About Dataset

This dataset contains detailed information about phones listed on Amazon, including product specifications, user reviews, ratings, and pricing. The dataset can be useful for analyzing product trends, consumer preferences, pricing strategies, and technical features of smartphones sold on the platform. It includes both new and Amazon-renewed phones.

Description

The dataset includes the following key features:

• Color: The available color of the phone.
• Image Links: URLs to the images of the products.
• Descriptions: Detailed descriptions of the phone, including specifications.
• Kind Product: The type or category of the product (smartphones, accessories, etc.).
• Ratings: User ratings (out of 5 stars).
• Number of Ratings: Total count of ratings the product has received.
• Status: Availability status (e.g., In Stock, Out of Stock).
• Number of Buyers Last Month More Than: Approximate number of buyers in the previous month.
• Typical Price: The regular price with usd of the phone without any discounts.
• Price: The current price with usd of the phone.
• You Save: The amount saved if the phone is on discount.
• Discount: The percentage discount offered on the product.
• Brand: The brand name of the phone (e.g., Apple, Samsung).
• OS: The operating system of the phone (e.g., Android, iOS).
• CPU Model: The model of the processor used in the phone.
• Resolution: The screen resolution of the phone.
• Name: The product name as listed on Amazon.
• Wireless Carrier: The supported wireless carrier (e.g., Verizon, AT&T).
• Cellular Technology: The cellular network technology (e.g., 4G, 5G).
• Dimensions: Physical dimensions of the phone.
• ASIN: Amazon Standard Identification Number, a unique product identifier.
• Model: The model number of the phone.
• Amazon Renewed: Indicates whether the product is part of the Amazon Renewed program (refurbished).
• Renewed Smartphones: Additional flag indicating if the phone is renewed.
• Battery Capacity: The capacity of the phone’s battery (in mAh).
• Battery Power: The power rating of the battery.
• Charging Time: Time taken to charge the phone fully.
• RAM: The amount of RAM in the phone.
• Storage: Internal storage capacity of the phone.
• Screen Size: Size of the display (in inches).
• Connectivity Technologies: Wireless technologies supported by the phone (e.g., Bluetooth, Wi-Fi).
• Wireless Network: Type of wireless networks supported (e.g., Wi-Fi 6).
• CPU Speed: The speed of the phone’s CPU (in GHz).
• Reviews USA: User reviews originating from the USA.
• Reviews Other: User reviews from countries other than the USA.

Detail

This dataset includes a comprehensive range of variables, offering insight into both the technical aspects and customer perceptions of various smartphones sold on Amazon. The dataset allows for:

• Product Comparisons: Comparison of specifications like RAM, CPU, storage, battery life, screen size, etc.
• Pricing Analysis: Understanding pricing trends, discounts, and price fluctuations across different brands and models.
• Consumer Insights: Analysis of consumer behavior through ratings, reviews, and the number of buyers over time.
• Product Availability: Insights into stock availability and how often certain products are sold or renewed.

Usage

The dataset can be used for several purposes, including but not limited to:

1. Market Research: Analyze product popularity and trends in smartphone sales on Amazon.
2. Sentiment Analysis: Perform sentiment analysis on reviews (USA and other countries) to understand customer satisfaction.
3. Price Forecasting: Build models to forecast price changes or identify the best time to buy based on historical data.
4. Product Recommendations: Develop recommendation systems based on user reviews, ratings, and product features.
5. Competitive Analysis: Compare different brands and models to identify strengths and weaknesses of various smartphones.
6. Feature Engineering for ML Models: Use product specifications like RAM, CPU speed, and battery power to create features for predictive machine learning models.

Summary

This Amazon product phones dataset provides an in-depth look at smartphones sold on Amazon, covering everything from technical specifications to user reviews and pricing. It is ideal for anyone looking to analyze trends in the smartphone market, consumer preferences, or technical specifications. The data can be leveraged for a wide array of projects such as market analysis, machine learning, and competitive intelligence.

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This dataset lists the most popular smartphones of 2023 in India gathered from Flipkart, one of the largest e-commerce platforms in the country.

The dataset can be used to identify which smartphones and price ranges are preferred by users, the impact of discounts, and how ratings vary.

Column Description

1. title - the name of the smartphone + color of the model + memory + RAM
2. price - price of the smartphone after discount
3. prod_rating - rating of the smartphone
4. rating_count - the number of people
5. discount - the discount offered in percentage
6. seller_rating - rating of the seller of that particular smartphone as rated by their buyer on the whole seller experience

Project Ideas

1) Extract information from the title like brand name, model, color, memory, and RAM. Use different strategies and see which works the best.

2) Correlation analysis - the price of the smartphone could be influenced by rating, number of ratings, discount, and seller rating.

3) Regression - build a regression model to predict the price of a smartphone, by using variables such as "prod_rating," "rating_count," "discount," and "seller_rating" as independent.

4) Visualizations - Get creative with visualizations, create an interactive dashboard, and create forecast charts.

Check out my other dataset on top-rated TV shows: https://www.kaggle.com/datasets/titassaha/top-rated-tv-shows

I write articles on data analysis and analytics, techniques, and document my learning process on my blog - https://emptyjar.in

Thanks.

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Comprehensive dataset tracking Cyber Monday 2024 discount percentages by product category, total sales volume, mobile transaction share, and year-over-year growth metrics. Data sourced from Adobe Analytics tracking of over 1 trillion U.S. retail site visits representing 80% of online transactions from top retailers.

📨 Direct Mail Marketing Response Dataset

This dataset focuses on predicting which customers are most likely to respond to a direct mail marketing promotion.
It is based on real data from a clothing store chain in New England.

