From e84f54371ff76c743825662c5a261733906396a6 Mon Sep 17 00:00:00 2001
From: PaulinaMamiaga <228375023+PaulinaMamiaga@users.noreply.github.com>
Date: Tue, 7 Apr 2026 03:02:34 +0200
Subject: [PATCH 1/3] # Create lab_univariate_solution.ipynb

---
 lab_eda_univariate_solution.ipynb | 845 ++++++++++++++++++++++++++++++
 1 file changed, 845 insertions(+)
 create mode 100644 lab_eda_univariate_solution.ipynb

diff --git a/lab_eda_univariate_solution.ipynb b/lab_eda_univariate_solution.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e7abf799",
+   "metadata": {},
+   "source": [
+    "# **Amazon UK Product Analysis - Univariate EDA**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed0caabe",
+   "metadata": {},
+   "source": [
+    "#### **By: Paulina Mamiaga**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2631ade",
+   "metadata": {},
+   "source": [
+    "----------------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fef8890",
+   "metadata": {},
+   "source": [
+    "## **1. Objective**\n",
+    "\n",
+    "The objective of this analysis is to explore and understand the structure and distribution of product listings on Amazon UK through univariate exploratory data analysis (EDA).\n",
+    "\n",
+    "This study focuses on key product attributes such as category, price, and customer ratings in order to:\n",
+    "\n",
+    "- Identify the most represented product categories and assess market concentration\n",
+    "- Analyze pricing patterns, including central tendencies and variability\n",
+    "- Understand customer rating behavior and detect potential biases or trends\n",
+    "\n",
+    "From a business perspective, this analysis aims to provide insights that can support product positioning, pricing strategies, and inventory decisions within a competitive e-commerce environment."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "022d6d7c",
+   "metadata": {},
+   "source": [
+    "## **2. Part 1: Understanding Product Categories**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "01081a25",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Project root: c:\\Users\\pauli\\OneDrive\\Documentos\\GitHub\\lab-eda-univariate\n",
+      "data/raw exists: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set up the environment and load the dataset\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "\n",
+    "current_dir = os.getcwd()\n",
+    "\n",
+    "\n",
+    "while not os.path.exists(os.path.join(current_dir, \"data\", \"raw\")):\n",
+    "    parent = os.path.dirname(current_dir)\n",
+    "    if parent == current_dir:\n",
+    "        raise FileNotFoundError(\"Project root with data/raw not found\")\n",
+    "    current_dir = parent\n",
+    "\n",
+    "\n",
+    "os.chdir(current_dir)\n",
+    "\n",
+    "print(\"Project root:\", os.getcwd())\n",
+    "print(\"data/raw exists:\", os.path.exists(\"data/raw\"))\n",
+    "\n",
+    "\n",
+    "pd.set_option(\"display.max_columns\", None)\n",
+    "sns.set_style(\"whitegrid\")\n",
+    "plt.rcParams[\"figure.figsize\"] = (8, 5)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5020e29e",
+   "metadata": {},
+   "source": [
+    "df = pd.read_csv(\"data/raw/train_v2.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "26547248",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 2443651 entries, 0 to 2443650\n",
+      "Data columns (total 9 columns):\n",
+      " #   Column             Dtype  \n",
+      "---  ------             -----  \n",
+      " 0   uid                int64  \n",
+      " 1   asin               object \n",
+      " 2   title              object \n",
+      " 3   stars              float64\n",
+      " 4   reviews            int64  \n",
+      " 5   price              float64\n",
+      " 6   isBestSeller       bool   \n",
+      " 7   boughtInLastMonth  int64  \n",
+      " 8   category           object \n",
+      "dtypes: bool(1), float64(2), int64(3), object(3)\n",
+      "memory usage: 151.5+ MB\n"
+     ]
+    },
+    {
+     "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>uid</th>\n",
+       "      <th>asin</th>\n",
+       "      <th>title</th>\n",
+       "      <th>stars</th>\n",
+       "      <th>reviews</th>\n",
+       "      <th>price</th>\n",
+       "      <th>isBestSeller</th>\n",
+       "      <th>boughtInLastMonth</th>\n",
+       "      <th>category</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>B09B96TG33</td>\n",
