📱 Mobile Specification Dataset Description This dataset contains comprehensive specifications and pricing details of a variety of smartphones available in the market. It is ideal for machine learning tasks like price prediction, feature-based recommendation systems, and market analysis.

🧩 Dataset Columns: Processor: The type of chipset used in the smartphone (e.g., Snapdragon 8 Gen 1, A15 Bionic, MediaTek Dimensity).

Ram_GB: Amount of RAM in gigabytes (GB).

Display_Size(Inches): Screen size in inches (e.g., 6.5").

Display_Quality: Quality/technology of display (e.g., AMOLED, IPS LCD, Retina).

Refresh_Rate: Screen refresh rate in Hertz (Hz), typically 60, 90, 120, or 144 Hz.

Rear_Camera_MP: Combined megapixels of the rear camera setup.

Front_Camera_MP: Megapixels of the front (selfie) camera.

Battery_Capacity: Battery size in milliamp-hours (mAh).

Rom_GB: Internal storage size in gigabytes (GB).

IOS: Binary indicator (1 or 0) representing if the phone runs on iOS.

Android: Binary indicator (1 or 0) representing if the phone runs on Android.

Version: The version of the operating system (e.g., Android 13, iOS 17).

Price: Final selling price of the device in local currency (e.g., INR or USD). (Note: This column is typically the target for prediction models.)

📌 Use Cases: Smartphone price prediction

Clustering phones by specs

Comparative analysis of hardware value vs. price

Feature impact studies on mobile pricing

This dataset contains detailed specifications and official launch prices of various mobile phone models from different companies. It provides insights into smartphone hardware, pricing trends, and brand competitiveness across multiple countries. The dataset includes key features such as RAM, camera specifications, battery capacity, processor details, and screen size.

One important aspect of this dataset is the pricing information. The recorded prices represent the official launch prices of the mobile phones at the time they were first introduced in the market. Prices vary based on the country and the launch period, meaning older models reflect their original launch prices, while newer models include their most recent launch prices. This makes the dataset valuable for studying price trends over time and comparing smartphone affordability across different regions.

Features:

• Company Name: The brand or manufacturer of the mobile phone.
• Model Name: The specific model of the smartphone.
• Mobile Weight: The weight of the mobile phone (in grams).
• RAM: The amount of Random Access Memory (RAM) in the device (in GB).
• Front Camera: The resolution of the front (selfie) camera (in MP).
• Back Camera: The resolution of the primary rear camera (in MP).
• Processor: The chipset or processor used in the device.
• Battery Capacity: The battery size of the smartphone (in mAh).
• Screen Size: The display size of the smartphone (in inches).
• Launched Price: (Pakistan, India, China, USA, Dubai): The official launch price of the mobile in the respective country at the time of its release. Prices vary based on the year the mobile was launched.
• Launched Year: The year the mobile phone was officially launched.

After fierce competition among vivo, Samsung, and Xiaomi over the past few years, vivo became the leading smartphone brand in India. Vivo led the market in the last three quarters. However, in the first two quarters of 2024, Xiaomi and Samsung ranked as high as or higher than Vivo. Aside from Samsung, the other four top smartphone brands are Chinese. Smartphone market share in India The number of smartphone users in India, the most populous country in the world, was on the rise. In 2023, the number of smartphone users in the country surpassed one billion for the first time. This figure was forecasted to jump to nearly 1.55 billion by 2040. And, around seven percent of the population in India purchase their phones online. This growth can also be observed in the volume of smartphone shipments in India. The number of smartphone shipments in India increased from four million units in the second quarter of 2012 to 47 million units in the third quarter of 2024. Major players South Korean giant Samsung, a leader in the global smartphone market, had been the top smartphone vendor in India since early 2013, when the company held about 30 percent of the market share, until the end of 2017. But its position has been challenged by the Chinese smartphone manufacturers like vivo, Xiaomi and OPPO. Vivo is a Chinese tech company based in Guangdong. It's one of the top five smartphone manufacturers in the world. And Xiaomi has quickly risen to the top of China's crowded technology market and is now one of the leading consumer electronics manufacturers globally, since its founding in 2010. Xiaomi specializes primarily in smartphones, but is also active in other markets, and it started manufacturing electric vehicles in 2023.

Context

It is no secret that mobile devices are increasingly taking over the market at the expense of stationary equipment and many forgotten tablets. Trends change over time and the data collected helps us understand them. So let's look at the share of these three sections in the most populous country in the world, which is India.

