USL_W1_AdditionalCaseStudy_ProductSegmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Product Segmentation Case Study"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Context\n",
    "\n",
    "When you think of sneakers for a trip, the importance of good footwear cannot be discarded, and the obvious brands that come to mind are Adidas and Nike. Adidas vs Nike is a constant debate as the two giants in the apparel market, with a large market cap and market share, battle it out to come on top. As a newly hired Data Scientist in a market research company, you have been given the task of extracting insights from the data of men's and women's shoes, and grouping products together to identify similarities and differences between the product range of these renowned brands."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objective\n",
    "\n",
    "To perform an exploratory data analysis and cluster the products based on various factors\n",
    "\n",
    "\n",
    "## Key Questions\n",
    "\n",
    "- Which variables are most important for clustering?\n",
    "- How each cluster is different from the others?\n",
    "- What are the business recommendations?\n",
    "\n",
    "\n",
    "## Data Description\n",
    "\n",
    "The dataset consists of 3268 products from Nike and Adidas with features of information including their ratings, discount, sales price, listed price, product name, and the number of reviews.\n",
    "\n",
    "- Product Name: Name of the product\n",
    "- Product ID: ID of the product\n",
    "- Listing Price: Listed price of the product\n",
    "- Sale Price: Sale price of the product\n",
    "- Discount: Percentage of discount on the product\n",
    "- Brand: Brand of the product\n",
    "- Rating: Rating of the product\n",
    "- Reviews: Number of reviews for the product"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iM29eMvG9MQ6"
   },
   "source": [
    "## Let's start coding!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# please uncomment and run the next line if yellowbrick library is not previously installed\n",
    "#!pip install yellowbrick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 2;\n",
       "                var nbb_unformatted_code = \"# this will help in making the Python code more structured automatically (good coding practice)\\n%load_ext nb_black\\n\\n# Libraries to help with reading and manipulating data\\nimport numpy as np\\nimport pandas as pd\\n\\n# Libraries to help with data visualization\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\\n\\nsns.set_theme()\\n\\n# Removes the limit for the number of displayed columns\\npd.set_option(\\\"display.max_columns\\\", None)\\n# Sets the limit for the number of displayed rows\\npd.set_option(\\\"display.max_rows\\\", 200)\\n\\nfrom sklearn.preprocessing import StandardScaler\\n\\nfrom scipy.spatial.distance import cdist\\nfrom sklearn.cluster import KMeans\\nfrom sklearn.metrics import silhouette_score\\n\\n# to visualize the elbow curve and silhouette scores\\nfrom yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer\";\n",
       "                var nbb_formatted_code = \"# this will help in making the Python code more structured automatically (good coding practice)\\n%load_ext nb_black\\n\\n# Libraries to help with reading and manipulating data\\nimport numpy as np\\nimport pandas as pd\\n\\n# Libraries to help with data visualization\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\\n\\nsns.set_theme()\\n\\n# Removes the limit for the number of displayed columns\\npd.set_option(\\\"display.max_columns\\\", None)\\n# Sets the limit for the number of displayed rows\\npd.set_option(\\\"display.max_rows\\\", 200)\\n\\nfrom sklearn.preprocessing import StandardScaler\\n\\nfrom scipy.spatial.distance import cdist\\nfrom sklearn.cluster import KMeans\\nfrom sklearn.metrics import silhouette_score\\n\\n# to visualize the elbow curve and silhouette scores\\nfrom yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer\";\n",
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    "# this will help in making the Python code more structured automatically (good coding practice)\n",
    "%load_ext nb_black\n",
    "\n",
    "# Libraries to help with reading and manipulating data\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Libraries to help with data visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_theme()\n",
    "\n",
    "# Removes the limit for the number of displayed columns\n",
    "pd.set_option(\"display.max_columns\", None)\n",
    "# Sets the limit for the number of displayed rows\n",
    "pd.set_option(\"display.max_rows\", 200)\n",
    "\n",
    "# to scale the data using z-score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# to compute distances\n",
    "from scipy.spatial.distance import cdist\n",
    "\n",
    "# to perform k-means clustering and compute silhouette scores\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "# to visualize the elbow curve and silhouette scores\n",
    "from yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
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       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 3;\n",
       "                var nbb_unformatted_code = \"# loading the dataset\\ndata = pd.read_csv(\\\"data_add_nik.csv\\\")\";\n",
       "                var nbb_formatted_code = \"# loading the dataset\\ndata = pd.read_csv(\\\"data_add_nik.csv\\\")\";\n",
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    "# loading the dataset\n",
    "data = pd.read_csv(\"data_add_nik.csv\")"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3268, 8)"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
       "                var nbb_unformatted_code = \"data.shape\";\n",
       "                var nbb_formatted_code = \"data.shape\";\n",
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    "data.shape"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The dataset has 3268 rows and 8 columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "</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>Product ID</th>\n",
       "      <th>Listing Price</th>\n",
       "      <th>Sale Price</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>Women's adidas Originals POD-S3.1 Shoes</td>\n",
       "      <td>CG6188</td>\n",
       "      <td>13999</td>\n",
       "      <td>6999</td>\n",
       "      <td>50</td>\n",
       "      <td>Adidas ORIGINALS</td>\n",
       "      <td>3.3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1551</th>\n",
       "      <td>Men's adidas Originals Superstar Pure Shoes</td>\n",
       "      <td>FV3013</td>\n",
       "      <td>11999</td>\n",
       "      <td>11999</td>\n",
       "      <td>0</td>\n",
       "      <td>Adidas ORIGINALS</td>\n",
       "      <td>3.9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1352</th>\n",
       "      <td>Men's adidas Originals Superstar Shoes</td>\n",
       "      <td>FV2806</td>\n",
       "      <td>7999</td>\n",
       "      <td>7999</td>\n",
       "      <td>0</td>\n",
       "      <td>Adidas ORIGINALS</td>\n",
       "      <td>4.4</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1060</th>\n",
       "      <td>Men's adidas Football Nemeziz 19.3 Indoor Shoes</td>\n",
       "      <td>F34411</td>\n",
       "      <td>5999</td>\n",
       "      <td>3599</td>\n",
       "      <td>40</td>\n",
       "      <td>Adidas SPORT PERFORMANCE</td>\n",
       "      <td>4.5</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808</th>\n",
       "      <td>Men's adidas Sport Inspired Court 80s Shoes</td>\n",
       "      <td>EE9676</td>\n",
       "      <td>5999</td>\n",
       "      <td>3599</td>\n",
       "      <td>40</td>\n",
       "      <td>Adidas CORE / NEO</td>\n",
       "      <td>4.5</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>Men's adidas Running Stargon 1.0 Shoes</td>\n",
       "      <td>CM4935</td>\n",
       "      <td>4799</td>\n",
       "      <td>3839</td>\n",
       "      <td>20</td>\n",
       "      <td>Adidas CORE / NEO</td>\n",
       "      <td>3.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2107</th>\n",
       "      <td>Men's adidas Originals Yung-96 Chasm Shoes</td>\n",
       "      <td>EE7238</td>\n",
