notebooks/content_based_filtering_recommendation_system.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import pandas and numpy\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Uniq Id</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Brand Name</th>\n",
       "      <th>Asin</th>\n",
       "      <th>Category</th>\n",
       "      <th>Upc Ean Code</th>\n",
       "      <th>List Price</th>\n",
       "      <th>Selling Price</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Model Number</th>\n",
       "      <th>...</th>\n",
       "      <th>Product Url</th>\n",
       "      <th>Stock</th>\n",
       "      <th>Product Details</th>\n",
       "      <th>Dimensions</th>\n",
       "      <th>Color</th>\n",
       "      <th>Ingredients</th>\n",
       "      <th>Direction To Use</th>\n",
       "      <th>Is Amazon Seller</th>\n",
       "      <th>Size Quantity Variant</th>\n",
       "      <th>Product Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4c69b61db1fc16e7013b43fc926e502d</td>\n",
       "      <td>DB Longboards CoreFlex Crossbow 41\" Bamboo Fib...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sports &amp; Outdoors | Outdoor Recreation | Skate...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$237.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.amazon.com/DB-Longboards-CoreFlex-...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>66d49bbed043f5be260fa9f7fbff5957</td>\n",
       "      <td>Electronic Snap Circuits Mini Kits Classpack, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Toys &amp; Games | Learning &amp; Education | Science ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$99.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55324</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.amazon.com/Electronic-Circuits-Cla...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2c55cae269aebf53838484b0d7dd931a</td>\n",
       "      <td>3Doodler Create Flexy 3D Printing Filament Ref...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Toys &amp; Games | Arts &amp; Crafts | Craft Kits</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$34.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.amazon.com/3Doodler-Plastic-Innova...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18018b6bc416dab347b1b7db79994afa</td>\n",
       "      <td>Guillow Airplane Design Studio with Travel Cas...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Toys &amp; Games | Hobbies | Models &amp; Model Kits |...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$28.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>142</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.amazon.com/Guillow-Airplane-Design...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e04b990e95bf73bbe6a3fa09785d7cd0</td>\n",
       "      <td>Woodstock- Collage 500 pc Puzzle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Toys &amp; Games | Puzzles | Jigsaw Puzzles</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$17.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62151</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.amazon.com/Woodstock-Collage-500-p...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Uniq Id  \\\n",
       "0  4c69b61db1fc16e7013b43fc926e502d   \n",
       "1  66d49bbed043f5be260fa9f7fbff5957   \n",
       "2  2c55cae269aebf53838484b0d7dd931a   \n",
       "3  18018b6bc416dab347b1b7db79994afa   \n",
       "4  e04b990e95bf73bbe6a3fa09785d7cd0   \n",
       "\n",
       "                                        Product Name  Brand Name  Asin  \\\n",
       "0  DB Longboards CoreFlex Crossbow 41\" Bamboo Fib...         NaN   NaN   \n",
       "1  Electronic Snap Circuits Mini Kits Classpack, ...         NaN   NaN   \n",
       "2  3Doodler Create Flexy 3D Printing Filament Ref...         NaN   NaN   \n",
       "3  Guillow Airplane Design Studio with Travel Cas...         NaN   NaN   \n",
       "4                   Woodstock- Collage 500 pc Puzzle         NaN   NaN   \n",
       "\n",
       "                                            Category Upc Ean Code  List Price  \\\n",
       "0  Sports & Outdoors | Outdoor Recreation | Skate...          NaN         NaN   \n",
       "1  Toys & Games | Learning & Education | Science ...          NaN         NaN   \n",
       "2          Toys & Games | Arts & Crafts | Craft Kits          NaN         NaN   \n",
       "3  Toys & Games | Hobbies | Models & Model Kits |...          NaN         NaN   \n",
       "4            Toys & Games | Puzzles | Jigsaw Puzzles          NaN         NaN   \n",
       "\n",
       "  Selling Price  Quantity Model Number  ...  \\\n",
       "0       $237.68       NaN          NaN  ...   \n",
       "1        $99.95       NaN        55324  ...   \n",
       "2        $34.99       NaN          NaN  ...   \n",
       "3        $28.91       NaN          142  ...   \n",
       "4        $17.49       NaN        62151  ...   \n",
       "\n",