Content

The database saved in .csv form contains 4 columns. The first column contains the date (YYYY-MM) from the measurement period. Each subsequent column contains the percentage of market share in mobile, desktop and tablet markets, given as a percentage, rounded to 2 decimal places (if the share is less than 0.5%, the value 0 remains, even though it may constitute a very small percentage of the share). We have a total of 180 rows, i.e. full 15 years of data for each month.

Source

The database comes from the statcounter website and is available under the CC BY-SA 3.0 license, which allows you to copy, use and distribute the data also for commercial purposes after citing the source.

Photo by Andrew Neel on Unsplash

Dive into the dynamic and competitive landscape of the Indian smartphone market with this extensive dataset. This collection details over 450 unique smartphone models from major brands like Apple, Samsung, Xiaomi, Vivo, Oppo, Realme, OnePlus, Motorola, and many more, anticipated to be available in 2024 and early 2025.

This dataset is a goldmine for tech enthusiasts, data analysts, market researchers, and machine learning practitioners. It provides a holistic view of the market, capturing the full spectrum from budget-friendly 5G entrants to flagship powerhouses, including futuristic models like the iPhone 17 series and Samsung Galaxy S25 Ultra.

Key Highlights:

Future-Forward: Includes specifications for upcoming, unreleased models (e.g., iPhone 17, Samsung Galaxy S25 series), offering a predictive look at the market.

Comprehensive Specifications: Covers critical phone attributes including price, brand, model, processor, RAM, storage, battery capacity, display specs, camera setup, OS version, and special features.

Indian Market Focus: All prices are listed in Indian Rupees (₹), reflecting the specific pricing strategies and options available in one of the world's largest smartphone markets.

Ideal for Analysis: Perfect for conducting competitive analysis, price prediction modeling, market segmentation, brand comparison, and feature trend analysis (e.g., the rise of 120Hz displays, large batteries, and specific chipsets).

**Potential Use Cases ** This dataset can be used for various projects and analyses, such as:

Price Prediction: Build a model to predict the price of a smartphone based on its specifications.

Market Basket Analysis: Explore which features commonly appear together (e.g., 120Hz display with a powerful processor).

Brand Comparison: Compare brands based on the average price, rating, or value-for-money propositions.

Segment Identification: Cluster phones into categories like Budget, Mid-Range, Premium, and Flagship based on their features and price.

Feature Trend Analysis: Analyze trends like battery capacity over time, the adoption of high-refresh-rate displays, or the evolution of camera setups.

Recommendation System: Develop a content-based system to recommend phones based on a user's preferred specifications.

Column Description Column Name Description model: The full name of the smartphone model (e.g., "Samsung Galaxy S24 Ultra"). price: The price of the smartphone in Indian Rupees (₹). rating: The user or critic rating of the phone (out of 100, where available). Many values are missing (NaN). sim: Details about SIM card support and connectivity options (e.g., "Dual Sim, 5G, VoLTE, Wi-Fi"). processor: The model and core configuration of the phone's processor (e.g., "Snapdragon 8 Gen 3, Octa Core"). ram: The RAM capacity and internal storage size (e.g., "12 GB RAM, 256 GB inbuilt"). battery: The battery capacity (in mAh) and details about fast charging support. For some Apple models, it lists the display size. display: Specifications of the phone's display including size, resolution, refresh rate, and type (e.g., "6.7 inches, 1080 x 2340 px, 120 Hz Display with Punch Hole"). camera:Configuration of the rear and front camera system (e.g., "50 MP + 8 MP Dual Rear & 32 MP Front Camera"). card : Information about expandable storage via memory card (e.g., "Memory Card Supported, upto 1 TB") or if it's not supported. os : The operating system and its version (e.g., "Android v15", "iOS v18"). Additional notes like "No FM Radio" are sometimes included here. Acknowledgements (for Kaggle) This dataset was synthetically compiled for educational and analytical purposes. The specifications and details are modeled after real-world patterns and rumors from the tech industry but are not officially sourced from the manufacturers. It is intended for practicing data science and analytics skills.

What can you discover about the future of smartphones? Can you predict the next big feature that will separate budget phones from flagships? How does a brand like Apple justify its premium price tag compared to competitors with similar specs? Is there a clear "sweet spot" for battery capacity and price in the mid-range segment? What is the most common RAM and storage configuration in 2024?

Happy analyzing!

