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.

Turkey number dataset is available for everyone who wants to run a successful marketing campaign. Meanwhile, the number list we created contains millions of updated consumer cell phone numbers. It is a one-time payment service and an instant downloadable file format. List to Data considers this database compulsory for all. The contact number list which we gathered and filtered, is the final result of our research. Turkey number dataset is an actual list from which anyone can promote their goods. This country is one of the main business hubs and tourist centers also. So, purchasing this kind of product will keep you way ahead of others. This directory has all the ingredients that will support you in your marketing. Turkey phone data is a customized contact database that anyone can buy now. This product is a collection of only real and active customers. With the help of this list, anyone would be able to reach thousands of prospective customers. Of course, it will support your marketing team all the way. Again, now you can keep a good relationship with your consumers by collecting this contact list. For instance, Turkey phone data only gives you valid and updated numbers. List to Data collects all the contacts and information from several trusted sources. After that, we double-check the data. For those reasons, you won’t find any inactive or inaccurate leads here. The Turkey dataset is a wide resource that will help you to promote your products and services effectively. Telemarketing and SMS marketing is now can be more easily done with this phone data. Turkey phone number list is a convenient way to promote brands and goods. To fulfill one’s marketing campaign this is the kind of product that you need. It has the potential to save you money and time. People from all across the globe now are more into cell phones. So, in this situation buying a phone number list would be a very wise decision to make. In conclusion, Turkey phone number list comes with valuable data that can be used to maximize your aiming efforts. With the cell phone number dataset decreases all of your additional costs. Feel free to contact us. We are available 24/7 to serve you with the best possible service.

About Flipkart:

Flipkart is an Indian e-commerce company, headquartered in Bangalore, Karnataka, India. It is the largest e-commerce company in India and was founded by Sachin and Binny Bansal. The company has wide variety of products electronics like laptops, tablets, smartphones, and mobile accessories to in-vogue fashion staples like shoes, clothing and lifestyle accessories; from modern furniture like sofa sets, dining tables, and wardrobes to appliances that make your life easy like washing machines, TVs, ACs, mixer grinder juicers and other time-saving kitchen and small appliances; from home furnishings like cushion covers, mattresses and bedsheets to toys and musical instruments.

Mobile Phones

Mobile phones are one of the most rapidly rising industries, as well as one of the most prominent industries in the technology sector. The rate of increase has been exponential, with the number of mobile phone customers increasing fivefold in the last decade. Globally, the number of smartphones sold to end users climbed from 300 million in 2010 to 1.5 billion by 2020.

Flipkart and Mobile Phones

As previously stated, mobile phones are in high demand and are one of the ideal products for a novice to sell. Flipkart will be the ideal spot for a vendor to market their stuff because its reach.

Content

The dataset contains description of top 5 most popular mobile brand in India. Columns : There are 16 columns each having a title which is self explanatory. Rows : There are 430 rows each having a mobile with at least a distinct feature.

Acknowledgements

The data was retrieved directly from Flipkart website using some web crawling techniques

Assumption

We don’t have direct sales report of how many units of a mobile model was sold. In general, number of people rating a product is directly proportional to number of units sold. So, for the purpose of the solution, we are using number of people rating the product as the equivalent units sold.

Inspiration

The objective is to address a hypothetical business problem for a Flipkart Authorized Seller. According to the hypothesis the individual is looking to sell mobile phones on Flipkart. For this, the individual is looking for the best product, brand, specification and deals that can generate the most revenue with the least amount of investment and budget constraints.

Questions to be answered: 1. Whether he should sell product for a particular brand only or try to focus on model from different brands? 2. Using EDA and Data Visualization find out insights and relation between different features 3. Perform detailed analysis of each brand. 4. Assuming a budget for the problem come to a solution with maximum return.

Phone numbers in India are a set of unique 10 digit numbers. Out of which, first 4 are network operator/circle code. These prefixes range from 6xxx - 9xxx. Last six are random. This is a dataset, charts, model of first four numbers with their respective circle, operator name.

