Weekly statistics showing how many Mint Mobile coupon codes were verified by the CouponBirds team. This dataset reflects real-time coupon validation activity to ensure coupon accuracy and reliability.

Weekly statistics showing how many Loop Mobile coupon codes were verified by the CouponBirds team. This dataset reflects real-time coupon validation activity to ensure coupon accuracy and reliability.

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.

Weekly statistics showing how many Wonda Mobile coupon codes were verified by the CouponBirds team. This dataset reflects real-time coupon validation activity to ensure coupon accuracy and reliability.

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.

Weekly statistics showing how many IQ Mobile coupon codes were verified by the CouponBirds team. This dataset reflects real-time coupon validation activity to ensure coupon accuracy and reliability.

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.

Weekly statistics showing how many Phone Rebel coupon codes were verified by the CouponBirds team. This dataset reflects real-time coupon validation activity to ensure coupon accuracy and reliability.

Weekly statistics showing how many The Big Phone Store coupon codes were verified by the CouponBirds team. This dataset reflects real-time coupon validation activity to ensure coupon accuracy and reliability.

Weekly statistics showing how many Nothing coupon codes were verified by the CouponBirds team. This dataset reflects real-time coupon validation activity to ensure coupon accuracy and reliability.

Businesses registered in the Carer and/or the Seniors Business Discount Card scheme which provide discounts or offers to holders of a Seniors Card, a Seniors Card +go, a Seniors Business Discount Card and/or a Carer Business Discount Card and venues participating in the Companion Card scheme

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

Weekly statistics showing how many Sarpino's coupon codes were verified by the CouponBirds team. This dataset reflects real-time coupon validation activity to ensure coupon accuracy and reliability.

Apple Iphones sold in India

Price, Rating, and Reviews

By Tony Paul [source]

About this dataset

This dataset contains detailed information about Apple iPhones that have been sold in India. Each entry includes the product name, brand, sale price, maximum retail price (MRP), universal product code (UPC), number of reviews and ratings obtained from customers, discount percentage offered on various products, as well as the random access memory (RAM) size associated with each product. Dive into this comprehensive collection of Apple products for a better understanding of selling iPhone models in India and accurately capture insights about customer preferences and market trends!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Here is how to use this dataset effectively: - Start by exploring the headers of each column to understand the data features available in the dataset; you should be able to identify which columns contain what kind of data. - To get an overview of your data, calculate summary statistics such as means and standard deviations for numerical columns (e.g., Sale Price, Mrp etc.). - Visualize your data using a variety of techniques like histograms, scatter plots and correlation matrices - this will help you look for possible relationships between different variables. You may also consider creating pair plots that allow you to compare and visualize pairs of variables against each other at a glance. - Finally, start building models or perform exploratory analysis such as hypothesis testing with the help of various statistical methods or machine learning algorithms for further insights into the Apple iPhone sales in India!

Research Ideas

• Developing an AI-based Product Recommender System using the attributes of Apple Iphones (e.g. price, discount percentage, ratings, reviews & RAM) for customers who are looking to purchase new Apple phone in India
• Creating a brand intelligence system that analyses the popularity of different Apple product models and rank them according to their performance over time
• Using Machine Learning to build a predictive model for forecasting sales patterns and predicting demand for future sales of Apple Iphones in India

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: apple_products.csv | Column name | Description | |:------------------------|:--------------------------------------------------------------------------| | Product Name | The name of the Apple iPhone product. (String) | | Product URL | The URL of the product page. (String) | | Brand | The brand of the Apple iPhone product. (String) | | Sale Price | The price of the Apple iPhone product at the time of sale. (Numeric) | | Mrp | The maximum retail price of the Apple iPhone product. (Numeric) | | Discount Percentage | The percentage of discount offered on the Apple iPhone product. (Numeric) | | Number Of Ratings | The number of ratings given to the Apple iPhone product. (Numeric) | | Number Of Reviews | The number of reviews given to the Apple iPhone product. (Numeric) | | Upc | The universal product code of the Apple iPhone product. (String) | | Star Rating | The star rating of the Apple iPhone product. (Numeric) | | Ram | The Random Access Memory size of the Apple iPhone product. (Numeric) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Tony Paul.

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.

Weekly statistics showing how many Eyedictive coupon codes were verified by the CouponBirds team. This dataset reflects real-time coupon validation activity to ensure coupon accuracy and reliability.

Weekly statistics showing how many Kardia coupon codes were verified by the CouponBirds team. This dataset reflects real-time coupon validation activity to ensure coupon accuracy and reliability.

Weekly statistics showing how many Ding coupon codes were verified by the CouponBirds team. This dataset reflects real-time coupon validation activity to ensure coupon accuracy and reliability.

Dirty Retail Store Sales Dataset

Overview

The Dirty Retail Store Sales dataset contains 12,575 rows of synthetic data representing sales transactions from a retail store. The dataset includes eight product categories with 25 items per category, each having static prices. It is designed to simulate real-world sales data, including intentional "dirtiness" such as missing or inconsistent values. This dataset is suitable for practicing data cleaning, exploratory data analysis (EDA), and feature engineering.