📄 Dataset Description: Smartprix Mobile Dataset Title: Smartprix Mobile Dataset Source: https://www.smartprix.com/mobiles License: Apache 2.0

📝 Overview This dataset contains detailed specifications of mobile phones scraped from Smartprix — a popular product comparison site. The data includes technical specifications, pricing, and ratings for a wide range of mobile devices available in the Indian market.

📦 Columns Included Column Name Description Name Full name of the smartphone Price Price in Indian Rupees (INR) Rating User rating (if available) SIM SIM type (Dual/Single SIM, 4G/5G supported) Processor Chipset used in the phone (e.g., Snapdragon 8 Gen 1) RAM Amount of RAM Battery Battery capacity (e.g., 5000 mAh) Display Display size and resolution Camera Primary camera specification Card Expandable storage support (e.g., microSD) OS Operating system (e.g., Android 13)

📊 Potential Use Cases Market trend analysis for mobile phones

Feature comparison across brands

Recommendation systems for e-commerce

NLP applications (e.g., generating product summaries)

⚠️ Disclaimer This dataset was collected using publicly available data from Smartprix.com and is intended for educational and research purposes only. Ensure compliance with the source’s terms of service before commercial use.

OPPO has significantly increased the production and sales of their smartphone lineup over the past five years, shipping almost 29 million units in the third quarter of 2024. The company – launched in 2004 – shipped 7.3 million units in the first quarter of 2015. Despite considerable growth over the past few years, the total smartphone units shipped by OPPO in the first quarter of 2024 was not the highest. OPPO’s growth: Leading five vendors OPPO’s growth has seen their output consistently place the company among the top five smartphone vendors in the world, shipping around 29 million units in the third quarter of 2024. While many of those shipments were domestic shipments in the company’s home country of China, OPPO has gained a footing in international markets, accounting for four percent of the smartphone market in Europe. OPPOrtunities in emerging markets Many of OPPO’s smartphones are available at a lower price-point than the flagship phones of vendors such as Apple, giving the company opportunities in emerging markets. For instance, the company regularly appears among the top vendors in the African smartphone market. A key reason for OPPO’s success in Africa is that 97 percent of all phones sold in the region sell for less than 400 U.S. dollars.

This dataset contains a landscape of 23,569 patents and patent applications registered in India and relevant to the domain of mobile technology. These patents and patent applications are held by 50 Indian and non-Indian companies operating in the country. The patent landscape has been released under the Creative Commons-Attribution-Share Alike 4.0 (CC-BY-SA 4.0) License as a part of the Pervasive Technologies research project at the Centre for Internet and Society, India.

This study was conducted by Prof Jorge Contreras at the University of Utah, USA, and Rohini Lakshané at the Centre for Internet and Society, India.

For the detailed methodology used for drawing up this landscape, refer to: http://cis-india.org/a2k/blogs/patent-landscaping-in-the-indian-mobile-device-market

A paper titled "Patents and Mobile Devices in India: An Empirical Survey" published in the Vanderbilt Journal of Transnational Law (2017) presents an analysis of this patent landscape.

For queries regarding the dataset or its reuse, write to rohini dot lakshane at gmail dot com.

Success.ai’s Fashion & Apparel Data for Apparel, Fashion & Luxury Goods Professionals in Asia provides a robust dataset tailored for businesses seeking to connect with key players in Asia’s thriving fashion and luxury goods industries. Covering roles such as brand managers, designers, retail executives, and supply chain leaders, this dataset includes verified contact details, professional insights, and actionable business data.

With access to over 700 million verified global profiles and 130 million profiles focused on Asia, Success.ai ensures your outreach, marketing, and business development strategies are supported by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution positions you to succeed in Asia’s competitive and ever-growing fashion markets.

Why Choose Success.ai’s Fashion & Apparel Data?

1. Verified Contact Data for Precision Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of professionals in apparel, fashion, and luxury goods industries across Asia.
   • AI-driven validation ensures 99% accuracy, reducing bounce rates and enhancing communication efficiency.

2. Comprehensive Coverage of Asian Fashion Professionals

   • Includes profiles from major fashion hubs such as China, India, Japan, South Korea, and Southeast Asia.
   • Gain insights into regional consumer trends, emerging fashion markets, and luxury goods opportunities.