Note: This dataset is provided "as-is" without any warranty of any kind. While I have personally fixed many errors, I still can't guarantee that this dataset is accurate. Use at your own risk.

Use Cases

• Privacy friendly alternative to reverse phone number lookup services like Truecaller

See the section below for pretrained models, training and inference script.

• Model training

• Spam Detection

Using Machine Learning To Predict Operator Names

A Python script named, predict-operator.py is provided with this project. It works by checking if the operator to predict is in dataset. If not, it will try using the appropriate model for predicting the operator. If appropriate model is not found, it will train the model using Gradient Boosting Classifer(GBC), save it, and predict using newly trained model.

Pretrained models are provided in releases section. If you want, you can train your own by running python train_save_all.py

Examples

$ python predict-operator.py ../data/6xxx-in-mob-prefix.csv 7000 ../models/6xxx-gbc.bin

Predicted Operator: 
['RJ']

$ python predict-operator.py ../data/9xxx-in-mob-prefix.csv 9000 ../models/9xxx-gbc.bin

Operator Found in Database
['AT']

Sources

Majority of this data is sourced from Wikipedia, Department of Telecom(DoT) and Telecom Regulatory Authority of India(TRAI). Rest of it was collected from various sources including web scrapping, personal research. The metro circles which have been merged into their respective states were updated. Then the rows which have both columns empty were dropped using the script provided in src/scripts folder.

The dataset contains missing values, because I want to be accurate, and this data is hard to find. If I wanted, I could fill the data with a single function call df.fillna(), but I did not.

Pakistan’s largest ecommerce data – Power BI Report

Dataset Link: pakistan’s_largest_ecommerce_dataset Cleaned Data: Cleaned_Pakistan’s_largest_ecommerce_dataset

Raw Data:

Rows: 584525 **Columns: **21

Process:

All the raw data transformed and saved in new Excel file Working – Pakistan Largest Ecommerce Dataset

Processed Data:

Rows: 582250 Columns: 22 Visualization: Here is the link of Visualization report link: Pakistan-s-largest-ecommerce-data-Power-BI-Data-Visualization-Report

Conclusion:

In categories Mobiles & Tables make more money by selling highest no of products and also providing highest amount of discount on products. On the other side Men’s Fashion Category has sell second highest no of products but it can’t generate money with that ratio, may be the prices of individual products is a good reason behind that. And in orders details we experience Mobiles & Tablets have highest no of canceled orders but completed orders are almost same as Men’s Fashion. We have mostly completed orders but have huge no of canceled orders. In payment methods cod has most no of completed order and mostly canceled orders have payment method Easyaxis.

TechnicalRemarks: This dataset consists of 79 dialogs between a human user and a chatbot in English language. This data was collected during an online experiment conducted by the research group "Information Systems & Service Design" at the Karlsruhe Institute of Technology (KIT). Experimental task: Participants were asked to interact with a chatbot to find out whether they could save money by switching to a better mobile phone plan. Additionally, there were shown a fictitious copy of last month's mobile phone bill. During the conversation, the chatbot asked about the participant's usage patterns (e.g., how much data was used) and recommended a randomly generated plan that better met the participant’s requirements. For more information, see Gnewuch et al. (2018). If you have any questions, please contact us via email (info@chatbotresearch.com) or visit https://chatbotresearch.com. WARNING! Some dialogs contain profanity and/or offensive language. Profanity was not removed because it is important for calculating sentiment scores. PUBLICATIONS / REFERENCES Gnewuch, U., Morana, S., Adam, M. T. P., and Maedche, A. 2018. “Faster Is Not Always Better: Understanding the Effect of Dynamic Response Delays in Human-Chatbot Interaction,” in Proceedings of the 26th European Conference on Information Systems (ECIS 2018), Portsmouth, United Kingdom. Feine, J., Morana, S., and Gnewuch, U. 2019. “Measuring Service Encounter Satisfaction with Customer Service Chatbots using Sentiment Analysis,” in Proceedings of the 14th International Conference on Wirtschaftsinformatik (WI 2019), Siegen, Germany, February 24–27.