       "                                         Product Url Stock Product Details  \\\n",
       "0  https://www.amazon.com/DB-Longboards-CoreFlex-...   NaN             NaN   \n",
       "1  https://www.amazon.com/Electronic-Circuits-Cla...   NaN             NaN   \n",
       "2  https://www.amazon.com/3Doodler-Plastic-Innova...   NaN             NaN   \n",
       "3  https://www.amazon.com/Guillow-Airplane-Design...   NaN             NaN   \n",
       "4  https://www.amazon.com/Woodstock-Collage-500-p...   NaN             NaN   \n",
       "\n",
       "  Dimensions Color Ingredients Direction To Use  Is Amazon Seller  \\\n",
       "0        NaN   NaN         NaN              NaN                 Y   \n",
       "1        NaN   NaN         NaN              NaN                 Y   \n",
       "2        NaN   NaN         NaN              NaN                 Y   \n",
       "3        NaN   NaN         NaN              NaN                 Y   \n",
       "4        NaN   NaN         NaN              NaN                 Y   \n",
       "\n",
       "  Size Quantity Variant  Product Description  \n",
       "0                   NaN                  NaN  \n",
       "1                   NaN                  NaN  \n",
       "2                   NaN                  NaN  \n",
       "3                   NaN                  NaN  \n",
       "4                   NaN                  NaN  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#upload data set to colab from desktop\n",
    "#once uploaded, load data set \n",
    "\n",
    "dataset = pd.read_csv(\"amazon products.csv\")\n",
    "dataset.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10002, 28)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#wrangle-rows by columns\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this dataset, there are a total of 10002 rows and 28 columns(variables)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10002 entries, 0 to 10001\n",
      "Data columns (total 28 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   Uniq Id                10002 non-null  object \n",
      " 1   Product Name           10002 non-null  object \n",
      " 2   Brand Name             0 non-null      float64\n",
      " 3   Asin                   0 non-null      float64\n",
      " 4   Category               9172 non-null   object \n",
      " 5   Upc Ean Code           34 non-null     object \n",
      " 6   List Price             0 non-null      float64\n",
      " 7   Selling Price          9895 non-null   object \n",
      " 8   Quantity               0 non-null      float64\n",
      " 9   Model Number           8232 non-null   object \n",
      " 10  About Product          9729 non-null   object \n",
      " 11  Product Specification  8370 non-null   object \n",
      " 12  Technical Details      9212 non-null   object \n",
      " 13  Shipping Weight        8864 non-null   object \n",
      " 14  Product Dimensions     479 non-null    object \n",
      " 15  Image                  10002 non-null  object \n",
      " 16  Variants               2478 non-null   object \n",
      " 17  Sku                    0 non-null      float64\n",
      " 18  Product Url            10002 non-null  object \n",
      " 19  Stock                  0 non-null      float64\n",
      " 20  Product Details        0 non-null      float64\n",
      " 21  Dimensions             0 non-null      float64\n",
      " 22  Color                  0 non-null      float64\n",
      " 23  Ingredients            0 non-null      float64\n",
      " 24  Direction To Use       0 non-null      float64\n",
      " 25  Is Amazon Seller       10002 non-null  object \n",
      " 26  Size Quantity Variant  0 non-null      float64\n",
      " 27  Product Description    0 non-null      float64\n",
      "dtypes: float64(13), object(15)\n",
      "memory usage: 2.1+ MB\n"
     ]
    }
   ],
   "source": [
    "#Check variable characteristics\n",
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        False\n",
       "1        False\n",
       "2        False\n",
       "3        False\n",
       "4        False\n",
       "         ...  \n",
       "9997     False\n",
       "9998     False\n",
       "9999     False\n",
       "10000    False\n",
       "10001    False\n",
       "Length: 10002, dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for duplicates\n",
    "dataset.duplicated()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All 10002 rows are unique. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping redundant columns\n",
    "cols = [0,2,3,5,6,8,9,12,14,15,16,17,18,19,20,21,22,23,24,25,26,27]\n",
    "dataset.drop(dataset.columns[cols], axis =1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Columns 2,3,6,8,17,19-24,26,27 only contains null values (not useful).\n",
    "Columns 1,5,9,12,14-16,18,25 are not helpful with achieving our objectives. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7216 entries, 0 to 10001\n",
