              "152240                       Shiny      1500681600  NaN  ...      []   \n",
              "152241                     Awesome      1489622400   34  ...      []   \n",
              "152242                  Five Stars      1488326400   46  ...      []   \n",
              "\n",
              "                                       rank  \\\n",
              "0        67,701 in Beauty & Personal Care (   \n",
              "1        67,701 in Beauty & Personal Care (   \n",
              "2        67,701 in Beauty & Personal Care (   \n",
              "3        67,701 in Beauty & Personal Care (   \n",
              "4        67,701 in Beauty & Personal Care (   \n",
              "...                                     ...   \n",
              "152238  207,410 in Beauty & Personal Care (   \n",
              "152239  478,123 in Beauty & Personal Care (   \n",
              "152240  478,123 in Beauty & Personal Care (   \n",
              "152241  478,123 in Beauty & Personal Care (   \n",
              "152242  478,123 in Beauty & Personal Care (   \n",
              "\n",
              "                                                also_view  \\\n",
              "0       [3254895630, B007VL1D9S, B00EH9A0RI, B0773MBG4...   \n",
              "1       [3254895630, B007VL1D9S, B00EH9A0RI, B0773MBG4...   \n",
              "2       [3254895630, B007VL1D9S, B00EH9A0RI, B0773MBG4...   \n",
              "3       [3254895630, B007VL1D9S, B00EH9A0RI, B0773MBG4...   \n",
              "4       [3254895630, B007VL1D9S, B00EH9A0RI, B0773MBG4...   \n",
              "...                                                   ...   \n",
              "152238  [B01KON9B4S, B000JN4CR0, B071ZHMRHS, B01HJ84TN...   \n",
              "152239                           [B06XNM7CGD, B071D7TMQS]   \n",
              "152240                           [B06XNM7CGD, B071D7TMQS]   \n",
              "152241                           [B06XNM7CGD, B071D7TMQS]   \n",
              "152242                           [B06XNM7CGD, B071D7TMQS]   \n",
              "\n",
              "                                                  details    main_cat  \\\n",
              "0       {'Shipping Weight:': '12 ounces (', 'ASIN: ': ...  All Beauty   \n",
              "1       {'Shipping Weight:': '12 ounces (', 'ASIN: ': ...  All Beauty   \n",
              "2       {'Shipping Weight:': '12 ounces (', 'ASIN: ': ...  All Beauty   \n",
              "3       {'Shipping Weight:': '12 ounces (', 'ASIN: ': ...  All Beauty   \n",
              "4       {'Shipping Weight:': '12 ounces (', 'ASIN: ': ...  All Beauty   \n",
              "...                                                   ...         ...   \n",
              "152238  {'Shipping Weight:': '1 pounds (', 'ASIN: ': '...  All Beauty   \n",
              "152239  {'\n",
              "    Item Weight: \n",
              "    ': '1.12 ounces', 'Sh...  All Beauty   \n",
              "152240  {'\n",
              "    Item Weight: \n",
              "    ': '1.12 ounces', 'Sh...  All Beauty   \n",
              "152241  {'\n",
              "    Item Weight: \n",
              "    ': '1.12 ounces', 'Sh...  All Beauty   \n",
              "152242  {'\n",
              "    Item Weight: \n",
              "    ': '1.12 ounces', 'Sh...  All Beauty   \n",
              "\n",
              "       similar_item date   price  \\\n",
              "0                         $12.15   \n",
              "1                         $12.15   \n",
              "2                         $12.15   \n",
              "3                         $12.15   \n",
              "4                         $12.15   \n",
              "...             ...  ...     ...   \n",
              "152238                    $55.63   \n",
              "152239                             \n",
              "152240                             \n",
              "152241                             \n",
              "152242                             \n",
              "\n",
              "                                                 imageURL  \\\n",
              "0       [https://images-na.ssl-images-amazon.com/image...   \n",
              "1       [https://images-na.ssl-images-amazon.com/image...   \n",
              "2       [https://images-na.ssl-images-amazon.com/image...   \n",
              "3       [https://images-na.ssl-images-amazon.com/image...   \n",
              "4       [https://images-na.ssl-images-amazon.com/image...   \n",
              "...                                                   ...   \n",
              "152238                                                 []   \n",
              "152239  [https://images-na.ssl-images-amazon.com/image...   \n",
              "152240  [https://images-na.ssl-images-amazon.com/image...   \n",
              "152241  [https://images-na.ssl-images-amazon.com/image...   \n",
              "152242  [https://images-na.ssl-images-amazon.com/image...   \n",
              "\n",
              "                                          imageURLHighRes  \n",
              "0       [https://images-na.ssl-images-amazon.com/image...  \n",
              "1       [https://images-na.ssl-images-amazon.com/image...  \n",
              "2       [https://images-na.ssl-images-amazon.com/image...  \n",
              "3       [https://images-na.ssl-images-amazon.com/image...  \n",
              "4       [https://images-na.ssl-images-amazon.com/image...  \n",
              "...                                                   ...  \n",
              "152238                                                 []  \n",
              "152239  [https://images-na.ssl-images-amazon.com/image...  \n",
              "152240  [https://images-na.ssl-images-amazon.com/image...  \n",
              "152241  [https://images-na.ssl-images-amazon.com/image...  \n",
              "152242  [https://images-na.ssl-images-amazon.com/image...  \n",
              "\n",
              "[152243 rows x 31 columns]"
            ],
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>overall</th>\n",
              "      <th>verified</th>\n",
              "      <th>reviewTime</th>\n",
              "      <th>reviewerID</th>\n",
              "      <th>asin</th>\n",
              "      <th>reviewerName</th>\n",
              "      <th>reviewText</th>\n",
              "      <th>summary</th>\n",
              "      <th>unixReviewTime</th>\n",
              "      <th>vote</th>\n",
              "      <th>...</th>\n",
              "      <th>feature</th>\n",
              "      <th>rank</th>\n",
              "      <th>also_view</th>\n",
              "      <th>details</th>\n",
              "      <th>main_cat</th>\n",
              "      <th>similar_item</th>\n",
              "      <th>date</th>\n",
              "      <th>price</th>\n",
              "      <th>imageURL</th>\n",
              "      <th>imageURLHighRes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.0</td>\n",
              "      <td>True</td>\n",
              "      <td>2016-03-10</td>\n",
              "      <td>A5TA1NJOC0PJ5</td>\n",
              "      <td>7414204790</td>\n",
              "      <td>Amazon Customer</td>\n",
              "      <td>Excellent very good</td>\n",
              "      <td>Five Stars</td>\n",
              "      <td>1457568000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>[]</td>\n",
              "      <td>67,701 in Beauty &amp; Personal Care (</td>\n",
              "      <td>[3254895630, B007VL1D9S, B00EH9A0RI, B0773MBG4...</td>\n",
              "      <td>{'Shipping Weight:': '12 ounces (', 'ASIN: ': ...</td>\n",
              "      <td>All Beauty</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>$12.15</td>\n",
              "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
              "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.0</td>\n",
              "      <td>True</td>\n",
              "      <td>2018-03-02</td>\n",
              "      <td>A3RSWSUNBBH49Y</td>\n",
              "      <td>7414204790</td>\n",
              "      <td>Jenny</td>\n",
              "      <td>I have OILY skin. I have tried quite a few mat...</td>\n",
              "      <td>Balanced</td>\n",
              "      <td>1519948800</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>[]</td>\n",
              "      <td>67,701 in Beauty &amp; Personal Care (</td>\n",
              "      <td>[3254895630, B007VL1D9S, B00EH9A0RI, B0773MBG4...</td>\n",
              "      <td>{'Shipping Weight:': '12 ounces (', 'ASIN: ': ...</td>\n",
              "      <td>All Beauty</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>$12.15</td>\n",
              "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
              "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.0</td>\n",
              "      <td>True</td>\n",
              "      <td>2017-09-09</td>\n",
              "      <td>A1NNFQIJE08G74</td>\n",