+       "      <td>Echo Dot (5th generation, 2022 release) | Big ...</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>15308</td>\n",
+       "      <td>21.99</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Hi-Fi Speakers</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>B01HTH3C8S</td>\n",
+       "      <td>Anker Soundcore mini, Super-Portable Bluetooth...</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>98099</td>\n",
+       "      <td>23.99</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Hi-Fi Speakers</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>B09B8YWXDF</td>\n",
+       "      <td>Echo Dot (5th generation, 2022 release) | Big ...</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>15308</td>\n",
+       "      <td>21.99</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Hi-Fi Speakers</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>B09B8T5VGV</td>\n",
+       "      <td>Echo Dot with clock (5th generation, 2022 rele...</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>7205</td>\n",
+       "      <td>31.99</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Hi-Fi Speakers</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>B09WX6QD65</td>\n",
+       "      <td>Introducing Echo Pop | Full sound compact Wi-F...</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>1881</td>\n",
+       "      <td>17.99</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Hi-Fi Speakers</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   uid        asin                                              title  stars  \\\n",
+       "0    1  B09B96TG33  Echo Dot (5th generation, 2022 release) | Big ...    4.7   \n",
+       "1    2  B01HTH3C8S  Anker Soundcore mini, Super-Portable Bluetooth...    4.7   \n",
+       "2    3  B09B8YWXDF  Echo Dot (5th generation, 2022 release) | Big ...    4.7   \n",
+       "3    4  B09B8T5VGV  Echo Dot with clock (5th generation, 2022 rele...    4.7   \n",
+       "4    5  B09WX6QD65  Introducing Echo Pop | Full sound compact Wi-F...    4.6   \n",
+       "\n",
+       "   reviews  price  isBestSeller  boughtInLastMonth        category  \n",
+       "0    15308  21.99         False                  0  Hi-Fi Speakers  \n",
+       "1    98099  23.99          True                  0  Hi-Fi Speakers  \n",
+       "2    15308  21.99         False                  0  Hi-Fi Speakers  \n",
+       "3     7205  31.99         False                  0  Hi-Fi Speakers  \n",
+       "4     1881  17.99         False                  0  Hi-Fi Speakers  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the dataset\n",
+    "\n",
+    "df = pd.read_csv(\"data/raw/amz_uk_price_prediction_dataset.csv\")  # Replace with your actual dataset path\n",
+    "\n",
+    "df.info()\n",
+    "df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "d439670b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Missing:\n",
+      "uid                  0\n",
+      "asin                 0\n",
+      "title                0\n",
+      "stars                0\n",
+      "reviews              0\n",
+      "price                0\n",
+      "isBestSeller         0\n",
+      "boughtInLastMonth    0\n",
+      "category             0\n",
+      "dtype: int64\n",
+      "\n",
+      "Duplicate products in df:\n",
+      "220909\n",
+      "\n",
+      "Unique products in df:\n",
+      "2222742\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check for missing values and duplicates\n",
+    "print(\"\\nMissing:\")\n",
+    "print(df.isnull().sum())\n",
+    "print(\"\\nDuplicate products in df:\")\n",
+    "print(df[\"asin\"].duplicated().sum())\n",
+    "print(\"\\nUnique products in df:\")\n",
+    "print(df[\"asin\"].nunique())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "34b2f865",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Duplicate products in df:\n",
+      "0\n",
+      "\n",
+      "Unique products in df:\n",
+      "2443651\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check for duplicates using 'uid' instead of 'asin'\n",
+    "print(\"\\nDuplicate products in df:\")\n",
+    "print(df[\"uid\"].duplicated().sum())\n",
+    "print(\"\\nUnique products in df:\")\n",
+    "print(df[\"uid\"].nunique())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afc1d742",
+   "metadata": {},
+   "source": [
+    "### **2.1.1. Frequency Table**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "40843aee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "category\n",