3. Continuously Updated Datasets

   • Real-time updates capture changes in leadership, market expansions, and product launches.
   • Stay aligned with evolving industry trends and capitalize on new opportunities effectively.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with professionals across the global fashion and apparel industries, with a focus on Asia.
• 130M+ Profiles in Asia: Gain detailed insights into professionals shaping the region’s fashion and luxury goods markets.
• Verified Contact Details: Access work emails, phone numbers, and business locations for precise targeting.
• Leadership Insights: Engage with designers, brand managers, and retail leaders driving Asia’s fashion trends.

Key Features of the Dataset:

1. Comprehensive Professional Profiles

   • Identify and connect with decision-makers in apparel design, luxury goods branding, retail operations, and supply chain management.
   • Target individuals leading innovation in sustainable fashion, fast fashion, and digital transformation.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (luxury goods, ready-to-wear, footwear), geographic location, or job function.
   • Tailor campaigns to align with specific market needs, such as emerging e-commerce platforms or regional fashion preferences.

3. Industry and Regional Insights

   • Leverage data on consumer behaviors, market growth, and regional trends in Asia’s fashion and luxury goods sectors.
   • Refine marketing strategies, product development, and partnership outreach based on actionable insights.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Brand Expansion

   • Design targeted campaigns to promote apparel, luxury goods, or retail solutions to fashion professionals in Asia.
   • Leverage multi-channel outreach, including email, phone, and social media, to maximize engagement.

2. Product Development and Consumer Insights

   • Utilize data on regional trends and consumer preferences to guide product development and marketing strategies.
   • Collaborate with brand managers and designers to tailor collections or launch new offerings aligned with market demands.

3. Partnership Development and Retail Collaboration

   • Build relationships with retail chains, luxury brands, and supply chain leaders seeking strategic alliances.
   • Foster partnerships that expand distribution channels, enhance brand visibility, or improve operational efficiencies.

4. Market Research and Competitive Analysis

   • Analyze trends in Asia’s fashion industry to refine business strategies, identify market gaps, and anticipate consumer demands.
   • Benchmark against competitors to stay ahead in the fast-paced fashion landscape.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality fashion and apparel data at competitive prices, ensuring strong ROI for your marketing, sales, and product development efforts.

2. Seamless Integration

   • Integrate verified data into CRM systems, analytics platforms, or marketing tools via APIs or downloadable formats, streamlining workfl...

This dataset provides a comprehensive look at the mobile phone market, with data on prices and specifications scraped from 91mobiles.com in June 2023. With its originality and rawness, this dataset is perfect for beginners who are just getting into machine learning. You can have a hands-on experience with everything from cleaning to exploratory data analysis (EDA) and building a model completely from scratch. Gain insights into the latest trends and make informed predictions about the future of the mobile phone industry. Whether you’re a data scientist, market analyst, or just curious about the world of mobile phones, this dataset is an valuable resource.

China is leading the ranking by number of smartphone users, recording ****** million users. Following closely behind is India with ****** million users, while Seychelles is trailing the ranking with **** million users, resulting in a difference of ****** million users to the ranking leader, China. Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

📱 Mobile Phones Under ₹50,000 in India 📱

Dataset Description

This dataset contains detailed information on mobile phones priced under ₹50,000 in India, collected from popular online marketplaces. The data was scraped using Beautiful Soup, a powerful web scraping library in Python. This dataset aims to provide insights into the pricing, features, and customer opinions on various mobile phone models available in the Indian market.

📊 Columns

• 📱 Product_Name: The name of the mobile phone model. This includes the brand and specific model identifier, which can help users identify and differentiate between various mobile phones.

• 💰 Prices: The price of the mobile phone in Indian Rupees (₹). This column provides the cost of each mobile phone, enabling users to compare prices across different models and brands within the ₹50,000 budget range.

• 📝 Description: A brief overview of the mobile phone's key features and specifications. This includes details such as screen size, camera quality, battery life, processor type, and other important attributes that define the mobile phone's capabilities.

• ⭐ Reviews: Customer reviews and ratings for each mobile phone model. This column includes actual user feedback, providing insights into the performance, reliability, and user satisfaction of the mobile phones.

🌐 Data Source

The data was collected from multiple popular online marketplaces to ensure a comprehensive coverage of the market. The use of Beautiful Soup allowed for efficient extraction of relevant information from the web pages.

🔍 Usage

This dataset can be used for various purposes including:

• 📈 Market Analysis: Analyzing trends and patterns in mobile phone prices and features under the ₹50,000 range in India.
• 💬 Customer Sentiment Analysis: Understanding customer opinions and satisfaction levels through reviews and ratings.
• 🔄 Product Comparison: Comparing different mobile phone models based on their specifications and prices.
• 🧠 Data Science Projects: Utilizing the dataset for machine learning models, data visualization projects, and other data-driven tasks.