              "      <td>7414204790</td>\n",
              "      <td>niro</td>\n",
              "      <td>I like this one then green one I don't know wh...</td>\n",
              "      <td>This thing works</td>\n",
              "      <td>1504915200</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>[]</td>\n",
              "      <td>67,701 in Beauty &amp; Personal Care (</td>\n",
              "      <td>[3254895630, B007VL1D9S, B00EH9A0RI, B0773MBG4...</td>\n",
              "      <td>{'Shipping Weight:': '12 ounces (', 'ASIN: ': ...</td>\n",
              "      <td>All Beauty</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>$12.15</td>\n",
              "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
              "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.0</td>\n",
              "      <td>True</td>\n",
              "      <td>2017-04-12</td>\n",
              "      <td>A1TZ1QV2QD7QZV</td>\n",
              "      <td>7414204790</td>\n",
              "      <td>Surya</td>\n",
              "      <td>Product delivered was different flavour instea...</td>\n",
              "      <td>Four Stars</td>\n",
              "      <td>1491955200</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>[]</td>\n",
              "      <td>67,701 in Beauty &amp; Personal Care (</td>\n",
              "      <td>[3254895630, B007VL1D9S, B00EH9A0RI, B0773MBG4...</td>\n",
              "      <td>{'Shipping Weight:': '12 ounces (', 'ASIN: ': ...</td>\n",
              "      <td>All Beauty</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>$12.15</td>\n",
              "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
              "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>True</td>\n",
              "      <td>2017-04-07</td>\n",
              "      <td>A3U25L8AA0LKZP</td>\n",
              "      <td>7414204790</td>\n",
              "      <td>Moitrayee</td>\n",
              "      <td>my favorite beauty product</td>\n",
              "      <td>my favorite beauty product</td>\n",
              "      <td>1491523200</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>[]</td>\n",
              "      <td>67,701 in Beauty &amp; Personal Care (</td>\n",
              "      <td>[3254895630, B007VL1D9S, B00EH9A0RI, B0773MBG4...</td>\n",
              "      <td>{'Shipping Weight:': '12 ounces (', 'ASIN: ': ...</td>\n",
              "      <td>All Beauty</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>$12.15</td>\n",
              "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
              "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152238</th>\n",
              "      <td>5.0</td>\n",
              "      <td>True</td>\n",
              "      <td>2017-05-09</td>\n",
              "      <td>A7WHO9ZFPHU2B</td>\n",
              "      <td>B01HJ84SGM</td>\n",
              "      <td>David P. Birtwell</td>\n",
              "      <td>I think D-Mannose, Nature's Herbs, Cranberry W...</td>\n",
              "      <td>Lifesavers...</td>\n",
              "      <td>1494288000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>[]</td>\n",
              "      <td>207,410 in Beauty &amp; Personal Care (</td>\n",
              "      <td>[B01KON9B4S, B000JN4CR0, B071ZHMRHS, B01HJ84TN...</td>\n",
              "      <td>{'Shipping Weight:': '1 pounds (', 'ASIN: ': '...</td>\n",
              "      <td>All Beauty</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>$55.63</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152239</th>\n",
              "      <td>5.0</td>\n",
              "      <td>True</td>\n",
              "      <td>2017-10-28</td>\n",
              "      <td>A338U2R53FLF9U</td>\n",
              "      <td>B01HJEGTYK</td>\n",
              "      <td>Gayle</td>\n",
              "      <td>love! love! love! these what a Time Saver for ...</td>\n",
              "      <td>love! love</td>\n",
              "      <td>1509148800</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>[]</td>\n",
              "      <td>478,123 in Beauty &amp; Personal Care (</td>\n",
              "      <td>[B06XNM7CGD, B071D7TMQS]</td>\n",
              "      <td>{'\n",
              "    Item Weight: \n",
              "    ': '1.12 ounces', 'Sh...</td>\n",
              "      <td>All Beauty</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
              "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152240</th>\n",
              "      <td>4.0</td>\n",
              "      <td>True</td>\n",
              "      <td>2017-07-22</td>\n",
              "      <td>A3EN87RJFCIORB</td>\n",
              "      <td>B01HJEGTYK</td>\n",
              "      <td>Cyndi P.</td>\n",
              "      <td>Good quality, a bit small.</td>\n",
              "      <td>Shiny</td>\n",
              "      <td>1500681600</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>[]</td>\n",
              "      <td>478,123 in Beauty &amp; Personal Care (</td>\n",
              "      <td>[B06XNM7CGD, B071D7TMQS]</td>\n",
              "      <td>{'\n",
              "    Item Weight: \n",
              "    ': '1.12 ounces', 'Sh...</td>\n",
              "      <td>All Beauty</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
              "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152241</th>\n",
              "      <td>5.0</td>\n",
              "      <td>True</td>\n",
              "      <td>2017-03-16</td>\n",
              "      <td>A3FSOR5IJOFIBE</td>\n",
              "      <td>B01HJEGTYK</td>\n",
              "      <td>TYW</td>\n",
              "      <td>I was skeptical about buying this.  Worried it...</td>\n",
              "      <td>Awesome</td>\n",
              "      <td>1489622400</td>\n",
              "      <td>34</td>\n",
              "      <td>...</td>\n",
              "      <td>[]</td>\n",
              "      <td>478,123 in Beauty &amp; Personal Care (</td>\n",
              "      <td>[B06XNM7CGD, B071D7TMQS]</td>\n",
              "      <td>{'\n",
              "    Item Weight: \n",
              "    ': '1.12 ounces', 'Sh...</td>\n",
              "      <td>All Beauty</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
              "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152242</th>\n",
              "      <td>5.0</td>\n",
              "      <td>True</td>\n",
              "      <td>2017-03-01</td>\n",
              "      <td>A1B5DK6CTP2P24</td>\n",
              "      <td>B01HJEGTYK</td>\n",
              "      <td>Norma Jennings</td>\n",
              "      <td>Makes me look good fast.</td>\n",
              "      <td>Five Stars</td>\n",
              "      <td>1488326400</td>\n",
              "      <td>46</td>\n",
              "      <td>...</td>\n",
              "      <td>[]</td>\n",
              "      <td>478,123 in Beauty &amp; Personal Care (</td>\n",
              "      <td>[B06XNM7CGD, B071D7TMQS]</td>\n",
              "      <td>{'\n",
              "    Item Weight: \n",
              "    ': '1.12 ounces', 'Sh...</td>\n",
              "      <td>All Beauty</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
              "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>152243 rows × 31 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3a0e64d5-8b80-4d7a-8131-25acde15b4a8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3a0e64d5-8b80-4d7a-8131-25acde15b4a8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3a0e64d5-8b80-4d7a-8131-25acde15b4a8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-09595b7f-37d8-483a-adb0-25cee787c72b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-09595b7f-37d8-483a-adb0-25cee787c72b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Dropping the null values in any cells row wise.\n",
        "\n",
        "```\n",
        "# This is formatted as code\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "8opFY77LOcDs"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yRvfDrZKMFb2"
      },
      "outputs": [],
      "source": [
        "#\n",
        "data1.dropna(subset=data1.columns, inplace=True)\n",
        "data1 = data1[data1.ne('').all(axis=1)]\n",
        "data2.dropna(subset=data2.columns, inplace=True)\n",
        "data2 = data2[data2.ne('').all(axis=1)]\n",
        "data2 = data2[data2.astype(str) != '[]'].dropna()\n",
        "data1 = data1[data1.astype(str) != '[]'].dropna()\n",
        "data1['description']=data1['description'].apply(lambda x:''.join(x))\n",
        "data2['description']=data2['description'].apply(lambda x:''.join(x))\n",
        "data2['title']=data2['title'].apply(lambda x:''.join(x))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Comment -3 - Building User profile and Item profile -\n",
        "User profile consists of review+description+title+brand as a single document each for each product.\n",
        "\n",
        "Item profile consists of description+title+brand as a single document each for each product."