       "      <td>7999</td>\n",
       "      <td>4799</td>\n",
       "      <td>40</td>\n",
       "      <td>Adidas ORIGINALS</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3002</th>\n",
       "      <td>Nike SB Air Max Stefan Janoski 2</td>\n",
       "      <td>AQ7477-009</td>\n",
       "      <td>0</td>\n",
       "      <td>9995</td>\n",
       "      <td>0</td>\n",
       "      <td>Nike</td>\n",
       "      <td>2.6</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2329</th>\n",
       "      <td>Men's adidas Originals Rivalry Low Shoes</td>\n",
       "      <td>FV4287</td>\n",
       "      <td>10999</td>\n",
       "      <td>10999</td>\n",
       "      <td>0</td>\n",
       "      <td>Adidas ORIGINALS</td>\n",
       "      <td>2.8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>Men's adidas Sport Inspired Lite Racer RBN Shoes</td>\n",
       "      <td>F36642</td>\n",
       "      <td>5599</td>\n",
       "      <td>3919</td>\n",
       "      <td>30</td>\n",
       "      <td>Adidas CORE / NEO</td>\n",
       "      <td>3.0</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                                          Product Name  Product ID  \\\n",
       "255            Women's adidas Originals POD-S3.1 Shoes      CG6188   \n",
       "1551       Men's adidas Originals Superstar Pure Shoes      FV3013   \n",
       "1352            Men's adidas Originals Superstar Shoes      FV2806   \n",
       "1060   Men's adidas Football Nemeziz 19.3 Indoor Shoes      F34411   \n",
       "808        Men's adidas Sport Inspired Court 80s Shoes      EE9676   \n",
       "836             Men's adidas Running Stargon 1.0 Shoes      CM4935   \n",
       "2107        Men's adidas Originals Yung-96 Chasm Shoes      EE7238   \n",
       "3002                  Nike SB Air Max Stefan Janoski 2  AQ7477-009   \n",
       "2329          Men's adidas Originals Rivalry Low Shoes      FV4287   \n",
       "602   Men's adidas Sport Inspired Lite Racer RBN Shoes      F36642   \n",
       "\n",
       "      Listing Price  Sale Price  Discount                     Brand  Rating  \\\n",
       "255           13999        6999        50          Adidas ORIGINALS     3.3   \n",
       "1551          11999       11999         0          Adidas ORIGINALS     3.9   \n",
       "1352           7999        7999         0          Adidas ORIGINALS     4.4   \n",
       "1060           5999        3599        40  Adidas SPORT PERFORMANCE     4.5   \n",
       "808            5999        3599        40         Adidas CORE / NEO     4.5   \n",
       "836            4799        3839        20         Adidas CORE / NEO     3.5   \n",
       "2107           7999        4799        40          Adidas ORIGINALS     3.0   \n",
       "3002              0        9995         0                      Nike     2.6   \n",
       "2329          10999       10999         0          Adidas ORIGINALS     2.8   \n",
       "602            5599        3919        30         Adidas CORE / NEO     3.0   \n",
       "\n",
       "      Reviews  \n",
       "255         8  \n",
       "1551       10  \n",
       "1352       42  \n",
       "1060       75  \n",
       "808        55  \n",
       "836        21  \n",
       "2107       62  \n",
       "3002       11  \n",
       "2329        8  \n",
       "602        56  "
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     "execution_count": 5,
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       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 5;\n",
       "                var nbb_unformatted_code = \"# viewing a random sample of the dataset\\ndata.sample(n=10, random_state=1)\";\n",
       "                var nbb_formatted_code = \"# viewing a random sample of the dataset\\ndata.sample(n=10, random_state=1)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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   "metadata": {},
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       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 6;\n",
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   "outputs": [
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       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"# fixing column names\\ndf.columns = [c.replace(\\\" \\\", \\\"_\\\") for c in df.columns]\";\n",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3268 entries, 0 to 3267\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Product_Name   3268 non-null   object \n",
      " 1   Product_ID     3268 non-null   object \n",
      " 2   Listing_Price  3268 non-null   int64  \n",
      " 3   Sale_Price     3268 non-null   int64  \n",
      " 4   Discount       3268 non-null   int64  \n",
      " 5   Brand          3268 non-null   object \n",
      " 6   Rating         3268 non-null   float64\n",
      " 7   Reviews        3268 non-null   int64  \n",
      "dtypes: float64(1), int64(4), object(3)\n",
      "memory usage: 204.4+ KB\n"
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       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 8;\n",
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       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's look at the structure of the data\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We won't need Product_ID for analysis, so let's drop this column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 9;\n",
       "                var nbb_unformatted_code = \"df.drop(\\\"Product_ID\\\", axis=1, inplace=True)\";\n",
       "                var nbb_formatted_code = \"df.drop(\\\"Product_ID\\\", axis=1, inplace=True)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.drop(\"Product_ID\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "117"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 10;\n",
       "                var nbb_unformatted_code = \"# let's check for duplicate observations\\ndf.duplicated().sum()\";\n",
       "                var nbb_formatted_code = \"# let's check for duplicate observations\\ndf.duplicated().sum()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's check for duplicate observations\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are 117 duplicate observations. We will remove them from the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 11;\n",
       "                var nbb_unformatted_code = \"df = df[(~df.duplicated())].copy()\";\n",
       "                var nbb_formatted_code = \"df = df[(~df.duplicated())].copy()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df[(~df.duplicated())].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's take a look at the summary of the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Listing_Price</th>\n",
       "      <th>Sale_Price</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3151.000000</td>\n",
       "      <td>3151.000000</td>\n",
       "      <td>3151.000000</td>\n",
       "      <td>3151.000000</td>\n",
       "      <td>3151.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7045.960330</td>\n",
       "      <td>5983.166931</td>\n",
       "      <td>27.860997</td>\n",
       "      <td>3.285687</td>\n",
       "      <td>41.891146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4652.089511</td>\n",
       "      <td>4173.708897</td>\n",
       "      <td>22.442753</td>\n",
       "      <td>1.371611</td>\n",
       "      <td>31.283464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>449.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4599.000000</td>\n",
       "      <td>2999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5999.000000</td>\n",
       "      <td>4599.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8999.000000</td>\n",
       "      <td>7799.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>29999.000000</td>\n",
       "      <td>36500.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>223.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Listing_Price    Sale_Price     Discount       Rating      Reviews\n",
       "count    3151.000000   3151.000000  3151.000000  3151.000000  3151.000000\n",
       "mean     7045.960330   5983.166931    27.860997     3.285687    41.891146\n",
       "std      4652.089511   4173.708897    22.442753     1.371611    31.283464\n",