      "Data columns (total 6 columns):\n",
      " #   Column                 Non-Null Count  Dtype \n",
      "---  ------                 --------------  ----- \n",
      " 0   Product Name           7216 non-null   object\n",
      " 1   Category               7216 non-null   object\n",
      " 2   Selling Price          7216 non-null   object\n",
      " 3   About Product          7216 non-null   object\n",
      " 4   Product Specification  7216 non-null   object\n",
      " 5   Shipping Weight        7216 non-null   object\n",
      "dtypes: object(6)\n",
      "memory usage: 394.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# dropping null values to avoid errors \n",
    "dataset.dropna(inplace = True)\n",
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# new data frame with split value columns. We use n = 3 to get a maximum of 3+1 columns\n",
    "new = dataset[\"Category\"].str.split(\"|\", n = 3, expand = True)\n",
    "  \n",
    "# making the first category called Main Category\n",
    "dataset[\"Main Category\"]= new[0] \n",
    "  \n",
    "# making the second category called sub_category \n",
    "dataset[\"Sub-Category\"]= new[1]\n",
    "\n",
    "# making the third category called side_category \n",
    "dataset[\"Side Category\"]= new[2]\n",
    "\n",
    "# making the last column consist of the remaining categories\n",
    "dataset[\"Other Categories\"]= new[3]\n",
    "\n",
    "# Dropping old category columns and the remaining categories \n",
    "dataset.drop(columns =[\"Category\", \"Other Categories\"], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Selling Price</th>\n",
       "      <th>About Product</th>\n",
       "      <th>Product Specification</th>\n",
       "      <th>Shipping Weight</th>\n",
       "      <th>Main Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Side Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DB Longboards CoreFlex Crossbow 41\" Bamboo Fib...</td>\n",
       "      <td>$237.68</td>\n",
       "      <td>Make sure this fits by entering your model num...</td>\n",
       "      <td>Shipping Weight: 10.7 pounds (View shipping ra...</td>\n",
       "      <td>10.7 pounds</td>\n",
       "      <td>Sports &amp; Outdoors</td>\n",
       "      <td>Outdoor Recreation</td>\n",
       "      <td>Skates, Skateboards &amp; Scooters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Electronic Snap Circuits Mini Kits Classpack, ...</td>\n",
       "      <td>$99.95</td>\n",
       "      <td>Make sure this fits by entering your model num...</td>\n",
       "      <td>Product Dimensions:         14.7 x 11.1 x 10.2...</td>\n",
       "      <td>4 pounds</td>\n",
       "      <td>Toys &amp; Games</td>\n",
       "      <td>Learning &amp; Education</td>\n",
       "      <td>Science Kits &amp; Toys</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3Doodler Create Flexy 3D Printing Filament Ref...</td>\n",
       "      <td>$34.99</td>\n",
       "      <td>Make sure this fits by entering your model num...</td>\n",
       "      <td>ProductDimensions:10.3x3.4x0.8inches|ItemWeigh...</td>\n",
       "      <td>12.8 ounces</td>\n",
       "      <td>Toys &amp; Games</td>\n",
       "      <td>Arts &amp; Crafts</td>\n",
       "      <td>Craft Kits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Guillow Airplane Design Studio with Travel Cas...</td>\n",
       "      <td>$28.91</td>\n",
       "      <td>Make 8 different Planes at one time. | Experim...</td>\n",
       "      <td>ProductDimensions:3.5x6.2x13inches|ItemWeight:...</td>\n",
       "      <td>13.4 ounces</td>\n",
       "      <td>Toys &amp; Games</td>\n",
       "      <td>Hobbies</td>\n",
       "      <td>Models &amp; Model Kits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Woodstock- Collage 500 pc Puzzle</td>\n",
       "      <td>$17.49</td>\n",
       "      <td>Make sure this fits by entering your model num...</td>\n",
       "      <td>ProductDimensions:1.9x8x10inches|ItemWeight:13...</td>\n",
       "      <td>13.4 ounces</td>\n",
       "      <td>Toys &amp; Games</td>\n",
       "      <td>Puzzles</td>\n",
       "      <td>Jigsaw Puzzles</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Product Name Selling Price  \\\n",
       "0  DB Longboards CoreFlex Crossbow 41\" Bamboo Fib...       $237.68   \n",
       "1  Electronic Snap Circuits Mini Kits Classpack, ...        $99.95   \n",
       "2  3Doodler Create Flexy 3D Printing Filament Ref...        $34.99   \n",
       "3  Guillow Airplane Design Studio with Travel Cas...        $28.91   \n",
       "4                   Woodstock- Collage 500 pc Puzzle        $17.49   \n",
       "\n",
       "                                       About Product  \\\n",
       "0  Make sure this fits by entering your model num...   \n",
       "1  Make sure this fits by entering your model num...   \n",
       "2  Make sure this fits by entering your model num...   \n",