      ],
      "metadata": {
        "id": "UdXTrgY4Pn_y"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wGmZgg5LOJ8I"
      },
      "outputs": [],
      "source": [
        "data1['tags'] = (\n",
        "    data1['reviewText'].astype(str) +\n",
        "    ' ' +\n",
        "    data1['description'].astype(str)+\n",
        "    ' ' +\n",
        "    data1['title'].astype(str)+\n",
        "    ' ' +\n",
        "    data1['brand'].astype(str)\n",
        ")\n",
        "\n",
        "data2['tags2']=(\n",
        "    data2['description'].astype(str)+\n",
        "    ' ' +\n",
        "    data2['title'].astype(str)+\n",
        "    ' ' +\n",
        "    data2['brand'].astype(str)\n",
        ")\n",
        "\n",
        "all_beauty= data1[['title','reviewerID','asin','tags']]\n",
        "all_beauty_des=data2[['title','tags2']]\n",
        "\n",
        "all_beauty=pd.DataFrame(all_beauty)\n",
        "all_beauty_des=pd.DataFrame(all_beauty_des)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Comment - 4 - Lemmatization, RegEx and StopWords removal"
      ],
      "metadata": {
        "id": "LB5vfBloqvxL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o_jXUIL0QVxR",
        "outputId": "13aa6188-99be-4e8c-c5cb-5453be3eba67"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        }
      ],
      "source": [
        "all_beauty['clean_tags'] = all_beauty['tags'].apply(lambda s: ' '.join(re.sub(\"[.,!?:;-='...$()-@#_]\", \" \", s).lower().split()))\n",
        "all_beauty[\"clean_tags\"].replace(r'\\d+', '', regex=True, inplace=True)\n",
        "all_beauty_des['clean_tags2'] = all_beauty_des['tags2'].apply(lambda s: ' '.join(re.sub(\"[.,!?:;-='...$()-@#_]\", \" \", s).lower().split()))\n",
        "all_beauty_des[\"clean_tags2\"].replace(r'\\d+', '', regex=True, inplace=True)\n",
        "#\n",
        "nltk.download('stopwords')\n",
        "stop = set(stopwords.words('english'))\n",
        "\n",
        "#\n",
        "def rem_en(input_txt):\n",
        "    words = input_txt.lower().split()\n",
        "    noise_free_words = [word for word in words if word not in stop]\n",
        "    noise_free_text = \" \".join(noise_free_words)\n",
        "    return noise_free_text\n",
        "all_beauty[\"clean_tags\"] = all_beauty[\"clean_tags\"].apply(lambda s: rem_en(s))\n",
        "all_beauty_des[\"clean_tags2\"] = all_beauty_des[\"clean_tags2\"].apply(lambda s: rem_en(s))\n",
        "\n",
        "#\n",
        "nltk.download('wordnet')\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "\n",
        "#\n",
        "def lemmatize_words(text):\n",
        "    return \" \".join([lemmatizer.lemmatize(word) for word in text.split()])\n",
        "all_beauty['clean_tags'] = all_beauty['clean_tags'].apply(lambda text: lemmatize_words(text))\n",
        "all_beauty_des[\"clean_tags2\"] = all_beauty_des[\"clean_tags2\"].apply(lambda text: lemmatize_words(text))\n",
        "#\n",
        "all_beauty = all_beauty.reset_index(drop=True)\n",
        "all_beauty_des = all_beauty_des.reset_index(drop=True)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#2.EDA"
      ],
      "metadata": {
        "id": "sq806VLOAKsQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "reviews = pd.read_json(\"/content/drive/MyDrive/RecSys/All_Beauty.json.gz\", lines=True)\n",
        "metadata = pd.read_json(\"/content/drive/MyDrive/RecSys/meta_All_Beauty.json.gz\", lines=True)\n",
        "\n",
        "reviews = reviews[[\"asin\", \"reviewerID\", \"overall\", \"summary\", \"reviewText\"]]\n",
        "metadata = metadata[[\"asin\", \"title\", \"description\", \"brand\"]] # , \"rank\", \"also_buy\", \"also_view\"\n",
        "reviews.rename(columns={\"reviewerID\": \"id\", \"overall\": \"rating\", \"summary\": \"head\", \"reviewText\": \"body\"}, inplace=True)\n",
        "\n",
        "ratings = reviews[\"rating\"].copy()\n",
        "reviews = reviews[reviews[\"rating\"] > 3]\n",
        "reviews.dropna(inplace=True)\n",
        "metadata.dropna(inplace=True)\n",
        "\n",
        "# metadata[\"rank\"] = metadata[\"rank\"].apply(str).apply(lambda x: (int(re.search(r\"[0-9,]+\", x).group(0).replace(\",\", \"\"))) if (re.fullmatch(r\"[0-9,]+ in Beauty \\& Personal Care \\(\", x) != None) else (-1))\n",
        "metadata[\"description\"] = [\" \".join(map(str, li)) for li in metadata[\"description\"]]\n",
        "\n",
        "reviews[\"head\"] = reviews[\"head\"].apply(lambda x: x.translate(str.maketrans(\"\", \"\", punctuations)).lower())\n",
        "reviews[\"body\"] = reviews[\"body\"].apply(lambda x: x.translate(str.maketrans(\"\", \"\", punctuations)).lower())\n",
        "metadata[\"title\"] = metadata[\"title\"].apply(lambda x: x.translate(str.maketrans(\"\", \"\", punctuations)).lower())\n",
        "metadata[\"description\"] = metadata[\"description\"].apply(lambda x: x.translate(str.maketrans(\"\", \"\", punctuations)).lower())\n",
        "metadata[\"brand\"] = metadata[\"brand\"].apply(lambda x: x.translate(str.maketrans(\"\", \"\", punctuations)).lower())\n",
        "\n",
        "reviews[\"content\"] = reviews[\"head\"] + \" \" + reviews[\"body\"]\n",
        "metadata[\"content\"] = metadata[\"title\"] + \" \" + metadata[\"description\"] + \" \" + metadata[\"brand\"]\n",
        "reviews.drop(columns=[\"head\", \"body\", \"rating\"], inplace=True)\n",
        "metadata.drop(columns=[\"title\", \"description\", \"brand\"], inplace=True)\n",
        "\n",
        "reviews.drop_duplicates(inplace=True)\n",
        "metadata.drop_duplicates(inplace=True)\n",
        "\n",
        "reviews = reviews.merge(metadata, on=\"asin\", how=\"inner\", suffixes=(\"_\", \"\"))"
      ],
      "metadata": {
        "id": "pNzLp48bBHZo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "doc = nlp(reviews[\"content\"].values[4].replace(\"\\\\\", \"\"))\n",
        "for sent in doc.sents:\n",
        "  print(\">\", sent.start, sent.end, \">\", sent)\n",
        "for sent in doc.sents:\n",
        "  for tok in sent:\n",
        "    print(tok, \"|\", tok.lemma_, \"|\", spacy.explain(tok.pos_), \"|\", tok.is_stop)\n",
        "  break\n",
        "for sent in doc.sents:\n",
        "  for tok in sent:\n",
        "    print(tok, \"|\", tok.morph)\n",
        "  break\n",
        "for ent in doc.ents:\n",
        "  print(ent, \"|\", ent.label_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LPMSxVQhCZHw",
        "outputId": "162eaccc-a544-4731-af46-4092a8e1a15c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "> 0 27 > lacto calamine skin balance oil control 120 ml pack of 2 lacto calamine skin balance daily nourishing lotion  oil control 120ml pack of 2 pirmal healthcare\n",
            "lacto | lacto | proper noun | False\n",
            "calamine | calamine | proper noun | False\n",
            "skin | skin | proper noun | False\n",
            "balance | balance | noun | False\n",
            "oil | oil | noun | False\n",
            "control | control | noun | False\n",
            "120 | 120 | numeral | False\n",
            "ml | ml | noun | False\n",
            "pack | pack | noun | False\n",
            "of | of | adposition | True\n",
            "2 | 2 | numeral | False\n",
            "lacto | lacto | noun | False\n",
            "calamine | calamine | proper noun | False\n",