       "min         0.000000    449.000000     0.000000     0.000000     0.000000\n",
       "25%      4599.000000   2999.000000     0.000000     2.600000    12.000000\n",
       "50%      5999.000000   4599.000000    40.000000     3.600000    40.000000\n",
       "75%      8999.000000   7799.000000    50.000000     4.400000    69.000000\n",
       "max     29999.000000  36500.000000    60.000000     5.000000   223.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 12;\n",
       "                var nbb_unformatted_code = \"df.describe()\";\n",
       "                var nbb_formatted_code = \"df.describe()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "\n",
    "- 0 in the listing price indicates missing values.\n",
    "- The average listing price is 7046.\n",
    "- The average sale price is 5983.\n",
    "- The average discount is 28%.\n",
    "- The average rating is 3.3.\n",
    "- The average number of reviews is 42."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "336"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 13;\n",
       "                var nbb_unformatted_code = \"# let's check how many products have listing price 0\\n(df.Listing_Price == 0).sum()\";\n",
       "                var nbb_formatted_code = \"# let's check how many products have listing price 0\\n(df.Listing_Price == 0).sum()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's check how many products have listing price 0\n",
    "(df.Listing_Price == 0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_Name</th>\n",
       "      <th>Listing_Price</th>\n",
       "      <th>Sale_Price</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2625</th>\n",
       "      <td>Nike Air Force 1 '07 Essential</td>\n",
       "      <td>0</td>\n",
       "      <td>7495</td>\n",
       "      <td>0</td>\n",
       "      <td>Nike</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2626</th>\n",
       "      <td>Nike Air Force 1 '07</td>\n",
       "      <td>0</td>\n",
       "      <td>7495</td>\n",
       "      <td>0</td>\n",
       "      <td>Nike</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627</th>\n",
       "      <td>Nike Air Force 1 Sage Low LX</td>\n",
       "      <td>0</td>\n",
       "      <td>9995</td>\n",
       "      <td>0</td>\n",
       "      <td>Nike</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2628</th>\n",
       "      <td>Nike Air Max Dia SE</td>\n",
       "      <td>0</td>\n",
       "      <td>9995</td>\n",
       "      <td>0</td>\n",
       "      <td>Nike</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2629</th>\n",
       "      <td>Nike Air Max Verona</td>\n",
       "      <td>0</td>\n",
       "      <td>9995</td>\n",
       "      <td>0</td>\n",
       "      <td>Nike</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3254</th>\n",
       "      <td>Nike Mercurial Vapor 13 Club MG</td>\n",
       "      <td>0</td>\n",
       "      <td>4995</td>\n",
       "      <td>0</td>\n",
       "      <td>Nike</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3257</th>\n",
       "      <td>Air Jordan 5 Retro</td>\n",
       "      <td>0</td>\n",
       "      <td>15995</td>\n",
       "      <td>0</td>\n",
       "      <td>Nike</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3260</th>\n",
       "      <td>Nike Tiempo Legend 8 Academy TF</td>\n",
       "      <td>0</td>\n",
       "      <td>6495</td>\n",
       "      <td>0</td>\n",
       "      <td>Nike</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3262</th>\n",
       "      <td>Nike React Metcon AMP</td>\n",
       "      <td>0</td>\n",
       "      <td>13995</td>\n",
       "      <td>0</td>\n",
       "      <td>Nike</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3266</th>\n",
       "      <td>Nike Air Max 98</td>\n",
       "      <td>0</td>\n",
       "      <td>16995</td>\n",
       "      <td>0</td>\n",
       "      <td>Nike</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>336 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Product_Name  Listing_Price  Sale_Price  Discount  \\\n",
       "2625   Nike Air Force 1 '07 Essential              0        7495         0   \n",
       "2626             Nike Air Force 1 '07              0        7495         0   \n",
       "2627     Nike Air Force 1 Sage Low LX              0        9995         0   \n",
       "2628              Nike Air Max Dia SE              0        9995         0   \n",
       "2629              Nike Air Max Verona              0        9995         0   \n",
       "...                               ...            ...         ...       ...   \n",
       "3254  Nike Mercurial Vapor 13 Club MG              0        4995         0   \n",
       "3257               Air Jordan 5 Retro              0       15995         0   \n",
       "3260  Nike Tiempo Legend 8 Academy TF              0        6495         0   \n",
       "3262            Nike React Metcon AMP              0       13995         0   \n",
       "3266                  Nike Air Max 98              0       16995         0   \n",
       "\n",
       "     Brand  Rating  Reviews  \n",
       "2625  Nike     0.0        0  \n",
       "2626  Nike     0.0        0  \n",
       "2627  Nike     0.0        0  \n",
       "2628  Nike     0.0        0  \n",
       "2629  Nike     0.0        0  \n",
       "...    ...     ...      ...  \n",
       "3254  Nike     0.0        0  \n",
       "3257  Nike     3.3        3  \n",
       "3260  Nike     0.0        0  \n",
       "3262  Nike     3.0        1  \n",
       "3266  Nike     4.0        4  \n",
       "\n",
       "[336 rows x 7 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 14;\n",
       "                var nbb_unformatted_code = \"# let's check the products which have listing price 0\\ndf[(df.Listing_Price == 0)]\";\n",
       "                var nbb_formatted_code = \"# let's check the products which have listing price 0\\ndf[(df.Listing_Price == 0)]\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's check the products which have listing price 0\n",
    "df[(df.Listing_Price == 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Listing_Price</th>\n",
       "      <th>Sale_Price</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>336.0</td>\n",
       "      <td>336.000000</td>\n",
       "      <td>336.0</td>\n",
       "      <td>336.000000</td>\n",
       "      <td>336.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11203.050595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.797619</td>\n",
       "      <td>8.261905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4623.825788</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.150445</td>\n",
       "      <td>19.708393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1595.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7995.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10995.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.950000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>13995.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>36500.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>223.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Listing_Price    Sale_Price  Discount      Rating     Reviews\n",
       "count          336.0    336.000000     336.0  336.000000  336.000000\n",
       "mean             0.0  11203.050595       0.0    2.797619    8.261905\n",
       "std              0.0   4623.825788       0.0    2.150445   19.708393\n",
       "min              0.0   1595.000000       0.0    0.000000    0.000000\n",
       "25%              0.0   7995.000000       0.0    0.000000    0.000000\n",
       "50%              0.0  10995.000000       0.0    3.950000    1.000000\n",
       "75%              0.0  13995.000000       0.0    4.700000    6.000000\n",
       "max              0.0  36500.000000       0.0    5.000000  223.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 15;\n",
       "                var nbb_unformatted_code = \"df[(df.Listing_Price == 0)].describe()\";\n",
       "                var nbb_formatted_code = \"df[(df.Listing_Price == 0)].describe()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[(df.Listing_Price == 0)].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are 336 observations that have missing values in the listing price column\n",