Abstract

The Global Findex 2025 reveals how mobile technology is equipping more adults around the world to own and use financial accounts to save formally, access credit, make and receive digital payments, and pursue opportunities. Including the inaugural Global Findex Digital Connectivity Tracker, this fifth edition of Global Findex presents new insights on the interactions among mobile phone ownership, internet use, and financial inclusion.

The Global Findex is the world’s most comprehensive database on digital and financial inclusion. It is also the only global source of comparable demand-side data, allowing cross-country analysis of how adults access and use mobile phones, the internet, and financial accounts to reach digital information and resources, save, borrow, make payments, and manage their financial health. Data for the Global Findex 2025 were collected from nationally representative surveys of about 145,000 adults in 141 economies. The latest edition follows the 2011, 2014, 2017, and 2021 editions and includes new series measuring mobile phone ownership and internet use, digital safety, and frequency of transactions using financial services.

The Global Findex 2025 is an indispensable resource for policy makers in the fields of digital connectivity and financial inclusion, as well as for practitioners, researchers, and development professionals.

Geographic coverage

National Coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most low- and middle-income economies, Global Findex data were collected through face-to-face interviews. In these economies, an area frame design was used for interviewing. In most high-income economies, telephone surveys were used. In 2024, face-to-face interviews were again conducted in 22 economies after phone-based surveys had been employed in 2021 as a result of mobility restrictions related to COVID-19. In addition, an abridged form of the questionnaire was administered by phone to survey participants in Algeria, China, the Islamic Republic of Iran, Libya, Mauritius, and Ukraine because of economy-specific restrictions. In just one economy, Singapore, did the interviewing mode change from face to face in 2021 to phone based in 2024.

In economies in which face-to-face surveys were conducted, the first stage of sampling was the identification of primary sampling units. These units were then stratified by population size, geography, or both and clustered through one or more stages of sampling. Where population information was available, sample selection was based on probabilities proportional to population size; otherwise, simple random sampling was used. Random route procedures were used to select sampled households. Unless an outright refusal occurred, interviewers made up to three attempts to survey each sampled household. To increase the probability of contact and completion, attempts were made at different times of the day and, where possible, on different days. If an interview could not be completed at a household that was initially part of the sample, a simple substitution method was used to select a replacement household for inclusion.

Respondents were randomly selected within sampled households. Each eligible household member (that is, all those ages 15 or older) was listed, and a handheld survey device randomly selected the household member to be interviewed. For paper surveys, the Kish grid method was used to select the respondent. In economies in which cultural restrictions dictated gender matching, respondents were randomly selected from among all eligible adults of the interviewer’s gender.

In economies in which Global Findex surveys have traditionally been phone based, respondent selection followed the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies in which mobile phone and landline penetration is high, a dual sampling frame was used.

The same procedure for respondent selection was applied to economies in which phone-based interviews were being conducted for the first time. Dual-frame (landline and mobile phone) random digit dialing was used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digit dialing was used in economies with limited or no landline presence (less than 20 percent). For landline respondents in economies in which mobile phone or landline penetration is 80 percent or higher, respondents were selected randomly by using either the next-birthday method or the household enumeration method, which involves listing all eligible household members and randomly selecting one to participate. For mobile phone respondents in these economies or in economies in which mobile phone or landline penetration is less than 80 percent, no further selection was performed. At least three attempts were made to reach the randomly selected person in each household, spread over different days and times of day.

Research instrument

The English version of the questionnaire is provided for download.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in: Klapper, Leora, Dorothe Singer, Laura Starita, and Alexandra Norris. 2025. The Global Findex Database 2025: Connectivity and Financial Inclusion in the Digital Economy. Washington, DC: World Bank. https://doi.org/10.1596/978-1-4648-2204-9.