       "3  Make 8 different Planes at one time. | Experim...   \n",
       "4  Make sure this fits by entering your model num...   \n",
       "\n",
       "                               Product Specification Shipping Weight  \\\n",
       "0  Shipping Weight: 10.7 pounds (View shipping ra...     10.7 pounds   \n",
       "1  Product Dimensions:         14.7 x 11.1 x 10.2...        4 pounds   \n",
       "2  ProductDimensions:10.3x3.4x0.8inches|ItemWeigh...     12.8 ounces   \n",
       "3  ProductDimensions:3.5x6.2x13inches|ItemWeight:...     13.4 ounces   \n",
       "4  ProductDimensions:1.9x8x10inches|ItemWeight:13...     13.4 ounces   \n",
       "\n",
       "        Main Category            Sub-Category  \\\n",
       "0  Sports & Outdoors      Outdoor Recreation    \n",
       "1       Toys & Games    Learning & Education    \n",
       "2       Toys & Games           Arts & Crafts    \n",
       "3       Toys & Games                 Hobbies    \n",
       "4       Toys & Games                 Puzzles    \n",
       "\n",
       "                      Side Category  \n",
       "0   Skates, Skateboards & Scooters   \n",
       "1               Science Kits & Toys  \n",
       "2                        Craft Kits  \n",
       "3              Models & Model Kits   \n",
       "4                    Jigsaw Puzzles  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Revised dataset to conduct EDA\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Selling Price($)</th>\n",
       "      <th>About Product</th>\n",
       "      <th>Product Specification</th>\n",
       "      <th>Shipping Weight(Pounds)</th>\n",
       "      <th>Main Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Side Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DB Longboards CoreFlex Crossbow 41\" Bamboo Fib...</td>\n",
       "      <td>237.68</td>\n",
       "      <td>Make sure this fits by entering your model num...</td>\n",
       "      <td>Shipping Weight: 10.7 pounds (View shipping ra...</td>\n",
       "      <td>10.7</td>\n",
       "      <td>Sports &amp; Outdoors</td>\n",
       "      <td>Outdoor Recreation</td>\n",
       "      <td>Skates, Skateboards &amp; Scooters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Electronic Snap Circuits Mini Kits Classpack, ...</td>\n",
       "      <td>99.95</td>\n",
       "      <td>Make sure this fits by entering your model num...</td>\n",
       "      <td>Product Dimensions:         14.7 x 11.1 x 10.2...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Toys &amp; Games</td>\n",
       "      <td>Learning &amp; Education</td>\n",
       "      <td>Science Kits &amp; Toys</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3Doodler Create Flexy 3D Printing Filament Ref...</td>\n",
       "      <td>34.99</td>\n",
       "      <td>Make sure this fits by entering your model num...</td>\n",
       "      <td>ProductDimensions:10.3x3.4x0.8inches|ItemWeigh...</td>\n",
       "      <td>12.8</td>\n",
       "      <td>Toys &amp; Games</td>\n",
       "      <td>Arts &amp; Crafts</td>\n",
       "      <td>Craft Kits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Guillow Airplane Design Studio with Travel Cas...</td>\n",
       "      <td>28.91</td>\n",
       "      <td>Make 8 different Planes at one time. | Experim...</td>\n",
       "      <td>ProductDimensions:3.5x6.2x13inches|ItemWeight:...</td>\n",
       "      <td>13.4</td>\n",
       "      <td>Toys &amp; Games</td>\n",
       "      <td>Hobbies</td>\n",
       "      <td>Models &amp; Model Kits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Woodstock- Collage 500 pc Puzzle</td>\n",
       "      <td>17.49</td>\n",
       "      <td>Make sure this fits by entering your model num...</td>\n",
       "      <td>ProductDimensions:1.9x8x10inches|ItemWeight:13...</td>\n",
       "      <td>13.4</td>\n",
       "      <td>Toys &amp; Games</td>\n",
       "      <td>Puzzles</td>\n",
       "      <td>Jigsaw Puzzles</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Product Name  Selling Price($)  \\\n",
       "0  DB Longboards CoreFlex Crossbow 41\" Bamboo Fib...            237.68   \n",
       "1  Electronic Snap Circuits Mini Kits Classpack, ...             99.95   \n",
       "2  3Doodler Create Flexy 3D Printing Filament Ref...             34.99   \n",
       "3  Guillow Airplane Design Studio with Travel Cas...             28.91   \n",
       "4                   Woodstock- Collage 500 pc Puzzle             17.49   \n",
       "\n",
       "                                       About Product  \\\n",
       "0  Make sure this fits by entering your model num...   \n",
       "1  Make sure this fits by entering your model num...   \n",
       "2  Make sure this fits by entering your model num...   \n",
       "3  Make 8 different Planes at one time. | Experim...   \n",
       "4  Make sure this fits by entering your model num...   \n",
       "\n",