File Information

• File Name: retail_store_sales.csv
• Number of Rows: 12,575
• Number of Columns: 11

Columns Description

Column Name	Description	Example Values
Transaction ID	A unique identifier for each transaction. Always present and unique.	TXN_1234567
Customer ID	A unique identifier for each customer. 25 unique customers.	CUST_01
Category	The category of the purchased item.	Food, Furniture
Item	The name of the purchased item. May contain missing values or None.	Item_1_FOOD, None
Price Per Unit	The static price of a single unit of the item. May contain missing or None values.	4.00, None
Quantity	The quantity of the item purchased. May contain missing or None values.	1, None
Total Spent	The total amount spent on the transaction. Calculated as Quantity * Price Per Unit.	8.00, None
Payment Method	The method of payment used. May contain missing or invalid values.	Cash, Credit Card
Location	The location where the transaction occurred. May contain missing or invalid values.	In-store, Online
Transaction Date	The date of the transaction. Always present and valid.	2023-01-15
Discount Applied	Indicates if a discount was applied to the transaction. May contain missing values.	True, False, None

Categories and Items

The dataset includes the following categories, each containing 25 items with corresponding codes, names, and static prices:

Electric Household Essentials

Item Code	Item Name	Price
Item_1_EHE	Blender	5.0
Item_2_EHE	Microwave	6.5
Item_3_EHE	Toaster	8.0
Item_4_EHE	Vacuum Cleaner	9.5
Item_5_EHE	Air Purifier	11.0
Item_6_EHE	Electric Kettle	12.5
Item_7_EHE	Rice Cooker	14.0
Item_8_EHE	Iron	15.5
Item_9_EHE	Ceiling Fan	17.0
Item_10_EHE	Table Fan	18.5
Item_11_EHE	Hair Dryer	20.0
Item_12_EHE	Heater	21.5
Item_13_EHE	Humidifier	23.0
Item_14_EHE	Dehumidifier	24.5
Item_15_EHE	Coffee Maker	26.0
Item_16_EHE	Portable AC	27.5
Item_17_EHE	Electric Stove	29.0
Item_18_EHE	Pressure Cooker	30.5
Item_19_EHE	Induction Cooktop	32.0
Item_20_EHE	Water Dispenser	33.5
Item_21_EHE	Hand Blender	35.0
Item_22_EHE	Mixer Grinder	36.5
Item_23_EHE	Sandwich Maker	38.0
Item_24_EHE	Air Fryer	39.5
Item_25_EHE	Juicer	41.0

Furniture

Item Code	Item Name	Price
Item_1_FUR	Office Chair	5.0
Item_2_FUR	Sofa	6.5
Item_3_FUR	Coffee Table	8.0
Item_4_FUR	Dining Table	9.5
Item_5_FUR	Bookshelf	11.0
Item_6_FUR	Bed F...

Dataset Description: E-commerce Customer Behavior

Overview: This dataset provides a comprehensive view of customer behavior within an e-commerce platform. Each entry in the dataset corresponds to a unique customer, offering a detailed breakdown of their interactions and transactions. The information is crafted to facilitate a nuanced analysis of customer preferences, engagement patterns, and satisfaction levels, aiding businesses in making data-driven decisions to enhance the customer experience.

Columns:

1. Customer ID:

   • Type: Numeric
   • Description: A unique identifier assigned to each customer, ensuring distinction across the dataset.

2. Gender:

   • Type: Categorical (Male, Female)
   • Description: Specifies the gender of the customer, allowing for gender-based analytics.

3. Age:

   • Type: Numeric
   • Description: Represents the age of the customer, enabling age-group-specific insights.

4. City:

   • Type: Categorical (City names)
   • Description: Indicates the city of residence for each customer, providing geographic insights.

5. Membership Type:

   • Type: Categorical (Gold, Silver, Bronze)
   • Description: Identifies the type of membership held by the customer, influencing perks and benefits.

6. Total Spend:

   • Type: Numeric
   • Description: Records the total monetary expenditure by the customer on the e-commerce platform.

7. Items Purchased:

   • Type: Numeric
   • Description: Quantifies the total number of items purchased by the customer.

8. Average Rating:

   • Type: Numeric (0 to 5, with decimals)
   • Description: Represents the average rating given by the customer for purchased items, gauging satisfaction.

9. Discount Applied:

   • Type: Boolean (True, False)
   • Description: Indicates whether a discount was applied to the customer's purchase, influencing buying behavior.

10. Days Since Last Purchase:

    • Type: Numeric
    • Description: Reflects the number of days elapsed since the customer's most recent purchase, aiding in retention analysis.

11. Satisfaction Level:

    • Type: Categorical (Satisfied, Neutral, Unsatisfied)
    • Description: Captures the overall satisfaction level of the customer, providing a subjective measure of their experience.

Use Cases:

1. Customer Segmentation:

   • Analyze and categorize customers based on demographics, spending habits, and satisfaction levels.

2. Satisfaction Analysis:

   • Investigate factors influencing customer satisfaction and identify areas for improvement.

3. Promotion Strategy:

   • Assess the impact of discounts on customer spending and tailor promotional strategies accordingly.

4. Retention Strategies:

   • Develop targeted retention strategies by understanding the time gap since the last purchase.

5. City-based Insights:

   • Explore regional variations in customer behavior to optimize marketing efforts based on location-specific trends.

Note: This dataset is synthetically generated for illustrative purposes, and any resemblance to real individuals or scenarios is coincidental.

Weekly statistics showing how many Oh Snap coupon codes were verified by the CouponBirds team. This dataset reflects real-time coupon validation activity to ensure coupon accuracy and reliability.