            "skin | skin | proper noun | False\n",
            "balance | balance | proper noun | False\n",
            "daily | daily | adjective | False\n",
            "nourishing | nourishing | noun | False\n",
            "lotion | lotion | noun | False\n",
            "  |   | space | False\n",
            "oil | oil | noun | False\n",
            "control | control | noun | False\n",
            "120ml | 120ml | adjective | False\n",
            "pack | pack | noun | False\n",
            "of | of | adposition | True\n",
            "2 | 2 | numeral | False\n",
            "pirmal | pirmal | noun | False\n",
            "healthcare | healthcare | noun | False\n",
            "lacto | Number=Sing\n",
            "calamine | Number=Sing\n",
            "skin | Number=Sing\n",
            "balance | Number=Sing\n",
            "oil | Number=Sing\n",
            "control | Number=Sing\n",
            "120 | NumType=Card\n",
            "ml | Number=Sing\n",
            "pack | Number=Sing\n",
            "of | \n",
            "2 | NumType=Card\n",
            "lacto | Number=Sing\n",
            "calamine | Number=Sing\n",
            "skin | Number=Sing\n",
            "balance | Number=Sing\n",
            "daily | Degree=Pos\n",
            "nourishing | Number=Sing\n",
            "lotion | Number=Sing\n",
            "  | \n",
            "oil | Number=Sing\n",
            "control | Number=Sing\n",
            "120ml | Degree=Pos\n",
            "pack | Number=Sing\n",
            "of | \n",
            "2 | NumType=Card\n",
            "pirmal | Number=Sing\n",
            "healthcare | Number=Sing\n",
            "lacto calamine skin | PERSON\n",
            "120 | CARDINAL\n",
            "2 | CARDINAL\n",
            "daily | DATE\n",
            "120ml | ORG\n",
            "2 | CARDINAL\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(reviews.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d5bRxlFXBy2t",
        "outputId": "06057a37-df68-475b-8b6c-7f957b2f2e31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 269777 entries, 0 to 269776\n",
            "Data columns (total 4 columns):\n",
            " #   Column    Non-Null Count   Dtype \n",
            "---  ------    --------------   ----- \n",
            " 0   asin      269777 non-null  object\n",
            " 1   id        269777 non-null  object\n",
            " 2   content_  269777 non-null  object\n",
            " 3   content   269777 non-null  object\n",
            "dtypes: object(4)\n",
            "memory usage: 10.3+ MB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metadata.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wtjk9cHwB0jg",
        "outputId": "5bcc557f-30e0-428b-91c0-cee8ed0be842"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 32488 entries, 0 to 32891\n",
            "Data columns (total 2 columns):\n",
            " #   Column   Non-Null Count  Dtype \n",
            "---  ------   --------------  ----- \n",
            " 0   asin     32488 non-null  object\n",
            " 1   content  32488 non-null  object\n",
            "dtypes: object(2)\n",
            "memory usage: 761.4+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_stemmed(text):\n",
        "  words = [PorterStemmer().stem(word) for word in word_tokenize(text) if word not in stop_words]\n",
        "  return \" \".join(words)\n",
        "\n",
        "def get_word_clouds(df, column):\n",
        "  all = \" \".join(df[column])\n",
        "  plt.figure()\n",
        "  word_cloud = WordCloud(background_color=\"white\").generate(all)\n",
        "  plt.imshow(word_cloud, interpolation=\"bilinear\")\n",
        "  plt.axis(\"off\")\n",
        "  plt.figure()\n",
        "  word_cloud = WordCloud(background_color=\"white\", mask=np.array(Image.open(\"./assets/amazon-logo.png\")), max_font_size=32).generate(all)\n",
        "  plt.imshow(word_cloud, interpolation=\"bilinear\")\n",
        "  plt.axis(\"off\")\n",
        "  plt.show()\n",
        "\n",
        "def get_most_common_words(df, column):\n",
        "  temp = df[column].apply(get_stemmed)\n",
        "  top_words = Counter([word for text in temp for word in text.split()]).most_common(25)\n",
        "  plt.bar(*zip(*top_words))\n",
        "  plt.xticks(rotation=-90)\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "ZC8ZBT8eB5UA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ratings_freq = ratings.value_counts().sort_index()\n",
        "plt.bar(ratings_freq.index, ratings_freq.values)\n",
        "plt.xlabel(\"Ratings\")\n",
        "plt.ylabel(\"Frequency\")\n",
        "plt.title(\"Frequency of Ratings\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "B-K1nkvNCHo4",
        "outputId": "37bce544-569a-4c8c-f8a9-0c2da824733f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.hist(df['overall'], bins=5, color='skyblue', edgecolor='black', alpha=0.7)\n",
        "plt.xlabel('Rating')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of Ratings')\n",
        "plt.xticks(range(1, 6))  # Set x-axis ticks to show all possible ratings (1 to 5)\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.show()\n",
        "\n",
        "# Visualize distribution of ratings using a box plot\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.boxplot(df['overall'], vert=False, patch_artist=True, notch=True, boxprops=dict(facecolor='lightgreen'))\n",
        "plt.xlabel('Rating')\n",
        "plt.title('Box Plot of Ratings')\n",
        "plt.grid(axis='x', linestyle='--', alpha=0.7)\n",
        "plt.show()\n",
        "\n",
        "\n",
        "#majority of ratings are concentrated at the highest level (5), with relatively few ratings at lower levels.\n",
        "#The distribution is right-skewed, indicating a positive bias towards higher ratings.\n",
        "#Outliers are present on the lower end of the scale, but they are relatively few in number compared to the bulk of the ratings."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2R2Hx3DuANQH",
        "outputId": "6ddd422b-d8ac-44c9-a0dc-bac46a796df2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "sns.countplot(x='verified', data=df)\n",
        "plt.title('Verified vs. Unverified Reviews')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "l-CvNL3qA0Bp",
        "outputId": "69ca1807-e7fa-40b3-e6b1-3895840b48cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "sns.countplot(x='verified', data=df)\n",
        "plt.title('Verified vs. Unverified Reviews')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Yk5u8e-PA5Fo",
        "outputId": "9fffc51a-f5df-4e15-f8b3-0d6e4975ee3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from nltk.tokenize import word_tokenize\n",
        "\n",
        "# Sample DataFrame 'df' with 'reviewText' column containing review data\n",
        "# Assuming 'df' is your actual DataFrame\n",
        "# You can skip this step if you already have the DataFrame 'df'\n",
        "# df = pd.DataFrame({'reviewText': ['This is a great product', 'The product was disappointing', 'Good quality item']})\n",
        "\n",
        "# Tokenize the text in the 'reviewText' column\n",
        "df['vote'] = df['reviewText'].apply(lambda x: len(word_tokenize(x)))\n",
        "\n",