+       "Sports & Outdoors                         836265\n",
+       "Beauty                                     19312\n",
+       "Handmade Clothing, Shoes & Accessories     19229\n",
+       "Bath & Body                                19092\n",
+       "Birthday Gifts                             18978\n",
+       "Manicure & Pedicure Products               18940\n",
+       "Skin Care                                  18769\n",
+       "Make-up                                    18756\n",
+       "Hair Care                                  18735\n",
+       "Fragrances                                 18564\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "category_counts = df['category'].value_counts()\n",
+    "\n",
+    "category_counts.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "7ce3d65b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "top10 = category_counts.head(10)\n",
+    "\n",
+    "plt.figure(figsize=(10,6))\n",
+    "sns.barplot(x=top10.values, y=top10.index)\n",
+    "plt.title(\"Top 10 Product Categories\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "126c7cb7",
+   "metadata": {},
+   "source": [
+    "### **2.1.2. Top 5 categories**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "6d210896",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                 category   count\n",
+      "0                       Sports & Outdoors  836265\n",
+      "1                                  Beauty   19312\n",
+      "2  Handmade Clothing, Shoes & Accessories   19229\n",
+      "3                             Bath & Body   19092\n",
+      "4                          Birthday Gifts   18978\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create table withe the top 5 categories and their counts\n",
+    "top5_df = top5.reset_index()\n",
+    "top5_df.columns = ['category', 'count']\n",
+    "\n",
+    "print(top5_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "8be9bbaf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "top5.plot.pie(autopct='%1.1f%%')\n",
+    "plt.title(\"Top 5 Categories Distribution\")\n",
+    "plt.ylabel(\"\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77d0b035",
+   "metadata": {},
+   "source": [
+    "## **3. Part 2: Delving into Product Pricing**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae276301",
+   "metadata": {},
+   "source": [
+    "### **3.2.1. Centrality**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "6d255af0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Price: 89.24380943923663\n",
+      "Median Price: 19.09\n",
+      "Mode Price: 9.99\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mean_price = df['price'].mean()\n",
+    "median_price = df['price'].median()\n",
+    "mode_price = df['price'].mode()[0]\n",
+    "\n",
+    "mean_price, median_price, mode_price\n",
+    "\n",
+    "print(f\"Mean Price: {mean_price}\")\n",
+    "print(f\"Median Price: {median_price}\")\n",
+    "print(f\"Mode Price: {mode_price}\")\n",
+    "\n",
+    "plt.figure(figsize=(10,6))\n",
+    "sns.histplot(df['price'], bins=30, kde=True)\n",
+    "plt.title(\"Price Distribution\")\n",
+    "plt.xlabel(\"Price\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "000c7bd0",
+   "metadata": {},
+   "source": [
+    "### **3.2.2. Dispersion**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "42e92849",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Variance: 119445.48532256528\n",
+      "Standard Deviation: 345.60886175352226\n",
+      "Range: 100000.0\n",
+      "IQR: 36.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "variance = df['price'].var()\n",
+    "std = df['price'].std()\n",
+    "price_range = df['price'].max() - df['price'].min()\n",
+    "iqr = df['price'].quantile(0.75) - df['price'].quantile(0.25)\n",
+    "\n",
+    "variance, std, price_range, iqr\n",
+    "\n",
+    "print(f\"Variance: {variance}\")\n",
+    "print(f\"Standard Deviation: {std}\")\n",
+    "print(f\"Range: {price_range}\")\n",
+    "print(f\"IQR: {iqr}\")\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5ca6b22",
+   "metadata": {},
+   "source": [
+    "### **3.2.3. Histogram**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "cbe33c3c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8,5))\n",
+    "sns.histplot(df['price'], bins=50)\n",
+    "plt.title(\"Price Distribution\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56fa7503",
+   "metadata": {},
+   "source": [