       "                               Product Specification  Shipping Weight(Pounds)  \\\n",
       "0  Shipping Weight: 10.7 pounds (View shipping ra...                     10.7   \n",
       "1  Product Dimensions:         14.7 x 11.1 x 10.2...                      4.0   \n",
       "2  ProductDimensions:10.3x3.4x0.8inches|ItemWeigh...                     12.8   \n",
       "3  ProductDimensions:3.5x6.2x13inches|ItemWeight:...                     13.4   \n",
       "4  ProductDimensions:1.9x8x10inches|ItemWeight:13...                     13.4   \n",
       "\n",
       "        Main Category            Sub-Category  \\\n",
       "0  Sports & Outdoors      Outdoor Recreation    \n",
       "1       Toys & Games    Learning & Education    \n",
       "2       Toys & Games           Arts & Crafts    \n",
       "3       Toys & Games                 Hobbies    \n",
       "4       Toys & Games                 Puzzles    \n",
       "\n",
       "                      Side Category  \n",
       "0   Skates, Skateboards & Scooters   \n",
       "1               Science Kits & Toys  \n",
       "2                        Craft Kits  \n",
       "3              Models & Model Kits   \n",
       "4                    Jigsaw Puzzles  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.rename(columns = {'Uniq Id':'Id','Shipping Weight':'Shipping Weight(Pounds)', 'Selling Price':'Selling Price($)'}, inplace = True)\n",
    "\n",
    "dataset['Shipping Weight(Pounds)'] = dataset['Shipping Weight(Pounds)'].str.strip('ounces')\n",
    "dataset['Shipping Weight(Pounds)'] = dataset['Shipping Weight(Pounds)'].str.strip('pounds').astype(float)\n",
    "dataset['Selling Price($)'] = dataset['Selling Price($)'].str.replace('$', '').astype(float)\n",
    "\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7216 entries, 0 to 10001\n",
      "Data columns (total 8 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   Product Name             7216 non-null   object \n",
      " 1   Selling Price($)         7216 non-null   float64\n",
      " 2   About Product            7216 non-null   object \n",
      " 3   Product Specification    7216 non-null   object \n",
      " 4   Shipping Weight(Pounds)  7216 non-null   float64\n",
      " 5   Main Category            7216 non-null   object \n",
      " 6   Sub-Category             7216 non-null   object \n",
      " 7   Side Category            6231 non-null   object \n",
      "dtypes: float64(2), object(6)\n",
      "memory usage: 507.4+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selling Price($) & Shipping Weight(Pounds) have been converted to Float from Object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start of EDA\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sports & Outdoors ', 'Toys & Games ',\n",
       "       'Clothing, Shoes & Jewelry ', 'Home & Kitchen ', 'Pet Supplies ',\n",
       "       'Arts, Crafts & Sewing ', 'Electronics ', 'Patio, Lawn & Garden ',\n",
       "       'Tools & Home Improvement ', 'Office Products ', 'Baby Products ',\n",
       "       'Grocery & Gourmet Food ', 'Video Games ',\n",
       "       'Remote & App Controlled Vehicle Parts ', 'Health & Household ',\n",
       "       'Industrial & Scientific ', 'Beauty & Personal Care ',\n",
       "       'Automotive ', 'Remote & App Controlled Vehicles & Parts ',\n",
       "       'Musical Instruments ', 'Hobbies ', 'Movies & TV ',\n",
       "       'Cell Phones & Accessories '], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding out the unique main categories\n",
    "dataset[\"Main Category\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count of products')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#data visualisation\n",
    "\n",
    "#lets look at patterns in terms of Product Category popularity\n",
    "#generate count for \"Main Category\"\n",
    "\n",
    "#Top 10 barplot of categories\n",
    "order = dataset['Main Category'].value_counts()[:10].index\n",
    "sns.countplot(y='Main Category', data=dataset, order=order)\n",
    "plt.title(\"Product count by category\")\n",
    "plt.xlabel(\"Main category\")\n",
    "plt.ylabel(\"Count of products\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The top 5 Category: \n",
    "1.Toys & Games\n",
    "2.Home & Kitchen\n",
    "3.Sports & Outdoors\n",
    "4.Clothing, Shoes & Jewelry\n",
    "5.Arts, Crafts & Sewing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of the Prices in Toys & Games Category')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#generate boxplot to understand the distribution  \n",
    "toys = dataset[dataset[\"Main Category\"] == 'Toys & Games ']\n",
    "sns.boxplot(data = toys, x='Main Category', y='Selling Price($)', showfliers=False)\n",
    "plt.title(\"Distribution of the Prices in Toys & Games Category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Relationship between Price & Shipping Weight in Toys & Games category')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#generate scatterplot to understand the relationship \n",