        "# Visualize the distribution of review lengths using a histogram\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.hist(df['vote'], bins=20, color='skyblue', edgecolor='black', alpha=0.7)\n",
        "plt.xlabel('Review Length (Number of Words)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of Review Lengths')\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "uQhpkIujBC9Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#3. Text Representation using TF-IDF"
      ],
      "metadata": {
        "id": "1dE6qg-Jy0jr"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_2e8SmYE-dlf"
      },
      "source": [
        "###Reccomendation on the basis of n_gram=(1,3) features\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mYKZFOMGWYaJ"
      },
      "outputs": [],
      "source": [
        "count = CountVectorizer()\n",
        "tfidf_vec = TfidfVectorizer(stop_words='english', analyzer='word', ngram_range=(1,3),max_features=45000)\n",
        "count_matrix1 = tfidf_vec.fit_transform(all_beauty['clean_tags'])\n",
        "count_matrix2 = tfidf_vec.fit_transform(all_beauty_des['clean_tags2'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#4. Dimensionality Reduction Using SVD"
      ],
      "metadata": {
        "id": "e1nU-8tp91IQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x7wresvhYqdm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a91753e-4ec2-4d3f-d437-a256401202a7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.04037434 0.07093908 0.10336026 ... 0.93387517 0.9339204  0.93396561]\n"
          ]
        }
      ],
      "source": [
        "truncatedSVD1=TruncatedSVD(1500)\n",
        "svd_matrix1 = truncatedSVD1.fit_transform(count_matrix1)\n",
        "explained_variance_ratio1 = truncatedSVD1.explained_variance_ratio_\n",
        "cumulative_explained_variance1 = np.cumsum(explained_variance_ratio1)\n",
        "print(cumulative_explained_variance1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "truncatedSVD2=TruncatedSVD(1500)\n",
        "svd_matrix2 = truncatedSVD2.fit_transform(count_matrix2)\n",
        "explained_variance_ratio2 = truncatedSVD2.explained_variance_ratio_\n",
        "cumulative_explained_variance2 = np.cumsum(explained_variance_ratio2)\n",
        "print(cumulative_explained_variance2)"
      ],
      "metadata": {
        "id": "R9e-H1fhv7Qo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "828e25cb-4018-4b70-8677-ed1e8f48fffb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.01273135 0.01718836 0.02152865 ... 0.5688473  0.56898813 0.56912878]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.argmax(cumulative_explained_variance1 >= 0.90) +1)\n",
        "print(np.argmax(cumulative_explained_variance2 >= 0.50) +1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ywre9Y059b6H",
        "outputId": "088e3892-791b-433b-9045-3b6fb104f51c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1015\n",
            "1097\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#5. Calculating Similarity using Cosaine Similarity"
      ],
      "metadata": {
        "id": "b4vzu05k-ADN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-jOsqYtU2u1_"
      },
      "outputs": [],
      "source": [
        "svd_batch_1=svd_matrix1[:15000]\n",
        "cosine_sim1 = cosine_similarity(svd_batch_1, svd_batch_1)#User reviewd products with user reviewd products\n",
        "cosine_sim2 = cosine_similarity(svd_batch_1, svd_matrix2)#User reviewd products with all the beauty category product\n",
        "cosine_sim3 = cosine_similarity(svd_matrix2, svd_matrix2)#All beauty category products with beauty category producst"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BzPMtto63aQP"
      },
      "outputs": [],
      "source": [
        "similarity_df1 = pd.DataFrame(cosine_sim1,index= all_beauty['clean_tags'][:15000].index, columns=all_beauty['clean_tags'][:15000].index) #user-user similarity\n",
        "similarity_df2 = pd.DataFrame(cosine_sim2,index= all_beauty['clean_tags'][:15000].index, columns=all_beauty_des['clean_tags2'].index) #user-all products similarity\n",
        "similarity_df3 = pd.DataFrame(cosine_sim3,index= all_beauty_des['clean_tags2'].index, columns=all_beauty_des['clean_tags2'].index) #all products - all products similarity\n",
        "mapping1 = pd.Series(all_beauty[:15000].index,index = all_beauty['title'][:15000])\n",
        "mapping2 = pd.Series(all_beauty_des.index,index = all_beauty_des['title'])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#6. Reccomendation top_k=25"
      ],
      "metadata": {
        "id": "1JO5iYXS-N2I"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "juy60krt7YHn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ac5c1033-0487-4d15-9a6a-b90585346d6a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Olivia Care Natural Moisturizing Olive Oil Bath Soap - French Mandarin 4 pk'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "all_beauty_des['title'][9076]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_similar_products(all_beauty0, all_beauty_des, similarity_df, mapping, product_index, top_n=25):\n",
        "    prod = all_beauty0['title'][product_index]\n",
        "    prod_index = mapping[prod]\n",
        "\n",
        "    similarity_score = list(enumerate(similarity_df.iloc[prod_index]))\n",
        "    similarity_score = sorted(similarity_score, key=lambda x: x[1], reverse=True)\n",
        "    similarity_score = similarity_score[1:top_n + 1]\n",
        "\n",
        "    prod_indices = [i[0] for i in similarity_score]\n",
        "\n",
        "    similar_titles = all_beauty_des['title'].iloc[prod_indices].tolist()\n",
        "    similar_indices = [comp_data[comp_data['title'] == title].index[0] for title in similar_titles]\n",
        "    ratings = [comp_data['overall'][index] for index in similar_indices]\n",
        "\n",
        "    return similar_titles, ratings, similar_indices\n",
        "\n",
        "\n",
        "product_index = 9076\n",
        "titles0, ratings0, indices0 = get_similar_products(all_beauty, all_beauty_des, similarity_df2, mapping1, product_index)\n",
        "titles, ratings, indices = get_similar_products(all_beauty_des, all_beauty_des, similarity_df3, mapping2, product_index)\n"
      ],
      "metadata": {
        "id": "7_snIa-Wu5gU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d1=np.array([titles0, ratings0, indices0]).T\n",
        "d2=np.array([titles, ratings, indices]).T\n",
        "User_Product_Reccomended_items=pd.DataFrame(d1,columns=['Title','Rating','Index'])\n",
        "Product_product_Reccomended_items = pd.DataFrame(d2,columns=['Title','Rating','Index'])"
      ],
      "metadata": {
        "id": "hplm1cqC4Cig"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "User_Product_Reccomended_items"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "id": "XuuilSBB3_cp",
        "outputId": "50458af8-67a5-4091-bf71-61cdb089a735"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                Title Rating   Index\n",
              "0   Benefit Cosmetics All Purpose Makeup Pencil Sh...    5.0  382523\n",
              "1   YSL YVES SAINT LAURENT Mon Paris 1.2ml/0.04oz ...    5.0  311933\n",
              "2          Gentlemen's Hardware Apothecary Brick Soap    4.0  382514\n",
              "3       Gentlemen's Hardware Apothecary Shaving Cream    5.0  382513\n",