+    "### **3.2.4. Boxplot**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "35dbc51e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='price'>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x=df['price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a560149b",
+   "metadata": {},
+   "source": [
+    "## **4. Part 3: Unpacking Product Ratings**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e0c417e",
+   "metadata": {},
+   "source": [
+    "### **4.3.1 Centrality**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "5ad114d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Stars: 2.152836472966065\n",
+      "Median Stars: 0.0\n",
+      "Mode Stars: 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['stars'].mean()\n",
+    "df['stars'].median()\n",
+    "df['stars'].mode()\n",
+    "\n",
+    "print(f\"Mean Stars: {df['stars'].mean()}\")\n",
+    "print(f\"Median Stars: {df['stars'].median()}\")\n",
+    "print(f\"Mode Stars: {df['stars'].mode()[0]}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "1ce84ba4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Rating: 2.152836472966065\n",
+      "Median Rating: 0.0\n",
+      "Mode Rating: 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "mean_rating = df['stars'].mean()\n",
+    "median_rating = df['stars'].median()\n",
+    "mode_rating = df['stars'].mode()[0]\n",
+    "\n",
+    "mean_rating, median_rating, mode_rating\n",
+    "\n",
+    "print(f\"Mean Rating: {mean_rating}\")\n",
+    "print(f\"Median Rating: {median_rating}\")\n",
+    "print(f\"Mode Rating: {mode_rating}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86157e5f",
+   "metadata": {},
+   "source": [
+    "### **4.3.2. Dispersion**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "5322a019",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Standard Deviation of Stars: 2.1948653785054697\n",
+      "Variance of Stars: 4.81743402976196\n",
+      "IQR of Stars: 4.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['stars'].std()\n",
+    "df['stars'].var()\n",
+    "\n",
+    "iqr_rating = df['stars'].quantile(0.75) - df['stars'].quantile(0.25)\n",
+    "\n",
+    "print(f\"Standard Deviation of Stars: {df['stars'].std()}\")\n",
+    "print(f\"Variance of Stars: {df['stars'].var()}\")\n",
+    "print(f\"IQR of Stars: {iqr_rating}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32001cf6",
+   "metadata": {},
+   "source": [
+    "### **3.4.3. Shape**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "ed09eeec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Skewness of Stars: 0.08120735761080985\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['stars'].skew()\n",
+    "df['stars'].kurt()\n",
+    "\n",
+    "print(f\"Skewness of Stars: {df['stars'].skew()}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8aa48c5f",
+   "metadata": {},
+   "source": [
+    "### **4.3.4. Histogram**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "2178a97d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(df['stars'], bins=20)\n",
+    "plt.title(\"Rating Distribution\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "01db6630",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    2.443651e+06\n",
+       "mean     2.152836e+00\n",
+       "std      2.194865e+00\n",
+       "min      0.000000e+00\n",
+       "25%      0.000000e+00\n",
+       "50%      0.000000e+00\n",
+       "75%      4.400000e+00\n",
+       "max      5.000000e+00\n",
+       "Name: stars, dtype: float64"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['stars'].describe()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 88ffdf5472461a151a5ce0c7c577687b9cbc5b25 Mon Sep 17 00:00:00 2001
From: PaulinaMamiaga <228375023+PaulinaMamiaga@users.noreply.github.com>
Date: Tue, 7 Apr 2026 03:18:11 +0200
Subject: [PATCH 2/3] # Upda lab_eda_univariate_solution.ipynb

---
 lab_eda_univariate_solution.ipynb | 94 +++++++++++++++++++++++++++++++
 1 file changed, 94 insertions(+)

diff --git a/lab_eda_univariate_solution.ipynb b/lab_eda_univariate_solution.ipynb
index e455b74..ab58e7f 100644
--- a/lab_eda_univariate_solution.ipynb
+++ b/lab_eda_univariate_solution.ipynb
@@ -454,6 +454,38 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "6231c521",
+   "metadata": {},
+   "source": [
+    "**Insight**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63e48573",
+   "metadata": {},
+   "source": [
+    "The distribution of product categories reveals an extreme concentration in a single dominant category: **Sports & Outdoors**, which accounts for the vast majority of listings.\n",