🙏 Acknowledgments

Special thanks to the online marketplaces from which the data was scraped. The use of Beautiful Soup greatly facilitated the data collection process, making it possible to compile this dataset.

📜 License

This dataset is available for use under Apache 2.0, ensuring that it can be freely accessed and utilized for both personal and commercial projects, with proper attribution to the source.

We hope this dataset serves as a valuable resource for your data analysis and research projects related to mobile phones in India! 📊📱✨

This table provides a concise summary of the dataset columns along with their respective descriptions.

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.

Business problem overview In the telecom industry, customers are able to choose from multiple service providers and actively switch from one operator to another. In this highly competitive market, the telecommunications industry experiences an average of 15-25% annual churn rate. Given the fact that it costs 5-10 times more to acquire a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition.

For many incumbent operators, retaining high profitable customers is the number one business goal.

To reduce customer churn, telecom companies need to predict which customers are at high risk of churn.

In this project, you will analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.

Understanding and defining churn There are two main models of payment in the telecom industry - postpaid (customers pay a monthly/annual bill after using the services) and prepaid (customers pay/recharge with a certain amount in advance and then use the services).

In the postpaid model, when customers want to switch to another operator, they usually inform the existing operator to terminate the services, and you directly know that this is an instance of churn.

However, in the prepaid model, customers who want to switch to another network can simply stop using the services without any notice, and it is hard to know whether someone has actually churned or is simply not using the services temporarily (e.g. someone may be on a trip abroad for a month or two and then intend to resume using the services again).

Thus, churn prediction is usually more critical (and non-trivial) for prepaid customers, and the term ‘churn’ should be defined carefully. Also, prepaid is the most common model in India and Southeast Asia, while postpaid is more common in Europe in North America.

This project is based on the Indian and Southeast Asian market.

Definitions of churn There are various ways to define churn, such as:

Revenue-based churn: Customers who have not utilised any revenue-generating facilities such as mobile internet, outgoing calls, SMS etc. over a given period of time. One could also use aggregate metrics such as ‘customers who have generated less than INR 4 per month in total/average/median revenue’.

The main shortcoming of this definition is that there are customers who only receive calls/SMSes from their wage-earning counterparts, i.e. they don’t generate revenue but use the services. For example, many users in rural areas only receive calls from their wage-earning siblings in urban areas.

Usage-based churn: Customers who have not done any usage, either incoming or outgoing - in terms of calls, internet etc. over a period of time.

A potential shortcoming of this definition is that when the customer has stopped using the services for a while, it may be too late to take any corrective actions to retain them. For e.g., if you define churn based on a ‘two-months zero usage’ period, predicting churn could be useless since by that time the customer would have already switched to another operator.

In this project, you will use the usage-based definition to define churn.

High-value churn In the Indian and the Southeast Asian market, approximately 80% of revenue comes from the top 20% customers (called high-value customers). Thus, if we can reduce churn of the high-value customers, we will be able to reduce significant revenue leakage.

In this project, you will define high-value customers based on a certain metric (mentioned later below) and predict churn only on high-value customers.

Understanding the business objective and the data The dataset contains customer-level information for a span of four consecutive months - June, July, August and September. The months are encoded as 6, 7, 8 and 9, respectively.

The business objective is to predict the churn in the last (i.e. the ninth) month using the data (features) from the first three months. To do this task well, understanding the typical customer behaviour during churn will be helpful.

Understanding customer behaviour during churn Customers usually do not decide to switch to another competitor instantly, but rather over a period of time (this is especially applicable to high-value customers). In churn prediction, we assume that there are three phases of customer lifecycle :

The ‘good’ phase: In this phase, the customer is happy with the service and behaves as usual.

The ‘action’ phase: The customer experience starts to sore in this phase, for e.g. he/she gets a compelling offer from a competitor, faces unjust charges, becomes unhappy with service quality etc. In this phase, the customer usually shows different behaviour than the ‘good’ months. Also, it is crucial to...

Title Telecom Churn Dataset

Description

This dataset contains 243,553 rows of customer data from four major telecom partners of India: Airtel, Reliance Jio, Vodafone, and BSNL. The dataset includes various demographic, location, and usage pattern variables for each customer, as well as a binary variable indicating whether the customer has churned or not.