              "4   Australian Gold 8 oz. Lotion with Kona Bronzer...    5.0  382511\n",
              "5   Degree Women MotionSense Antiperspirant Deodor...    1.0  382510\n",
              "6   BIAL 24K Golden Beauty Bar Facial Massager Wat...    1.0  382505\n",
              "7   colgate slim soft charcoal toothbrush (pack of...    5.0  311893\n",
              "8   Yosoo Professional Hair Salon Nylon Barber Cap...    5.0  311877\n",
              "9   5 Pairs Pack Disposable Petal Shape Invisible ...    5.0  382498\n",
              "10  Bhbuy Steel Ear Body Piercing Gun with 100 Set...    3.0  382489\n",
              "11  Adecco LLC Laser Cut Metal Venetian Masquerade...    5.0  311866\n",
              "12  JahyShow Replacement Shaver Head For Philips N...    5.0  311828\n",
              "13  MET-Rx Big 100 Colossal - Chocolate Chip Cooki...    4.0  382467\n",
              "14              Tupperware Rat Tail Hair Styling Comb    5.0  382469\n",
              "15  Santa Maria Novella Creama Per Le Mani Hand Cr...    5.0  382459\n",
              "16  3 Pack- Maybelline Dream Wonder Fluid-Touch Fo...    5.0  382454\n",
              "17  For Samsung Galaxy S7 edge,AutumnFall&reg; Lux...    1.0  311800\n",
              "18  AKOAK 100 Pcs Plant Support Clips for Garden T...    5.0  311777\n",
              "19  MagiDeal Wedding Bridal Pearl Rhinestone Hair ...    5.0  382450\n",
              "20  Scope Outlast Rns Llmnt Size 33.8z Scope Outla...    5.0  382448\n",
              "21  Crest Kid's Crest Cavity Protection Toothpaste...    5.0  382435\n",
              "22  Adecco LLC Great Mixed DIY Size Glitter Rhines...    5.0  311748\n",
              "23        Pani Walewska Gold Perfumed Deodorant, 90ml    5.0  382429\n",
              "24  Too Faced Better Than Sex Mascara Waterproof M...    1.0  382418"
            ],
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              "      <th>Title</th>\n",
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              "      <th>0</th>\n",
              "      <td>Benefit Cosmetics All Purpose Makeup Pencil Sh...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>382523</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>YSL YVES SAINT LAURENT Mon Paris 1.2ml/0.04oz ...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>311933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Gentlemen's Hardware Apothecary Brick Soap</td>\n",
              "      <td>4.0</td>\n",
              "      <td>382514</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Gentlemen's Hardware Apothecary Shaving Cream</td>\n",
              "      <td>5.0</td>\n",
              "      <td>382513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Australian Gold 8 oz. Lotion with Kona Bronzer...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>382511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Degree Women MotionSense Antiperspirant Deodor...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>382510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>BIAL 24K Golden Beauty Bar Facial Massager Wat...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>382505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>colgate slim soft charcoal toothbrush (pack of...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>311893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Yosoo Professional Hair Salon Nylon Barber Cap...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>311877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>5 Pairs Pack Disposable Petal Shape Invisible ...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>382498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Bhbuy Steel Ear Body Piercing Gun with 100 Set...</td>\n",
              "      <td>3.0</td>\n",
              "      <td>382489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Adecco LLC Laser Cut Metal Venetian Masquerade...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>311866</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>JahyShow Replacement Shaver Head For Philips N...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>311828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>MET-Rx Big 100 Colossal - Chocolate Chip Cooki...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>382467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Tupperware Rat Tail Hair Styling Comb</td>\n",
              "      <td>5.0</td>\n",
              "      <td>382469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Santa Maria Novella Creama Per Le Mani Hand Cr...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>382459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>3 Pack- Maybelline Dream Wonder Fluid-Touch Fo...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>382454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>For Samsung Galaxy S7 edge,AutumnFall&amp;reg; Lux...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>311800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>AKOAK 100 Pcs Plant Support Clips for Garden T...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>311777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>MagiDeal Wedding Bridal Pearl Rhinestone Hair ...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>382450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Scope Outlast Rns Llmnt Size 33.8z Scope Outla...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>382448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Crest Kid's Crest Cavity Protection Toothpaste...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>382435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Adecco LLC Great Mixed DIY Size Glitter Rhines...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>311748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Pani Walewska Gold Perfumed Deodorant, 90ml</td>\n",
              "      <td>5.0</td>\n",
              "      <td>382429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Too Faced Better Than Sex Mascara Waterproof M...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>382418</td>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-aceba074-a5d5-4b33-8eda-9479f6411b5f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-aceba074-a5d5-4b33-8eda-9479f6411b5f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-aceba074-a5d5-4b33-8eda-9479f6411b5f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "User_Product_Reccomended_items",
              "summary": "{\n  \"name\": \"User_Product_Reccomended_items\",\n  \"rows\": 25,\n  \"fields\": [\n    {\n      \"column\": \"Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"Yosoo Professional Hair Salon Nylon Barber Cape Hair Cutting Gown with Snap Closure for Kids and Adults - 59&quot; x 47&quot; Black\",\n          \"3 Pack- Maybelline Dream Wonder Fluid-Touch Foundation #95 Coconut\",\n          \"Benefit Cosmetics All Purpose Makeup Pencil Sharpener - Pink - For All Size Pencils\"\n        ],\n        \"num_unique_values\": 25,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"4.0\",\n          \"3.0\",\n          \"5.0\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Index\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"311877\",\n          \"382454\",\n          \"382523\"\n        ],\n        \"num_unique_values\": 25,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "source": [