    "- We see that the discount for the products with listing price 0 is 0.\n",
    "- So, we will replace the listing price with the corresponding sale price for those observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 16;\n",
       "                var nbb_unformatted_code = \"df.loc[(df.Listing_Price == 0), [\\\"Listing_Price\\\"]] = df.loc[\\n    (df.Listing_Price == 0), [\\\"Sale_Price\\\"]\\n].values\";\n",
       "                var nbb_formatted_code = \"df.loc[(df.Listing_Price == 0), [\\\"Listing_Price\\\"]] = df.loc[\\n    (df.Listing_Price == 0), [\\\"Sale_Price\\\"]\\n].values\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[(df.Listing_Price == 0), [\"Listing_Price\"]] = df.loc[\n",
    "    (df.Listing_Price == 0), [\"Sale_Price\"]\n",
    "].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     3151.000000\n",
       "mean      8240.573151\n",
       "std       4363.018245\n",
       "min        899.000000\n",
       "25%       4999.000000\n",
       "50%       7599.000000\n",
       "75%      10995.000000\n",
       "max      36500.000000\n",
       "Name: Listing_Price, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 17;\n",
       "                var nbb_unformatted_code = \"df.Listing_Price.describe()\";\n",
       "                var nbb_formatted_code = \"df.Listing_Price.describe()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Listing_Price.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product_Name     0\n",
       "Listing_Price    0\n",
       "Sale_Price       0\n",
       "Discount         0\n",
       "Brand            0\n",
       "Rating           0\n",
       "Reviews          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 18;\n",
       "                var nbb_unformatted_code = \"# checking missing values\\ndf.isna().sum()\";\n",
       "                var nbb_formatted_code = \"# checking missing values\\ndf.isna().sum()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking missing values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are no missing values in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 19;\n",
       "                var nbb_unformatted_code = \"# function to plot a boxplot and a histogram along the same scale.\\n\\n\\ndef histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):\\n    \\\"\\\"\\\"\\n    Boxplot and histogram combined\\n\\n    data: dataframe\\n    feature: dataframe column\\n    figsize: size of figure (default (12,7))\\n    kde: whether to the show density curve (default False)\\n    bins: number of bins for histogram (default None)\\n    \\\"\\\"\\\"\\n    f2, (ax_box2, ax_hist2) = plt.subplots(\\n        nrows=2,  # Number of rows of the subplot grid= 2\\n        sharex=True,  # x-axis will be shared among all subplots\\n        gridspec_kw={\\\"height_ratios\\\": (0.25, 0.75)},\\n        figsize=figsize,\\n    )  # creating the 2 subplots\\n    sns.boxplot(\\n        data=data, x=feature, ax=ax_box2, showmeans=True, color=\\\"violet\\\"\\n    )  # boxplot will be created and a star will indicate the mean value of the column\\n    sns.histplot(\\n        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette=\\\"winter\\\"\\n    ) if bins else sns.histplot(\\n        data=data, x=feature, kde=kde, ax=ax_hist2\\n    )  # For histogram\\n    ax_hist2.axvline(\\n        data[feature].mean(), color=\\\"green\\\", linestyle=\\\"--\\\"\\n    )  # Add mean to the histogram\\n    ax_hist2.axvline(\\n        data[feature].median(), color=\\\"black\\\", linestyle=\\\"-\\\"\\n    )  # Add median to the histogram\";\n",
       "                var nbb_formatted_code = \"# function to plot a boxplot and a histogram along the same scale.\\n\\n\\ndef histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):\\n    \\\"\\\"\\\"\\n    Boxplot and histogram combined\\n\\n    data: dataframe\\n    feature: dataframe column\\n    figsize: size of figure (default (12,7))\\n    kde: whether to the show density curve (default False)\\n    bins: number of bins for histogram (default None)\\n    \\\"\\\"\\\"\\n    f2, (ax_box2, ax_hist2) = plt.subplots(\\n        nrows=2,  # Number of rows of the subplot grid= 2\\n        sharex=True,  # x-axis will be shared among all subplots\\n        gridspec_kw={\\\"height_ratios\\\": (0.25, 0.75)},\\n        figsize=figsize,\\n    )  # creating the 2 subplots\\n    sns.boxplot(\\n        data=data, x=feature, ax=ax_box2, showmeans=True, color=\\\"violet\\\"\\n    )  # boxplot will be created and a star will indicate the mean value of the column\\n    sns.histplot(\\n        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette=\\\"winter\\\"\\n    ) if bins else sns.histplot(\\n        data=data, x=feature, kde=kde, ax=ax_hist2\\n    )  # For histogram\\n    ax_hist2.axvline(\\n        data[feature].mean(), color=\\\"green\\\", linestyle=\\\"--\\\"\\n    )  # Add mean to the histogram\\n    ax_hist2.axvline(\\n        data[feature].median(), color=\\\"black\\\", linestyle=\\\"-\\\"\\n    )  # Add median to the histogram\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# function to plot a boxplot and a histogram along the same scale.\n",
    "\n",
    "\n",
    "def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):\n",
    "    \"\"\"\n",
    "    Boxplot and histogram combined\n",
    "\n",
    "    data: dataframe\n",
    "    feature: dataframe column\n",
    "    figsize: size of figure (default (12,7))\n",
    "    kde: whether to the show density curve (default False)\n",
    "    bins: number of bins for histogram (default None)\n",
    "    \"\"\"\n",
    "    f2, (ax_box2, ax_hist2) = plt.subplots(\n",
    "        nrows=2,  # Number of rows of the subplot grid= 2\n",
    "        sharex=True,  # x-axis will be shared among all subplots\n",
    "        gridspec_kw={\"height_ratios\": (0.25, 0.75)},\n",
    "        figsize=figsize,\n",
    "    )  # creating the 2 subplots\n",
    "    sns.boxplot(\n",
    "        data=data, x=feature, ax=ax_box2, showmeans=True, color=\"violet\"\n",
    "    )  # boxplot will be created and a star will indicate the mean value of the column\n",
    "    sns.histplot(\n",
    "        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette=\"winter\"\n",
    "    ) if bins else sns.histplot(\n",
    "        data=data, x=feature, kde=kde, ax=ax_hist2\n",
    "    )  # For histogram\n",
    "    ax_hist2.axvline(\n",
    "        data[feature].mean(), color=\"green\", linestyle=\"--\"\n",
    "    )  # Add mean to the histogram\n",
    "    ax_hist2.axvline(\n",
    "        data[feature].median(), color=\"black\", linestyle=\"-\"\n",
    "    )  # Add median to the histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 20;\n",
       "                var nbb_unformatted_code = \"# selecting numerical columns\\nnum_col = df.select_dtypes(include=np.number).columns.tolist()\\n\\nfor item in num_col:\\n    histogram_boxplot(df, item)\";\n",
       "                var nbb_formatted_code = \"# selecting numerical columns\\nnum_col = df.select_dtypes(include=np.number).columns.tolist()\\n\\nfor item in num_col:\\n    histogram_boxplot(df, item)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# selecting numerical columns\n",
    "num_col = df.select_dtypes(include=np.number).columns.tolist()\n",
    "\n",
    "for item in num_col:\n",
    "    histogram_boxplot(df, item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "\n",
    "- Listing price and sale price have right-skewed distributions with upper outliers, which indicates the presence of very expensive products.\n",
    "- The maximum discount given is 60%.\n",
    "- Rating is left-skewed and most of the ratings are between 2.5 and 4.5.\n",
    "- The number of reviews is between 1 and 100, with an outlier value above 200."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 21;\n",
       "                var nbb_unformatted_code = \"fig, axes = plt.subplots(3, 2, figsize=(20, 15))\\nfig.suptitle(\\\"CDF plot of numerical variables\\\", fontsize=20)\\ncounter = 0\\nfor ii in range(3):\\n    sns.ecdfplot(ax=axes[ii][0], x=df[num_col[counter]])\\n    counter = counter + 1\\n    if counter != 5:\\n        sns.ecdfplot(ax=axes[ii][1], x=df[num_col[counter]])\\n        counter = counter + 1\\n    else:\\n        pass\\n\\nfig.tight_layout(pad=2.0)\";\n",