+    "\n",
+    "- This category alone represents over 90% of the top 5 categories distribution.\n",
+    "- The remaining categories (Beauty, Bath & Body, etc.) each contribute only a marginal share (~2% each).\n",
+    "\n",
+    "**Interpretation:**\n",
+    "The marketplace is highly skewed, suggesting either:\n",
+    "- A dataset bias (over-representation of one category), or\n",
+    "- A strong supply concentration in specific verticals.\n",
+    "\n",
+    "**Business Implication:**\n",
+    "- Competing in dominant categories may require strong differentiation due to saturation.\n",
+    "- Niche categories could represent **untapped opportunities** with lower competition.\n",
+    "- Category imbalance may distort global insights if not segmented properly.\n",
+    "\n",
+    "**Recommendation:**\n",
+    "Future analysis should segment results by category to avoid misleading conclusions driven by this dominance."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "77d0b035",
@@ -626,6 +658,37 @@
     "sns.boxplot(x=df['price'])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "1ffe12e4",
+   "metadata": {},
+   "source": [
+    "**Insight**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d76db34",
+   "metadata": {},
+   "source": [
+    "**Visual (Histogram & Boxplot):**\n",
+    "- Dense concentration at low prices\n",
+    "- Long tail of high-value products\n",
+    "- Clear presence of outliers (luxury or mispriced items)\n",
+    "\n",
+    "**Business Implication:**\n",
+    "- The marketplace operates primarily in a **low-price, high-volume model**\n",
+    "- Premium products exist but are rare and may follow a different strategy\n",
+    "\n",
+    "**Key Risk:**\n",
+    "Using mean price for decision-making would be misleading\n",
+    "\n",
+    "**Recommendation:**\n",
+    "- Use **median and IQR** for pricing strategy\n",
+    "- Segment pricing by category for more actionable insights\n",
+    "- Consider removing or capping outliers for modeling purposes"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "a560149b",
@@ -819,6 +882,37 @@
    "source": [
     "df['stars'].describe()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ecf6ec73",
+   "metadata": {},
+   "source": [
+    "**Insight**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47f16177",
+   "metadata": {},
+   "source": [
+    "Customer satisfaction is actually **high among reviewed products**, but:\n",
+    "- Many products have **no customer feedback yet**\n",
+    "\n",
+    "**Business Implication:**\n",
+    "- The dataset mixes **rated vs non-rated products**, which biases analysis\n",
+    "- Average rating (~2.15) is misleading and not representative\n",
+    "\n",
+    "**Recommendation:**\n",
+    "- Separate analysis into:\n",
+    "  1. Products with ratings > 0\n",
+    "  2. Products with no ratings\n",
+    "- Use ratings only for products with reviews to avoid bias\n",
+    "\n",
+    "**Strategic Insight:**\n",
+    "- Products with no ratings represent **growth opportunity**\n",
+    "- Early reviews could significantly impact product success"
+   ]
   }
  ],
  "metadata": {

From 3b5118c1368eb142eee534de386117c228130cc9 Mon Sep 17 00:00:00 2001
From: PaulinaMamiaga <228375023+PaulinaMamiaga@users.noreply.github.com>
Date: Tue, 7 Apr 2026 03:24:57 +0200
Subject: [PATCH 3/3] # Create lab_eda_univariate_insights.md

---
 data/raw/lab_eda_univariate_insitghs.md | 179 ++++++++++++++++++++++++
 1 file changed, 179 insertions(+)
 create mode 100644 data/raw/lab_eda_univariate_insitghs.md

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+# 📊 Amazon UK Product Analysis – Univariate EDA
+
+**By:** Paulina Mamiaga  
+
+---
+
+## 📌 Objective
+
+The objective of this analysis is to explore and understand the structure and distribution of product listings on Amazon UK through univariate exploratory data analysis (EDA).
+
+This study focuses on key product attributes such as **category, price, and customer ratings** in order to:
+
+- Identify the most represented product categories and assess market concentration  
+- Analyze pricing patterns, including central tendencies and variability  
+- Understand customer rating behavior and detect potential biases or trends  
+
+From a business perspective, this analysis aims to generate insights that support **product positioning, pricing strategies, and inventory decisions** in a competitive e-commerce environment.