        "from matplotlib import pyplot as plt\n",
        "Product_product_Reccomended_items['Rating'].plot(kind='hist', bins=20, title='Rating of predicted products')\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ],
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "LPTy1IY__y8f",
        "outputId": "66a1a91c-f29f-4aa8-eaec-267abcbe3680"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Product_product_Reccomended_items"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "id": "k54Ongsa5mcW",
        "outputId": "a6f201ee-634a-4beb-ff51-5f59d3b9634c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                Title Rating   Index\n",
              "0       Olivia Care Liquid Hand Soap Mandarin, 18.5oz    5.0  317157\n",
              "1   Olivia Care Liquid Hand Soap, Verbena, 18.5-Ou...    5.0  317162\n",
              "2   Olivia Care Liquid Hand Soap , Lavender, 18.5-...    5.0  317150\n",
              "3   Olivia Care French Natural Olive Oil Bar Soap ...    5.0  359461\n",
              "4   Natural Olive Nablus Natural Olive Oil Soap 10...    5.0  332613\n",
              "5   Bariani Olive Oil Body Soap &amp; Shampoo -(12oz)    5.0  330131\n",
              "6   Bar Soap 100% Virgin Olive Oil Face &amp; Body...    1.0   33569\n",
              "7                        Proclaim Natural 7 Olive Oil    5.0  125701\n",
              "8         Blood Orange Olive Oil Bar Soap by 80 Acres    5.0  321637\n",
              "9           Olivina Bath Soap, Classic Olive, 8 Ounce    5.0  321970\n",
              "10          Olivina Bath Soap, Classic Olive, 4 Ounce    5.0  122058\n",
              "11  Savonnerie Rampal Latour Savon Extra Marseille...    4.0  358153\n",
              "12            Olivia Glossing Hair Cream - 130ml Tube    4.0  322892\n",
              "13  Oatmeal Soap - Handcrafted- Natural Shea Butte...    5.0  316574\n",
              "14  Softee Signature Exra Virgin Olive oil Hair an...    1.0  361943\n",
              "15  Greenwich Bay CUCUMBER OLIVE OIL Spa Soap, Enr...    5.0  349892\n",
              "16  La Vie En Rose - Natural Bar Soap, Rose Gerani...    5.0  355535\n",
              "17  Vinolia Cold Cream Bath Soap (10 Pack) 10 soap...    5.0  322643\n",
              "18  Health &amp; Beauty H&amp;B Dead Sea Olive Oil...    5.0  349466\n",
              "19                 Powerful Cream Olive Oil and Honey    5.0  362925\n",
              "20                                    DW Natural soap    5.0  348846\n",
              "21  Brazilian Mud Soap (SET of 4) - Handmade Soap ...    5.0  212092\n",
              "22  Simpatico Shea Butter Bar Soap 8 Oz. - Ambergr...    5.0  356288\n",
              "23  2 Bar Set Venezia Soapworks Oatmeal Moisturizi...    5.0  325137\n",
              "24  Yes to Blueberries Shea Liquid Hand Soap 12 fl...    5.0  352719"
            ],
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              "      <th>0</th>\n",
              "      <td>Olivia Care Liquid Hand Soap Mandarin, 18.5oz</td>\n",
              "      <td>5.0</td>\n",
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              "      <td>Olivia Care Liquid Hand Soap , Lavender, 18.5-...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Olivia Care French Natural Olive Oil Bar Soap ...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>359461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Natural Olive Nablus Natural Olive Oil Soap 10...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>332613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Bariani Olive Oil Body Soap &amp;amp; Shampoo -(12oz)</td>\n",
              "      <td>5.0</td>\n",
              "      <td>330131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Bar Soap 100% Virgin Olive Oil Face &amp;amp; Body...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>33569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Proclaim Natural 7 Olive Oil</td>\n",
              "      <td>5.0</td>\n",
              "      <td>125701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Blood Orange Olive Oil Bar Soap by 80 Acres</td>\n",
              "      <td>5.0</td>\n",
              "      <td>321637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Olivina Bath Soap, Classic Olive, 8 Ounce</td>\n",
              "      <td>5.0</td>\n",
              "      <td>321970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Olivina Bath Soap, Classic Olive, 4 Ounce</td>\n",
              "      <td>5.0</td>\n",
              "      <td>122058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Savonnerie Rampal Latour Savon Extra Marseille...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>358153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Olivia Glossing Hair Cream - 130ml Tube</td>\n",
              "      <td>4.0</td>\n",
              "      <td>322892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Oatmeal Soap - Handcrafted- Natural Shea Butte...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>316574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Softee Signature Exra Virgin Olive oil Hair an...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>361943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Greenwich Bay CUCUMBER OLIVE OIL Spa Soap, Enr...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>349892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>La Vie En Rose - Natural Bar Soap, Rose Gerani...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>355535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Vinolia Cold Cream Bath Soap (10 Pack) 10 soap...</td>\n",
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              "      <td>322643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Health &amp;amp; Beauty H&amp;amp;B Dead Sea Olive Oil...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>349466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Powerful Cream Olive Oil and Honey</td>\n",
              "      <td>5.0</td>\n",
              "      <td>362925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>DW Natural soap</td>\n",
              "      <td>5.0</td>\n",
              "      <td>348846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Brazilian Mud Soap (SET of 4) - Handmade Soap ...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>212092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Simpatico Shea Butter Bar Soap 8 Oz. - Ambergr...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>356288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2 Bar Set Venezia Soapworks Oatmeal Moisturizi...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>325137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Yes to Blueberries Shea Liquid Hand Soap 12 fl...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>352719</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "Product_product_Reccomended_items",