       "                var nbb_formatted_code = \"fig, axes = plt.subplots(3, 2, figsize=(20, 15))\\nfig.suptitle(\\\"CDF plot of numerical variables\\\", fontsize=20)\\ncounter = 0\\nfor ii in range(3):\\n    sns.ecdfplot(ax=axes[ii][0], x=df[num_col[counter]])\\n    counter = counter + 1\\n    if counter != 5:\\n        sns.ecdfplot(ax=axes[ii][1], x=df[num_col[counter]])\\n        counter = counter + 1\\n    else:\\n        pass\\n\\nfig.tight_layout(pad=2.0)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, 2, figsize=(20, 15))\n",
    "fig.suptitle(\"CDF plot of numerical variables\", fontsize=20)\n",
    "counter = 0\n",
    "for ii in range(3):\n",
    "    sns.ecdfplot(ax=axes[ii][0], x=df[num_col[counter]])\n",
    "    counter = counter + 1\n",
    "    if counter != 5:\n",
    "        sns.ecdfplot(ax=axes[ii][1], x=df[num_col[counter]])\n",
    "        counter = counter + 1\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "fig.tight_layout(pad=2.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "\n",
    "- 90% of the products have listing prices less than 15000.\n",
    "- 95% of the product have a sale price of less than 15000.\n",
    "- 80% of the products have at least 50% discount or less than 50%.\n",
    "- 50% off the products have a rating of 3.5 or less than 3.5.\n",
    "- Almost all products have 100 or fewer reviews."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 22;\n",
       "                var nbb_unformatted_code = \"# function to create labeled barplots\\n\\n\\ndef labeled_barplot(data, feature, perc=False, n=None):\\n    \\\"\\\"\\\"\\n    Barplot with percentage at the top\\n\\n    data: dataframe\\n    feature: dataframe column\\n    perc: whether to display percentages instead of count (default is False)\\n    n: displays the top n category levels (default is None, i.e., display all levels)\\n    \\\"\\\"\\\"\\n\\n    total = len(data[feature])  # length of the column\\n    count = data[feature].nunique()\\n    if n is None:\\n        plt.figure(figsize=(count + 1, 5))\\n    else:\\n        plt.figure(figsize=(n + 1, 5))\\n\\n    plt.xticks(rotation=90, fontsize=15)\\n    ax = sns.countplot(\\n        data=data,\\n        x=feature,\\n        palette=\\\"Paired\\\",\\n        order=data[feature].value_counts().index[:n].sort_values(),\\n    )\\n\\n    for p in ax.patches:\\n        if perc:\\n            label = \\\"{:.1f}%\\\".format(\\n                100 * p.get_height() / total\\n            )  # percentage of each class of the category\\n        else:\\n            label = p.get_height()  # count of each level of the category\\n\\n        x = p.get_x() + p.get_width() / 2  # width of the plot\\n        y = p.get_height()  # height of the plot\\n\\n        ax.annotate(\\n            label,\\n            (x, y),\\n            ha=\\\"center\\\",\\n            va=\\\"center\\\",\\n            size=12,\\n            xytext=(0, 5),\\n            textcoords=\\\"offset points\\\",\\n        )  # annotate the percentage\\n\\n    plt.show()  # show the plot\";\n",
       "                var nbb_formatted_code = \"# function to create labeled barplots\\n\\n\\ndef labeled_barplot(data, feature, perc=False, n=None):\\n    \\\"\\\"\\\"\\n    Barplot with percentage at the top\\n\\n    data: dataframe\\n    feature: dataframe column\\n    perc: whether to display percentages instead of count (default is False)\\n    n: displays the top n category levels (default is None, i.e., display all levels)\\n    \\\"\\\"\\\"\\n\\n    total = len(data[feature])  # length of the column\\n    count = data[feature].nunique()\\n    if n is None:\\n        plt.figure(figsize=(count + 1, 5))\\n    else:\\n        plt.figure(figsize=(n + 1, 5))\\n\\n    plt.xticks(rotation=90, fontsize=15)\\n    ax = sns.countplot(\\n        data=data,\\n        x=feature,\\n        palette=\\\"Paired\\\",\\n        order=data[feature].value_counts().index[:n].sort_values(),\\n    )\\n\\n    for p in ax.patches:\\n        if perc:\\n            label = \\\"{:.1f}%\\\".format(\\n                100 * p.get_height() / total\\n            )  # percentage of each class of the category\\n        else:\\n            label = p.get_height()  # count of each level of the category\\n\\n        x = p.get_x() + p.get_width() / 2  # width of the plot\\n        y = p.get_height()  # height of the plot\\n\\n        ax.annotate(\\n            label,\\n            (x, y),\\n            ha=\\\"center\\\",\\n            va=\\\"center\\\",\\n            size=12,\\n            xytext=(0, 5),\\n            textcoords=\\\"offset points\\\",\\n        )  # annotate the percentage\\n\\n    plt.show()  # show the plot\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# function to create labeled barplots\n",
    "\n",
    "\n",
    "def labeled_barplot(data, feature, perc=False, n=None):\n",
    "    \"\"\"\n",
    "    Barplot with percentage at the top\n",
    "\n",
    "    data: dataframe\n",
    "    feature: dataframe column\n",
    "    perc: whether to display percentages instead of count (default is False)\n",
    "    n: displays the top n category levels (default is None, i.e., display all levels)\n",
    "    \"\"\"\n",
    "\n",
    "    total = len(data[feature])  # length of the column\n",
    "    count = data[feature].nunique()\n",
    "    if n is None:\n",
    "        plt.figure(figsize=(count + 1, 5))\n",
    "    else:\n",
    "        plt.figure(figsize=(n + 1, 5))\n",
    "\n",
    "    plt.xticks(rotation=90, fontsize=15)\n",
    "    ax = sns.countplot(\n",
    "        data=data,\n",
    "        x=feature,\n",
    "        palette=\"Paired\",\n",
    "        order=data[feature].value_counts().index[:n].sort_values(),\n",
    "    )\n",
    "\n",
    "    for p in ax.patches:\n",
    "        if perc:\n",
    "            label = \"{:.1f}%\".format(\n",
    "                100 * p.get_height() / total\n",
    "            )  # percentage of each class of the category\n",
    "        else:\n",
    "            label = p.get_height()  # count of each level of the category\n",
    "\n",
    "        x = p.get_x() + p.get_width() / 2  # width of the plot\n",
    "        y = p.get_height()  # height of the plot\n",
    "\n",
    "        ax.annotate(\n",
    "            label,\n",
    "            (x, y),\n",
    "            ha=\"center\",\n",
    "            va=\"center\",\n",
    "            size=12,\n",
    "            xytext=(0, 5),\n",
    "            textcoords=\"offset points\",\n",
    "        )  # annotate the percentage\n",
    "\n",
    "    plt.show()  # show the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 23;\n",
       "                var nbb_unformatted_code = \"# let's explore discounts further\\nlabeled_barplot(df, \\\"Discount\\\", perc=True)\";\n",
       "                var nbb_formatted_code = \"# let's explore discounts further\\nlabeled_barplot(df, \\\"Discount\\\", perc=True)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's explore discounts further\n",
    "labeled_barplot(df, \"Discount\", perc=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "\n",
    "- 37.6% of products did not have any discounts.\n",
    "- More than 50% of the products have 40-50% discount."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's check for correlations.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 24;\n",
       "                var nbb_unformatted_code = \"plt.figure(figsize=(15, 7))\\nsns.heatmap(df[num_col].corr(), annot=True, vmin=-1, vmax=1, fmt=\\\".2f\\\", cmap=\\\"Spectral\\\")\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"plt.figure(figsize=(15, 7))\\nsns.heatmap(df[num_col].corr(), annot=True, vmin=-1, vmax=1, fmt=\\\".2f\\\", cmap=\\\"Spectral\\\")\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "sns.heatmap(df[num_col].corr(), annot=True, vmin=-1, vmax=1, fmt=\".2f\", cmap=\"Spectral\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "\n",
    "- The sale price and the listing price are highly positively correlated, which is obvious.\n",
    "- Discount is somewhat negatively correlated with listing price or sale price, which indicates that expensive products either have fewer discounts or no discounts at all."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 25;\n",