    "sns.scatterplot(data=toys, x=\"Selling Price($)\", y=\"Shipping Weight(Pounds)\")\n",
    "plt.title(\"Relationship between Price & Shipping Weight in Toys & Games category\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We cant really draw a conclusion from the lower shipping weight. but for heavier items some of them seems to be positively related to the selling pricer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of the Prices in Home & Kitchen Category')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#generate boxplot to understand the distribution  \n",
    "home = dataset[dataset[\"Main Category\"] == 'Home & Kitchen ']\n",
    "sns.boxplot(data = home, x='Main Category', y='Selling Price($)', showfliers=False)\n",
    "plt.title(\"Distribution of the Prices in Home & Kitchen Category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Relationship between Price & Shipping Weight in Home & Kitchen Category')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#generate scatterplot to understand the relationship\n",
    "sns.scatterplot(data=home, x=\"Selling Price($)\", y=\"Shipping Weight(Pounds)\")\n",
    "plt.title(\"Relationship between Price & Shipping Weight in Home & Kitchen Category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of the Prices in Sports & Outdoors Category')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#generate boxplot to understand the distribution  \n",
    "sports = dataset[dataset[\"Main Category\"] == 'Sports & Outdoors ']\n",
    "sns.boxplot(data = sports, x='Main Category', y='Selling Price($)', showfliers=False)\n",
    "plt.title(\"Distribution of the Prices in Sports & Outdoors Category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Relationship between Price & Shipping Weight in Sports & Outdoors Category')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#generate scatterplot to understand the relationship\n",
    "sns.scatterplot(data=sports, x=\"Selling Price($)\", y=\"Shipping Weight(Pounds)\")\n",
    "plt.title(\"Relationship between Price & Shipping Weight in Sports & Outdoors Category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of the Prices in Clothing, Shoes & Jewelry Category')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#generate boxplot to understand the distribution  \n",
    "csj = dataset[dataset[\"Main Category\"] == 'Clothing, Shoes & Jewelry ']\n",
    "sns.boxplot(data = csj, x='Main Category', y='Selling Price($)', showfliers=False)\n",
    "plt.title(\"Distribution of the Prices in Clothing, Shoes & Jewelry Category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Relationship between Price & Shipping Weight in Clothing, Shoes & Jewelry Category')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#generate scatterplot to understand the relationship\n",
    "sns.scatterplot(data=csj, x=\"Selling Price($)\", y=\"Shipping Weight(Pounds)\")\n",
    "plt.title(\"Relationship between Price & Shipping Weight in Clothing, Shoes & Jewelry Category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of the Prices in Arts, Crafts & Sewing Category')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#generate boxplot to understand the distribution  \n",
    "artscraft = dataset[dataset[\"Main Category\"] == 'Arts, Crafts & Sewing ']\n",
    "sns.boxplot(data = artscraft, x='Main Category', y='Selling Price($)', showfliers=False)\n",
    "plt.title(\"Distribution of the Prices in Arts, Crafts & Sewing Category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Relationship between Price & Shipping Weight in Arts, Crafts & Sewing Category')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#generate scatterplot to understand the relationship\n",
    "sns.scatterplot(data=artscraft, x=\"Selling Price($)\", y=\"Shipping Weight(Pounds)\")\n",
    "plt.title(\"Relationship between Price & Shipping Weight in Arts, Crafts & Sewing Category\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion: From this EDA, we can draw that there is little relation between the shipping weight and the selling price of the product. Therefore, we decided that a more suitable way of recommending products would be by their types which is in this case is through their Categories....."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Denzel's Portion: \n",
    "Recommendation System - Content-Based Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"amazon products.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10002 entries, 0 to 10001\n",
      "Data columns (total 28 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   Uniq Id                10002 non-null  object \n",
      " 1   Product Name           10002 non-null  object \n",
      " 2   Brand Name             0 non-null      float64\n",
      " 3   Asin                   0 non-null      float64\n",
      " 4   Category               9172 non-null   object \n",