              "summary": "{\n  \"name\": \"Product_product_Reccomended_items\",\n  \"rows\": 25,\n  \"fields\": [\n    {\n      \"column\": \"Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"Blood Orange Olive Oil Bar Soap by 80 Acres\",\n          \"La Vie En Rose - Natural Bar Soap, Rose Geranium Essential Oil, 7 Oz\",\n          \"Olivia Care Liquid Hand Soap Mandarin, 18.5oz\"\n        ],\n        \"num_unique_values\": 25,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"5.0\",\n          \"1.0\",\n          \"4.0\"\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Index\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"321637\",\n          \"355535\",\n          \"317157\"\n        ],\n        \"num_unique_values\": 25,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "source": [
        "\n",
        "from matplotlib import pyplot as plt\n",
        "Product_product_Reccomended_items['Rating'].plot(kind='hist', bins=20, title='Rating of predicted products')\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ],
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "9NPTh-bT_fz5",
        "outputId": "5afb4b5b-6316-442d-febc-dfb31ffa7e0a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#7. Evaluation"
      ],
      "metadata": {
        "id": "soH6Ygx26iea"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###We have taken a very naive approach to evaluation by using the formula - where,\n",
        "*number of successfull reccomendations* = no of the reccomended product's rating that are greater than 3 (either 4 or 5) i.e positive product recommendations.   \n",
        "*Total no of reccomendations* = top k products reccomended\n",
        "\n",
        "\n",
        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)"
      ],
      "metadata": {
        "id": "Ohe-9pef6noQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def calculate_success_rate(recommendations_df):\n",
        "    recommendations_df['Rating'] = pd.to_numeric(recommendations_df['Rating'], errors='coerce')\n",
        "    # Droping rows with NaN values in the 'Rating' column\n",
        "    recommendations_df.dropna(subset=['Rating'], inplace=True)\n",
        "    # recommended products with ratings greater than 3\n",
        "    successful_recommendations = recommendations_df[recommendations_df['Rating'] > 3]\n",
        "    #number of successful recommendations\n",
        "    num_successful_recommendations = successful_recommendations.shape[0]\n",
        "    #total number of recommendations made\n",
        "    total_recommendations = recommendations_df.shape[0]\n",
        "    #success rate\n",
        "    success_rate = num_successful_recommendations / total_recommendations\n",
        "    return success_rate\n",
        "success_rate = calculate_success_rate(Product_product_Reccomended_items)\n",
        "success_rate2 = calculate_success_rate(User_Product_Reccomended_items)\n",
        "print(\"Success rate of recommendations for user_reviewd_products and all_beauty_product comparision:\", success_rate)\n",
        "print(\"Success rate of recommendations for all_beauty_product and all_beauty_product comparision:\", success_rate2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yMYhCfi17rw4",
        "outputId": "33a041c0-137a-4985-8fcf-9923ef31052f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Success rate of recommendations for user_reviewd_products and all_beauty_product comparision: 0.92\n",
            "Success rate of recommendations for all_beauty_product and all_beauty_product comparision: 0.8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#8. Observations"
      ],
      "metadata": {
        "id": "5YKV3Hc1-Vs4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "ljCXqMOD-izI"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K8xhAIfg-pGM"
      },
      "source": [
        "###Reccomendation on the basis of n_gram=(2,2) features\n",
        "We observed that the 1500 features could explain only 80% of the data in case of user profile vector and could explain only 60% of the all beauty products data. When we took n_gram=(2,2) (bigram) our 1500 features could explain only 50% of the all beauty products data, therfore the same features could explain more data when taken n_gram=(1,3)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Extra_code(Not relavant to results)"
      ],
      "metadata": {
        "id": "PjBTakJz-bi4"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3MP31tQC7qPP"
      },
      "outputs": [],
      "source": [
        "# count = CountVectorizer()\n",
        "# tfidf_vec = TfidfVectorizer(stop_words='english', analyzer='word', ngram_range=(2,2),max_features=45000)\n",
        "# count_matrix1 = tfidf_vec.fit_transform(all_beauty['clean_tags'])\n",
        "# count_matrix2 = tfidf_vec.fit_transform(all_beauty_des['clean_tags2'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u3AeY5PGAY4s"
      },
      "outputs": [],
      "source": [
        "# truncatedSVD1=TruncatedSVD(1500)\n",
        "# svd_matrix1 = truncatedSVD1.fit_transform(count_matrix1)\n",
        "# explained_variance_ratio1 = truncatedSVD1.explained_variance_ratio_\n",
        "# cumulative_explained_variance1 = np.cumsum(explained_variance_ratio1)\n",
        "# print(cumulative_explained_variance1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IFQz4ac0_aGF"
      },
      "outputs": [],
      "source": [
        "# truncatedSVD2=TruncatedSVD(1500)\n",
        "# svd_matrix2 = truncatedSVD2.fit_transform(count_matrix2)\n",
        "# explained_variance_ratio2 = truncatedSVD2.explained_variance_ratio_\n",
        "# cumulative_explained_variance2 = np.cumsum(explained_variance_ratio2)\n",
        "# print(cumulative_explained_variance2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1ehnmRry_kHJ"
      },
      "outputs": [],
      "source": [
        "# print(np.argmax(cumulative_explained_variance1 >= 0.90) +1)\n",
        "# print(np.argmax(cumulative_explained_variance2 >= 0.60) +1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C9XGC9-p-yEX"
      },
      "outputs": [],
      "source": [
        "# svd_batch_1=svd_matrix1[:15000]\n",
        "# cosine_sim1 = cosine_similarity(svd_batch_1, svd_batch_1)\n",
        "# cosine_sim2 = cosine_similarity(svd_batch_1, svd_matrix2)\n",
        "# cosine_sim3 = cosine_similarity(svd_matrix2, svd_matrix2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "loLRU5PDNPmS"
      },
      "outputs": [],
      "source": [
        "# all_beauty_des.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XeI-Ur5U_Pg1"
      },
      "outputs": [],
      "source": [
        "# similarity_df1 = pd.DataFrame(cosine_sim1,index= all_beauty['clean_tags'][:15000].index, columns=all_beauty['clean_tags'][:15000].index)\n",
        "# similarity_df2 = pd.DataFrame(cosine_sim2,index= all_beauty['clean_tags'][:15000].index, columns=all_beauty_des['clean_tags2'].index)\n",
        "# similarity_df3 = pd.DataFrame(cosine_sim3,index= all_beauty_des['clean_tags2'].index, columns=all_beauty_des['clean_tags2'].index)\n",
        "# mapping1 = pd.Series(all_beauty[:15000].index,index = all_beauty['title'][:15000])\n",
        "# mapping2 = pd.Series(all_beauty_des.index,index = all_beauty_des['title'])\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "sq806VLOAKsQ",
        "1dE6qg-Jy0jr",
        "e1nU-8tp91IQ",
        "b4vzu05k-ADN",
        "1JO5iYXS-N2I",
        "Ohe-9pef6noQ",
        "PjBTakJz-bi4"
      ]
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}