       "                var nbb_unformatted_code = \"sns.pairplot(data=df[num_col], diag_kind=\\\"kde\\\")\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"sns.pairplot(data=df[num_col], diag_kind=\\\"kde\\\")\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=df[num_col], diag_kind=\"kde\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "\n",
    "- 50-60% discounts are mostly given to products whose listing price is 10,000 or less.\n",
    "- Many products have no discount.\n",
    "- As seen before, most of the products have 100 or fewer reviews."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Listing_Price', 'Sale_Price', 'Discount', 'Rating', 'Reviews']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 26;\n",
       "                var nbb_unformatted_code = \"# variables used for clustering\\nnum_col\";\n",
       "                var nbb_formatted_code = \"# variables used for clustering\\nnum_col\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# variables used for clustering\n",
    "num_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 27;\n",
       "                var nbb_unformatted_code = \"# scaling the dataset before clustering\\nscaler = StandardScaler()\\nsubset = df[num_col].copy()\\nsubset_scaled = scaler.fit_transform(subset)\";\n",
       "                var nbb_formatted_code = \"# scaling the dataset before clustering\\nscaler = StandardScaler()\\nsubset = df[num_col].copy()\\nsubset_scaled = scaler.fit_transform(subset)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scaling the dataset before clustering\n",
    "scaler = StandardScaler()\n",
    "subset = df[num_col].copy()\n",
    "subset_scaled = scaler.fit_transform(subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 28;\n",
       "                var nbb_unformatted_code = \"# creating a dataframe of the scaled columns\\nsubset_scaled_df = pd.DataFrame(subset_scaled, columns=subset.columns)\";\n",
       "                var nbb_formatted_code = \"# creating a dataframe of the scaled columns\\nsubset_scaled_df = pd.DataFrame(subset_scaled, columns=subset.columns)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creating a dataframe of the scaled columns\n",
    "subset_scaled_df = pd.DataFrame(subset_scaled, columns=subset.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Clusters: 1 \tAverage Distortion: 2.0907211006458244\n",
      "Number of Clusters: 2 \tAverage Distortion: 1.71203567523211\n",
      "Number of Clusters: 3 \tAverage Distortion: 1.5554822489540914\n",
      "Number of Clusters: 4 \tAverage Distortion: 1.4451709755028774\n",
      "Number of Clusters: 5 \tAverage Distortion: 1.3083067148001288\n",
      "Number of Clusters: 6 \tAverage Distortion: 1.2302992759867148\n",
      "Number of Clusters: 7 \tAverage Distortion: 1.1629923806047235\n",
      "Number of Clusters: 8 \tAverage Distortion: 1.1008904924660998\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 29;\n",
       "                var nbb_unformatted_code = \"clusters = range(1, 9)\\nmeanDistortions = []\\n\\nfor k in clusters:\\n    model = KMeans(n_clusters=k)\\n    model.fit(subset_scaled_df)\\n    prediction = model.predict(subset_scaled_df)\\n    distortion = (\\n        sum(\\n            np.min(cdist(subset_scaled_df, model.cluster_centers_, \\\"euclidean\\\"), axis=1)\\n        )\\n        / subset_scaled_df.shape[0]\\n    )\\n\\n    meanDistortions.append(distortion)\\n\\n    print(\\\"Number of Clusters:\\\", k, \\\"\\\\tAverage Distortion:\\\", distortion)\\n\\nplt.plot(clusters, meanDistortions, \\\"bx-\\\")\\nplt.xlabel(\\\"k\\\")\\nplt.ylabel(\\\"Average distortion\\\")\\nplt.title(\\\"Selecting k with the Elbow Method\\\")\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"clusters = range(1, 9)\\nmeanDistortions = []\\n\\nfor k in clusters:\\n    model = KMeans(n_clusters=k)\\n    model.fit(subset_scaled_df)\\n    prediction = model.predict(subset_scaled_df)\\n    distortion = (\\n        sum(\\n            np.min(cdist(subset_scaled_df, model.cluster_centers_, \\\"euclidean\\\"), axis=1)\\n        )\\n        / subset_scaled_df.shape[0]\\n    )\\n\\n    meanDistortions.append(distortion)\\n\\n    print(\\\"Number of Clusters:\\\", k, \\\"\\\\tAverage Distortion:\\\", distortion)\\n\\nplt.plot(clusters, meanDistortions, \\\"bx-\\\")\\nplt.xlabel(\\\"k\\\")\\nplt.ylabel(\\\"Average distortion\\\")\\nplt.title(\\\"Selecting k with the Elbow Method\\\")\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusters = range(1, 9)\n",
    "meanDistortions = []\n",
    "\n",
    "for k in clusters:\n",
    "    model = KMeans(n_clusters=k)\n",
    "    model.fit(subset_scaled_df)\n",
    "    prediction = model.predict(subset_scaled_df)\n",
    "    distortion = (\n",
    "        sum(\n",
    "            np.min(cdist(subset_scaled_df, model.cluster_centers_, \"euclidean\"), axis=1)\n",
    "        )\n",
    "        / subset_scaled_df.shape[0]\n",
    "    )\n",
    "\n",
    "    meanDistortions.append(distortion)\n",
    "\n",
    "    print(\"Number of Clusters:\", k, \"\\tAverage Distortion:\", distortion)\n",
    "\n",
    "plt.plot(clusters, meanDistortions, \"bx-\")\n",
    "plt.xlabel(\"k\")\n",
    "plt.ylabel(\"Average distortion\")\n",
    "plt.title(\"Selecting k with the Elbow Method\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Appropriate value for k seems to be 3 or 4.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's check the silhouette scores.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 2, the silhouette score is 0.33477783024143065)\n",
      "For n_clusters = 3, the silhouette score is 0.29744330547812764)\n",
      "For n_clusters = 4, the silhouette score is 0.30575395153576057)\n",
      "For n_clusters = 5, the silhouette score is 0.27039882323588793)\n",
      "For n_clusters = 6, the silhouette score is 0.2813209185631928)\n",
      "For n_clusters = 7, the silhouette score is 0.2908294156005331)\n",
      "For n_clusters = 8, the silhouette score is 0.29281420335577346)\n",
      "For n_clusters = 9, the silhouette score is 0.2860040582312006)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 30;\n",
       "                var nbb_unformatted_code = \"sil_score = []\\ncluster_list = list(range(2, 10))\\nfor n_clusters in cluster_list:\\n    clusterer = KMeans(n_clusters=n_clusters)\\n    preds = clusterer.fit_predict((subset_scaled_df))\\n    # centers = clusterer.cluster_centers_\\n    score = silhouette_score(subset_scaled_df, preds)\\n    sil_score.append(score)\\n    print(\\\"For n_clusters = {}, the silhouette score is {})\\\".format(n_clusters, score))\\n\\nplt.plot(cluster_list, sil_score)\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"sil_score = []\\ncluster_list = list(range(2, 10))\\nfor n_clusters in cluster_list:\\n    clusterer = KMeans(n_clusters=n_clusters)\\n    preds = clusterer.fit_predict((subset_scaled_df))\\n    # centers = clusterer.cluster_centers_\\n    score = silhouette_score(subset_scaled_df, preds)\\n    sil_score.append(score)\\n    print(\\\"For n_clusters = {}, the silhouette score is {})\\\".format(n_clusters, score))\\n\\nplt.plot(cluster_list, sil_score)\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sil_score = []\n",
    "cluster_list = list(range(2, 10))\n",
    "for n_clusters in cluster_list:\n",
    "    clusterer = KMeans(n_clusters=n_clusters)\n",
    "    preds = clusterer.fit_predict((subset_scaled_df))\n",
    "    # centers = clusterer.cluster_centers_\n",
    "    score = silhouette_score(subset_scaled_df, preds)\n",
    "    sil_score.append(score)\n",
    "    print(\"For n_clusters = {}, the silhouette score is {})\".format(n_clusters, score))\n",
    "\n",
    "plt.plot(cluster_list, sil_score)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Silhouette score for 4 is higher than that for 3. So, we will choose 4 as value of k.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Silhouette Plot of KMeans Clustering for 3151 Samples in 3 Centers'}, xlabel='silhouette coefficient values', ylabel='cluster label'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 31;\n",
       "                var nbb_unformatted_code = \"# finding optimal no. of clusters with silhouette coefficients\\nvisualizer = SilhouetteVisualizer(KMeans(3, random_state=1))\\nvisualizer.fit(subset_scaled_df)\\nvisualizer.show()\";\n",