+
+---
+
+## 📂 Data Source
+
+Dataset used:  
+👉 https://www.kaggle.com/datasets/asaniczka/uk-optimal-product-price-prediction/
+
+---
+
+# 🧠 Part 1: Understanding Product Categories
+
+## 🔍 Key Findings
+
+- The dataset shows **extreme concentration in a single category: _Sports & Outdoors_**
+- This category dominates the dataset, representing the vast majority of listings
+- Other categories (Beauty, Bath & Body, etc.) contribute only marginal shares (~2% each)
+
+## 💡 Business Insights
+
+👉 **Market Concentration Risk**
+- The dataset is highly skewed → global insights may be misleading  
+- Results are heavily driven by one dominant category  
+
+👉 **Competitive Dynamics**
+- High concentration → high competition  
+- Smaller categories → potential niche opportunities  
+
+👉 **Recommendation**
+- Always segment analysis by category  
+- Identify underrepresented categories for strategic positioning  
+
+---
+
+# 💰 Part 2: Product Pricing Analysis
+
+## 📊 Central Tendency
+
+- Mean price: **£89.24**
+- Median price: **£19.09**
+- Mode price: **£9.99**
+
+## 📊 Dispersion
+
+- Standard deviation: **345.61**
+- Range: **£0 – £100,000**
+- IQR: **£36**
+
+## 🔍 Key Findings
+
+- Strong gap between mean and median → **right-skewed distribution**
+- Majority of products are low-priced
+- Presence of extreme high-value outliers
+
+## 💡 Business Insights
+
+👉 **Skewed Pricing Structure**
+- Marketplace operates in a **low-price, high-volume model**
+
+👉 **Mean is Misleading**
+- Average price is inflated by outliers  
+- Median better represents typical product pricing  
+
+👉 **Outliers**
+- Extreme values distort analysis  
+- Likely luxury or incorrectly priced products  
+
+👉 **Recommendation**
+- Use median & IQR for pricing strategy  
+- Segment pricing by category  
+- Consider removing or capping outliers  
+
+---
+
+# ⭐ Part 3: Product Ratings Analysis
+
+## 📊 Centrality
+
+- Mean rating: **2.15**
+- Median rating: **0.0**
+- Mode rating: **0.0**
+
+## 📊 Dispersion
+
+- Standard deviation: **2.19**
+- IQR: **4.4**
+
+## 🔍 Key Findings
+
+- 50% of products have rating = 0  
+- Sharp jump from 0 → 4.4 (75th percentile)  
+- Ratings are not normally distributed  
+
+## 💡 Business Insights
+
+👉 **Critical Interpretation**
+- Rating = 0 likely means **no reviews**, not poor quality  
+
+👉 **Hidden Structure**
+Two groups exist:
+1. Products without ratings  
+2. Products with high ratings (4–5 stars)  
+
+👉 **Misleading Average**
+- Mean rating underestimates real customer satisfaction  
+
+👉 **Business Opportunity**
+- Many products lack reviews → opportunity to influence perception early  
+
+👉 **Recommendation**
+- Separate analysis:
+  - Rated products (stars > 0)
+  - Unrated products (stars = 0)  
+
+---
+
+# 🚀 Overall Business Insights
+
+The Amazon UK marketplace shows strong structural imbalances:
+
+### 1. Category Concentration
+- One category dominates the dataset  
+- Risk of biased insights  
+
+### 2. Price Skewness
+- Most products are low-priced  
+- Few outliers distort averages  
+
+### 3. Rating Bias
+- Many products have no ratings  
+- Customer satisfaction is higher than it appears  
+
+---
+
+## 🎯 Final Takeaways
+
+- Always segment before analyzing  
+- Median > Mean for pricing decisions  
+- Ratings must be interpreted carefully  
+- Unrated products = growth opportunity  
+
+---
+
+## 🛠️ Tech Stack
+
+- Python (Pandas, NumPy)
+- Matplotlib / Seaborn
+- Jupyter Notebook  
+
+---
+
+## 📎 Notes
+
+- No missing values detected  
+- Duplicate products exist at ASIN level  
+- Large dataset (~2.4M rows) ensures robustness  
+
+------
+
+## 🌐 Data Source
+
+[https://www.kaggle.com/datasets/asaniczka/uk-optimal-product-price-prediction/]
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