      " 5   Upc Ean Code           34 non-null     object \n",
      " 6   List Price             0 non-null      float64\n",
      " 7   Selling Price          9895 non-null   object \n",
      " 8   Quantity               0 non-null      float64\n",
      " 9   Model Number           8232 non-null   object \n",
      " 10  About Product          9729 non-null   object \n",
      " 11  Product Specification  8370 non-null   object \n",
      " 12  Technical Details      9212 non-null   object \n",
      " 13  Shipping Weight        8864 non-null   object \n",
      " 14  Product Dimensions     479 non-null    object \n",
      " 15  Image                  10002 non-null  object \n",
      " 16  Variants               2478 non-null   object \n",
      " 17  Sku                    0 non-null      float64\n",
      " 18  Product Url            10002 non-null  object \n",
      " 19  Stock                  0 non-null      float64\n",
      " 20  Product Details        0 non-null      float64\n",
      " 21  Dimensions             0 non-null      float64\n",
      " 22  Color                  0 non-null      float64\n",
      " 23  Ingredients            0 non-null      float64\n",
      " 24  Direction To Use       0 non-null      float64\n",
      " 25  Is Amazon Seller       10002 non-null  object \n",
      " 26  Size Quantity Variant  0 non-null      float64\n",
      " 27  Product Description    0 non-null      float64\n",
      "dtypes: float64(13), object(15)\n",
      "memory usage: 2.1+ MB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Word Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can compute the similarity between categories using TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10002, 1133)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import TfIdfVectorizer from scikit-learn\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "#Define a TF-IDF Vectorizer Object. Remove all english stop words such as 'the', 'a'\n",
    "tfidf = TfidfVectorizer(stop_words='english')\n",
    "\n",
    "#Replace NaN with an empty string\n",
    "dataset[\"Category\"] = dataset[\"Category\"].fillna(\"\")\n",
    "\n",
    "#Construct the required TF-IDF matrix by fitting and transforming the data\n",
    "tfidf_matrix = tfidf.fit_transform(dataset[\"Category\"])\n",
    "\n",
    "#Output the shape of tfidf_matrix\n",
    "tfidf_matrix.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that there are 1133 vocabularies in our dataset of 10002 products."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['accent',\n",
       " 'accents',\n",
       " 'accessories',\n",
       " 'accessory',\n",
       " 'action',\n",
       " 'activities',\n",
       " 'activity',\n",
       " 'additives',\n",
       " 'adhesives',\n",
       " 'adirondack',\n",
       " 'adult',\n",
       " 'advent',\n",
       " 'agility',\n",
       " 'aids',\n",
       " 'air',\n",
       " 'airbrush',\n",
       " 'aircraft',\n",
       " 'airplane',\n",
       " 'airplanes',\n",
       " 'albums']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can look at some of the features.\n",
    "tfidf.get_feature_names()[0:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use this matrix to calculate the similarity score with linear kernel, cosine similarity and sigmoid score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Computing similarity score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import linear_kernel, cosine_similarity, and sigmoid_kernel\n",
    "from sklearn.metrics.pairwise import linear_kernel\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.metrics.pairwise import sigmoid_kernel\n",
    "\n",
    "# Create the matrix\n",
    "linear = linear_kernel(tfidf_matrix, tfidf_matrix)\n",
    "cosine_sim = cosine_similarity(tfidf_matrix, tfidf_matrix)\n",
    "sig_score = sigmoid_kernel(tfidf_matrix, tfidf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10002, 10002)\n",
      "(10002, 10002)\n",
      "(10002, 10002)\n"
     ]
    }
   ],
   "source": [
    "print(linear.shape)\n",
    "print(cosine_sim.shape)\n",
    "print(sig_score.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         1.         0.27876877 ... 0.25244281 0.27876877 0.        ]\n",
      "[0.         1.         0.27876877 ... 0.25244281 0.27876877 0.        ]\n",
      "[0.76159416 0.76196458 0.76169747 ... 0.76168771 0.76169747 0.76159416]\n"
     ]
    }
   ],
   "source": [
    "print(linear[1])\n",
    "print(cosine_sim[1])\n",
    "print(sig_score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This matrix represents each product category's similarity score with every other product category."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Input / output function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to define a function that takes the product name as an input and outputs a list of the 10 most similar products. For this we need a reverse mapping of products and DataFrame indices. This means we need a mechanism to identify the index of a product in our DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Construct a reverse map of indices and product names\n",