       "                var nbb_formatted_code = \"# finding optimal no. of clusters with silhouette coefficients\\nvisualizer = SilhouetteVisualizer(KMeans(3, random_state=1))\\nvisualizer.fit(subset_scaled_df)\\nvisualizer.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# finding optimal no. of clusters with silhouette coefficients\n",
    "visualizer = SilhouetteVisualizer(KMeans(3, random_state=1))\n",
    "visualizer.fit(subset_scaled_df)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Silhouette Plot of KMeans Clustering for 3151 Samples in 4 Centers'}, xlabel='silhouette coefficient values', ylabel='cluster label'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 32;\n",
       "                var nbb_unformatted_code = \"# finding optimal no. of clusters with silhouette coefficients\\nvisualizer = SilhouetteVisualizer(KMeans(4, random_state=1))\\nvisualizer.fit(subset_scaled_df)\\nvisualizer.show()\";\n",
       "                var nbb_formatted_code = \"# finding optimal no. of clusters with silhouette coefficients\\nvisualizer = SilhouetteVisualizer(KMeans(4, random_state=1))\\nvisualizer.fit(subset_scaled_df)\\nvisualizer.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# finding optimal no. of clusters with silhouette coefficients\n",
    "visualizer = SilhouetteVisualizer(KMeans(4, random_state=1))\n",
    "visualizer.fit(subset_scaled_df)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=4, random_state=0)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 33;\n",
       "                var nbb_unformatted_code = \"# let's take 4 as number of clusters\\nkmeans = KMeans(n_clusters=4, random_state=0)\\nkmeans.fit(subset_scaled_df)\";\n",
       "                var nbb_formatted_code = \"# let's take 4 as number of clusters\\nkmeans = KMeans(n_clusters=4, random_state=0)\\nkmeans.fit(subset_scaled_df)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's take 4 as number of clusters\n",
    "kmeans = KMeans(n_clusters=4, random_state=0)\n",
    "kmeans.fit(subset_scaled_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 34;\n",
       "                var nbb_unformatted_code = \"# adding kmeans cluster labels to the original and scaled dataframes\\n\\ndf[\\\"K_means_segments\\\"] = kmeans.labels_\\nsubset_scaled_df[\\\"K_means_segments\\\"] = kmeans.labels_\";\n",
       "                var nbb_formatted_code = \"# adding kmeans cluster labels to the original and scaled dataframes\\n\\ndf[\\\"K_means_segments\\\"] = kmeans.labels_\\nsubset_scaled_df[\\\"K_means_segments\\\"] = kmeans.labels_\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# adding kmeans cluster labels to the original and scaled dataframes\n",
    "\n",
    "df[\"K_means_segments\"] = kmeans.labels_\n",
    "subset_scaled_df[\"K_means_segments\"] = kmeans.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cluster Profiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 35;\n",
       "                var nbb_unformatted_code = \"cluster_profile = df.groupby(\\\"K_means_segments\\\").mean()\";\n",
       "                var nbb_formatted_code = \"cluster_profile = df.groupby(\\\"K_means_segments\\\").mean()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_profile = df.groupby(\"K_means_segments\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 36;\n",
       "                var nbb_unformatted_code = \"cluster_profile[\\\"count_in_each_segments\\\"] = (\\n    df.groupby(\\\"K_means_segments\\\")[\\\"Sale_Price\\\"].count().values\\n)\";\n",
       "                var nbb_formatted_code = \"cluster_profile[\\\"count_in_each_segments\\\"] = (\\n    df.groupby(\\\"K_means_segments\\\")[\\\"Sale_Price\\\"].count().values\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_profile[\"count_in_each_segments\"] = (\n",
    "    df.groupby(\"K_means_segments\")[\"Sale_Price\"].count().values\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_484bd_row0_col0,#T_484bd_row0_col1,#T_484bd_row0_col3,#T_484bd_row1_col2,#T_484bd_row1_col4,#T_484bd_row1_col5{\n",
       "            background-color:  lightgreen;\n",
       "        }</style><table id=\"T_484bd_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Listing_Price</th>        <th class=\"col_heading level0 col1\" >Sale_Price</th>        <th class=\"col_heading level0 col2\" >Discount</th>        <th class=\"col_heading level0 col3\" >Rating</th>        <th class=\"col_heading level0 col4\" >Reviews</th>        <th class=\"col_heading level0 col5\" >count_in_each_segments</th>    </tr>    <tr>        <th class=\"index_name level0\" >K_means_segments</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_484bd_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_484bd_row0_col0\" class=\"data row0 col0\" >15541.586283</td>\n",
       "                        <td id=\"T_484bd_row0_col1\" class=\"data row0 col1\" >13406.792035</td>\n",
       "                        <td id=\"T_484bd_row0_col2\" class=\"data row0 col2\" >8.761062</td>\n",
       "                        <td id=\"T_484bd_row0_col3\" class=\"data row0 col3\" >3.926327</td>\n",
       "                        <td id=\"T_484bd_row0_col4\" class=\"data row0 col4\" >31.869469</td>\n",
       "                        <td id=\"T_484bd_row0_col5\" class=\"data row0 col5\" >452</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_484bd_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_484bd_row1_col0\" class=\"data row1 col0\" >6801.354260</td>\n",
       "                        <td id=\"T_484bd_row1_col1\" class=\"data row1 col1\" >3686.673206</td>\n",
       "                        <td id=\"T_484bd_row1_col2\" class=\"data row1 col2\" >45.465247</td>\n",
       "                        <td id=\"T_484bd_row1_col3\" class=\"data row1 col3\" >3.390863</td>\n",
       "                        <td id=\"T_484bd_row1_col4\" class=\"data row1 col4\" >50.173206</td>\n",
       "                        <td id=\"T_484bd_row1_col5\" class=\"data row1 col5\" >1784</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_484bd_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_484bd_row2_col0\" class=\"data row2 col0\" >6498.135802</td>\n",
       "                        <td id=\"T_484bd_row2_col1\" class=\"data row2 col1\" >6159.969136</td>\n",
       "                        <td id=\"T_484bd_row2_col2\" class=\"data row2 col2\" >1.018519</td>\n",
       "                        <td id=\"T_484bd_row2_col3\" class=\"data row2 col3\" >3.797685</td>\n",
       "                        <td id=\"T_484bd_row2_col4\" class=\"data row2 col4\" >38.438272</td>\n",
       "                        <td id=\"T_484bd_row2_col5\" class=\"data row2 col5\" >648</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_484bd_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_484bd_row3_col0\" class=\"data row3 col0\" >9725.996255</td>\n",
       "                        <td id=\"T_484bd_row3_col1\" class=\"data row3 col1\" >8331.101124</td>\n",
       "                        <td id=\"T_484bd_row3_col2\" class=\"data row3 col2\" >7.715356</td>\n",
       "                        <td id=\"T_484bd_row3_col3\" class=\"data row3 col3\" >0.255805</td>\n",
       "                        <td id=\"T_484bd_row3_col4\" class=\"data row3 col4\" >11.898876</td>\n",
       "                        <td id=\"T_484bd_row3_col5\" class=\"data row3 col5\" >267</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x288b7745b50>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 37;\n",
       "                var nbb_unformatted_code = \"# let's display cluster profiles\\ncluster_profile.style.highlight_max(color=\\\"lightgreen\\\", axis=0)\";\n",
       "                var nbb_formatted_code = \"# let's display cluster profiles\\ncluster_profile.style.highlight_max(color=\\\"lightgreen\\\", axis=0)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's display cluster profiles\n",
    "cluster_profile.style.highlight_max(color=\"lightgreen\", axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 38;\n",