• Rows: 28,799 customers
• Features: 51 fields (customer demographics, purchase history, and marketing interactions)
• Target: RESP (whether a customer responded to a promotion)

📊 Dataset Details

Each row corresponds to a unique customer, with information about spending behavior, product preferences, and marketing exposure.

Key Fields

• HHKEY → Unique encrypted customer ID
• ZIP_CODE → Customer’s ZIP code
• FRE → Number of purchase visits
• MON → Total net sales
• AVRG → Average spend per visit
• AMSPEND, PSSPEND, CSSPEND, AXSPEND → Spend across four different franchise brands
• OMONSPEND, TMONSPEND, SMOSPEND → Spend over past 1, 3, and 6 months
• PREVPD → Spend in the same period last year
• GMP → Gross margin percentage
• PROMOS → Number of marketing promotions on file
• DAYS → Number of days customer has been on file
• FREDAYS, LTFREDAY → Time between purchases (recent & lifetime average)
• CLASSES → Number of different product classes purchased
• STYLES → Number of individual items purchased
• STORES → Number of stores shopped at
• MARKDOWN, COUPONS, MAILED, RESPONDE, RESPONSERATE → Promotion and discount engagement
• HI → Product uniformity (lower = more diverse purchases)
• CLUSTYPE → Lifestyle cluster type (encrypted)
• PERCRET → Percent of returns
• CC_CARD, VALPHON, WEB → Flags for credit card, valid phone number, and web shopper status

🛍 Clothing Category Spend Variables

Variables: PSWEATERS, PKNIT_TOPS, PKNIT_DRES, PBLOUSES,PJACKETS, PCAR_PNTS, PCAS_PNTS, PSHIRTS, PDRESSES, PSUITS, POUTERWEAR, PJEWELRY, PFASHION, PLEGWEAR, PCOLLSPND; AC_CALC20

Percentages of spend across 15 clothing/product categories:

sweaters, knit tops, knit dresses, blouses, jackets, career pants, casual pants, shirts, dresses, suits, outerwear, jewelry, fashion, legwear, collectibles

🎯 Target Variable

• RESP → Binary variable indicating whether the customer responded to a mailed promotion.

Credits: https://iscap.pt/~aazevedo/variaveis.html

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These data are part of NACJD's Fast Track Release and are distributed as they there received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except of the removal of direct identifiers. Users should refer to the accompany readme file for a brief description of the files available with this collections and consult the investigator(s) if further information is needed.The purpose of the study was to provide statistically sound estimates on the prevalence of trafficking victimization and investigate the type of trafficking victimization among unauthorized migrant laborers in San Diego. Data were collected through face to face interviews using respondent driven sampling (Labor Trafficking Main Data, n=826 and Specific Trafficking Incident Data, n=826). There were sixteen interview sites spread across San Diego county. All interviews were conducted with at least two interviewers present. The study used a total of seven bilingual interviewers who conducted 826 valid interviews. Each subject was paid thirty dollars for participating in the interview, and given three referral coupons worth ten dollars each. The Respondent Driven Sampling (RDS) began with an initial set of "seeds" recruited from the target population through a combination of recruiting strangers at day labor sites and existing community contacts within the social networks of Center for Social Advocacy (CSA) outreach workers. To be eligible for participation in the study, one had to be unauthorized in the United States and be working (or have worked within) the past 3 months. Other than the seeds, all subsequent referrals had to call the project phone number to schedule interviews with their coupon numbers.

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Please be aware two new fields have been added to the Business Discount Directory ( Mobile Business Flag and Parent Business Category)

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This dataset contains 500,000 records of customer purchase behavior for an e-commerce platform. It can be used for predictive modeling tasks such as

• Predicting whether a customer will make a purchase (PurchaseStatus)
• Customer segmentation and profiling
• Analyzing the impact of demographic, behavioral, and engagement factors on purchases
• The dataset is suitable for classification (purchase prediction) and exploratory data analysis (EDA).

Attribute Explanation

1. Age (int): Customer’s age in years. Range: 15–81.
2. AnnualIncome (float): Customer’s annual income in USD. Range: 11,966 – 204,178.
3. NumberOfPurchases (int): Total purchases made by the customer. Can indicate engagement level.
4. TimeSpentOnWebsite (float): Average time (in minutes) spent on the website per visit.
5. CustomerTenureYears (float): Duration (in years) since the customer joined the platform.
6. LastPurchaseDaysAgo (int): Number of days since the customer’s most recent purchase.
7. Gender (categorical: Male/Female): Customer’s gender.
8. ProductCategory (categorical: Fashion, Electronics, Furniture, Groceries, Sports, etc.): Most frequently purchased product category.
9. PreferredDevice (categorical: Mobile/Desktop/Tablet): Device most often used by the customer.
10. Region (categorical: North, South, East, West): Geographic region of the customer.
11. ReferralSource (categorical: Organic, Paid Ads, Referral, Social Media, Email): How the customer discovered the platform.
12. CustomerSegment (categorical: Regular, Premium, VIP): Business-defined customer classification.
13. LoyaltyProgram (binary: 0/1): Whether the customer is enrolled in a loyalty program.
14. DiscountsAvailed (int): Number of discounts/coupons redeemed by the customer.
15. SessionCount (int): Number of sessions/visits recorded for the customer.
16. CustomerSatisfaction (int: 1–5): Customer satisfaction rating (1 = very dissatisfied, 5 = very satisfied).
17. PurchaseStatus (target variable, binary: 0/1): Purchase successful or not

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