    "indices = pd.Series(dataset.index, index=dataset[\"Product Name\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product Name\n",
       "DB Longboards CoreFlex Crossbow 41\" Bamboo Fiberglass Longboard Complete                                                                             0\n",
       "Electronic Snap Circuits Mini Kits Classpack, FM Radio, Motion Detector, Music Box (Set of 5)                                                        1\n",
       "3Doodler Create Flexy 3D Printing Filament Refill Bundle (X5 Pack, Over 1000'. of Extruded Plastics! - Innovate                                      2\n",
       "Guillow Airplane Design Studio with Travel Case Building Kit                                                                                         3\n",
       "Woodstock- Collage 500 pc Puzzle                                                                                                                     4\n",
       "Terra by Battat – 4 Dinosaur Toys, Medium – Dinosaurs for Kids & Collectors, Scientifically Accurate & Designed by A Paleo-Artist; Age 3+ (4 Pc)     5\n",
       "Rubie's Child's Pokemon Deluxe Pikachu Costume, X-Small                                                                                              6\n",
       "Hoffmaster 120813 Double-Tipped Triangular Crayon, 88 mm Length, Wrapped (500 Packs of 2)                                                            7\n",
       "ARTSCAPE Etched Glass 24\" x 36\" Window Film, 24-by-36-Inch                                                                                           8\n",
       "Pokemon TCG: Sun and Moon Crimson Invasion Elite Trainer Box                                                                                         9\n",
       "Moonlite, 3-Story Bundle for Boys, Includes 3 Story Reels for Use with Storybook Projector                                                          10\n",
       "DC Cover Girls: Black Canary by Joëlle Jones Statue                                                                                                 11\n",
       "Baby Doll Bedding Chevron Window Valance and Curtain Set, Orange                                                                                    12\n",
       "LEGO Minecraft Creeper BigFig and Ocelot Characters 21156 Buildable Toy Minecraft Figure Gift Set for Play and Decoration, New 2020 (184 Pieces)    13\n",
       "C&D Visionary DC Comic Originals Flash Logo Sticker                                                                                                 14\n",
       "The Learning Journey ABC Melody Maker, Primary                                                                                                      15\n",
       "The Complete Common Core: State Standards Kit, Grade 5                                                                                              16\n",
       "Flash Furniture 25''W x 45''L Trapezoid Red HP Laminate Activity Table - Height Adjustable Short Legs                                               17\n",
       "Schylling Veterinarian Kit                                                                                                                          18\n",
       "Yellies! Frizz; Voice-Activated Spider Pet; Ages 5 & Up                                                                                             19\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that takes in product name as input and outputs most similar product\n",
    "def rec_lin(product_name, linear=linear):\n",
    "    # Get the index of the product that matches the product name\n",
    "    idx = indices[product_name]\n",
    "\n",
    "    # Get the pairwise similarity scores\n",
    "    # Enumerate adds a counter to the iterable and lets it be converted into a list of tuples\n",
    "    sim_scores = list(enumerate(linear[idx]))\n",
    "\n",
    "    # Sort the products based on the similarity scores\n",
    "    # Reverse gives us the similarity scores in descending order\n",
    "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
    "    \n",
    "    # Get the scores of the 10 most similar products\n",
    "    sim_scores = sim_scores[1:11]\n",
    "\n",
    "    # Get the product indices\n",
    "    product_indices = [i[0] for i in sim_scores]\n",
    "\n",
    "    # Return the top 10 most similar products\n",
    "    # iloc allows us to retrieve rows from a data frame\n",
    "    return dataset[[\"Product Name\",\"Selling Price\"]].iloc[product_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = input(\"What would you like to search for today? \")\n",
    "result = rec_lin(name)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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