       "                var nbb_unformatted_code = \"fig, axes = plt.subplots(1, 5, figsize=(16, 6))\\nfig.suptitle(\\\"Boxplot of scaled numerical variables for each cluster\\\", fontsize=20)\\ncounter = 0\\nfor ii in range(5):\\n    sns.boxplot(\\n        ax=axes[ii],\\n        y=subset_scaled_df[num_col[counter]],\\n        x=subset_scaled_df[\\\"K_means_segments\\\"],\\n    )\\n    counter = counter + 1\\n\\nfig.tight_layout(pad=2.0)\";\n",
       "                var nbb_formatted_code = \"fig, axes = plt.subplots(1, 5, figsize=(16, 6))\\nfig.suptitle(\\\"Boxplot of scaled numerical variables for each cluster\\\", fontsize=20)\\ncounter = 0\\nfor ii in range(5):\\n    sns.boxplot(\\n        ax=axes[ii],\\n        y=subset_scaled_df[num_col[counter]],\\n        x=subset_scaled_df[\\\"K_means_segments\\\"],\\n    )\\n    counter = counter + 1\\n\\nfig.tight_layout(pad=2.0)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 5, figsize=(16, 6))\n",
    "fig.suptitle(\"Boxplot of scaled numerical variables for each cluster\", fontsize=20)\n",
    "counter = 0\n",
    "for ii in range(5):\n",
    "    sns.boxplot(\n",
    "        ax=axes[ii],\n",
    "        y=subset_scaled_df[num_col[counter]],\n",
    "        x=subset_scaled_df[\"K_means_segments\"],\n",
    "    )\n",
    "    counter = counter + 1\n",
    "\n",
    "fig.tight_layout(pad=2.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 39;\n",
       "                var nbb_unformatted_code = \"fig, axes = plt.subplots(1, 5, figsize=(16, 6))\\nfig.suptitle(\\\"Boxplot of original numerical variables for each cluster\\\", fontsize=20)\\ncounter = 0\\nfor ii in range(5):\\n    sns.boxplot(ax=axes[ii], y=df[num_col[counter]], x=df[\\\"K_means_segments\\\"])\\n    counter = counter + 1\\n\\nfig.tight_layout(pad=2.0)\";\n",
       "                var nbb_formatted_code = \"fig, axes = plt.subplots(1, 5, figsize=(16, 6))\\nfig.suptitle(\\\"Boxplot of original numerical variables for each cluster\\\", fontsize=20)\\ncounter = 0\\nfor ii in range(5):\\n    sns.boxplot(ax=axes[ii], y=df[num_col[counter]], x=df[\\\"K_means_segments\\\"])\\n    counter = counter + 1\\n\\nfig.tight_layout(pad=2.0)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 5, figsize=(16, 6))\n",
    "fig.suptitle(\"Boxplot of original numerical variables for each cluster\", fontsize=20)\n",
    "counter = 0\n",
    "for ii in range(5):\n",
    "    sns.boxplot(ax=axes[ii], y=df[num_col[counter]], x=df[\"K_means_segments\"])\n",
    "    counter = counter + 1\n",
    "\n",
    "fig.tight_layout(pad=2.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Insights\n",
    "\n",
    "- **Cluster 0**:\n",
    "    - This cluster contains more expensive premium products, with an average listing price of approximately 15500.\n",
    "    - Almost no discount is given to products in this cluster.\n",
    "    - The median rating of this cluster is around 4 suggesting that customers are very satisfied with 50% of the products in this cluster.\n",
    "    - Products in these clusters have got lesser reviews, which can be because these products are very expensive and fewer people buy them.\n",
    "   \n",
    "- **Cluster 1**:\n",
    "    - Listing price is low for products in this cluster, and the sale price is lower, as there are heavier discounts on these products.\n",
    "    - 50% of the products have discounts between 30% to 40%.\n",
    "    - More than 50% of products have a rating from 2.5 to 4.5 which indicates that customer satisfaction varies across products in this cluster.\n",
    "    - On average, 50 reviews are given to products in this cluster.\n",
    "    \n",
    "- **Cluster 2**:\n",
    "    - The listing price of products in this cluster is similar to those in Cluster 1, but the sale price is comparatively higher as there are almost no discounts.\n",
    "    - On average, 38 reviews are given to products in this cluster.\n",
    "\n",
    "\n",
    "- **Cluster 3**:\n",
    "    - Listing and sale price of products in this cluster suggest that these are medium-range products.\n",
    "    - Almost no discount is given to products in this cluster.\n",
    "    - Products in this cluster have a low rating or no rating at all."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's compare Cluster vs Brand**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_69cb8_row0_col4,#T_69cb8_row1_col0,#T_69cb8_row1_col1,#T_69cb8_row1_col2,#T_69cb8_row1_col3{\n",
       "            background-color:  lightgreen;\n",
       "        }</style><table id=\"T_69cb8_\" ><thead>    <tr>        <th class=\"index_name level0\" >Brand</th>        <th class=\"col_heading level0 col0\" >Adidas Adidas ORIGINALS</th>        <th class=\"col_heading level0 col1\" >Adidas CORE / NEO</th>        <th class=\"col_heading level0 col2\" >Adidas ORIGINALS</th>        <th class=\"col_heading level0 col3\" >Adidas SPORT PERFORMANCE</th>        <th class=\"col_heading level0 col4\" >Nike</th>    </tr>    <tr>        <th class=\"index_name level0\" >K_means_segments</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_69cb8_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_69cb8_row0_col0\" class=\"data row0 col0\" >0</td>\n",
       "                        <td id=\"T_69cb8_row0_col1\" class=\"data row0 col1\" >0</td>\n",
       "                        <td id=\"T_69cb8_row0_col2\" class=\"data row0 col2\" >135</td>\n",
       "                        <td id=\"T_69cb8_row0_col3\" class=\"data row0 col3\" >128</td>\n",
       "                        <td id=\"T_69cb8_row0_col4\" class=\"data row0 col4\" >189</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_69cb8_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_69cb8_row1_col0\" class=\"data row1 col0\" >1</td>\n",
       "                        <td id=\"T_69cb8_row1_col1\" class=\"data row1 col1\" >864</td>\n",
       "                        <td id=\"T_69cb8_row1_col2\" class=\"data row1 col2\" >563</td>\n",
       "                        <td id=\"T_69cb8_row1_col3\" class=\"data row1 col3\" >356</td>\n",
       "                        <td id=\"T_69cb8_row1_col4\" class=\"data row1 col4\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_69cb8_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_69cb8_row2_col0\" class=\"data row2 col0\" >0</td>\n",
       "                        <td id=\"T_69cb8_row2_col1\" class=\"data row2 col1\" >229</td>\n",
       "                        <td id=\"T_69cb8_row2_col2\" class=\"data row2 col2\" >155</td>\n",
       "                        <td id=\"T_69cb8_row2_col3\" class=\"data row2 col3\" >99</td>\n",
       "                        <td id=\"T_69cb8_row2_col4\" class=\"data row2 col4\" >165</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_69cb8_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_69cb8_row3_col0\" class=\"data row3 col0\" >0</td>\n",
       "                        <td id=\"T_69cb8_row3_col1\" class=\"data row3 col1\" >18</td>\n",
       "                        <td id=\"T_69cb8_row3_col2\" class=\"data row3 col2\" >53</td>\n",
       "                        <td id=\"T_69cb8_row3_col3\" class=\"data row3 col3\" >23</td>\n",
       "                        <td id=\"T_69cb8_row3_col4\" class=\"data row3 col4\" >173</td>\n",
       "            </tr>\n",
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    "**Observations**\n",
    "\n",
    "- Adidas gives more discounts on their products, while Nike barely gives any.\n",
    "- Nearly one-third of Nike products are premium in nature."
   ]
  },
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    "## Business Recommendations\n",
    "\n",
    "- Cluster 0 consists of premium products, so focused marketing campaigns can be done to target customers with higher purchasing power.\n",
    "- Cluster 0 products can also be made available in showrooms situated in premium locations because people generally tend to buy premium products from showrooms.\n",
    "- Cluster 1 consists of products that are sold at higher discounts, so these might be the ones that are introduced in large quantity during festive sales (like Christmas, Black Friday, New Year, etc). This strategy can be continued with high inventory.\n",
    "- Cluster 2 and 3 consists of products with comparatively low and medium prices with almost no discount, so general marketing campaigns can be done for these to target the general public."
   ]
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