Variables

customer_id: Unique identifier for each customer. telecom_partner: The telecom partner associated with the customer. gender: The gender of the customer. age: The age of the customer. state: The Indian state in which the customer is located. city: The city in which the customer is located. pincode: The pincode of the customer's location. date_of_registration: The date on which the customer registered with the telecom partner. num_dependents: The number of dependents (e.g. children) the customer has. estimated_salary: The customer's estimated salary. calls_made: The number of calls made by the customer. sms_sent: The number of SMS messages sent by the customer. data_used: The amount of data used by the customer. churn: Binary variable indicating whether the customer has churned or not (1 = churned, 0 = not churned).

Potential Use Cases

This dataset can be used for a variety of machine learning tasks such as customer churn prediction, customer segmentation, and customer lifetime value estimation. The dataset is also suitable for exploratory data analysis (EDA) to gain insights into the telecom industry and its customers

Acknowledgements

This dataset was created for use in an industry-wide case study on customer churn in the telecom industry.

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.

This dataset provides detailed information about various smartphones available on the e-commerce platform Flipkart as of July 2023. The dataset is in CSV format and contains the following columns:

img_link: This column contains the URL link to the image of each smartphone. It can be used to retrieve and display the corresponding image for each smartphone.

phone_name: This column contains the name or model of each smartphone. It provides a unique identifier for each device in the dataset.

avg_rating: This column represents the average rating of each smartphone on Flipkart. It indicates the overall customer satisfaction level based on user ratings. The rating scale typically ranges from 1 to 5 stars, with 5 being the highest rating.

total_rating: This column indicates the total number of people who have rated each smartphone on Flipkart. It provides an understanding of the popularity and feedback from customers who have shared their ratings.

total_reviews: This column represents the total number of reviews available for each smartphone on Flipkart. It provides insights into the level of engagement and the amount of user-generated content related to each device.

discounted_price: This column contains the discounted price of each smartphone in Indian Rupees (INR). It represents the current selling price of the device after applying any applicable discounts or promotional offers.

actual_price: This column displays the actual or original price of each smartphone in Indian Rupees (INR) before any discounts. It provides a reference point for the discounted price and helps users understand the amount of savings or price reduction available.

The dataset is valuable for conducting various analyses related to smartphones available on Flipkart. Researchers, data scientists, and analysts can use this dataset to explore trends in customer ratings, reviews, pricing, and discounts. They can also perform market research, brand comparisons, sentiment analysis, and other studies related to the smartphone industry.

Please note that this dataset is specific to Flipkart and represents the smartphone market as of July 2023. It can be used to gain insights into customer preferences, pricing strategies, and overall market dynamics in the context of Flipkart's smartphone offerings.

Saree Retailers Database in India

Accurate Up-to-Date Data for All Types of Business Purposes

By Amresh [source]

About this dataset

This All India Saree Retailers Database is a comprehensive collection of up-to-date information on 10,000 Saree Retailers located all over India. The database is updated in April 2021 and offers an overall accuracy rate of around 90%.

For business owners, marketers, and data analysts and researchers, this dataset is an invaluable resource. It contains contact details of store name, contact person names, phone number and email address along with store location information like city state and pin code to help you target the right audience precisely.

The database can be accessed in Microsoft Excel (.xlsx) format which makes it easy to read or manipulate the file according to your needs. Apart from this wide range of payment options like Credit/Debit Card; Online Transfer; NEFT; Cash Deposit; Paytm; PhonePe; Google Pay or PayPal allow quick download access within 2-3 business hours.

So if you are looking for reliable business intelligence data related to Indian saree retailers that can help you unlock incredible opportunities for your business then make sure to download our All India Saree Retailers Database at the earliest!

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How to use the dataset

This dataset provides a comprehensive list of Saree retailers in India, including store name, contact person, email address, mobile number, phone number, address details like city and state along with pin code. It contains 10 thousand records updated in April 2021 with an overall accuracy rate of around 90%. This data can be used to understand customer behaviour as well as to analyse geographical customer pattern.

Using this dataset you can: - Target specific states or cities where potential customers are located for your Saree business. - Get in touch with local Saree retailers for possible collaborations and partnerships. - Learn more about industry trends from actual store owners who can offer insights into the latest ongoing trends and identify new opportunities for you to grow your business. 4 .Analyse existing competitors’ market share by studying the cities/states where they operate and their contact information such as Mobile Number & Email Ids .
5 .Identify potential new customers for better sales conversion rates by understanding who is already operating in similar products nearby or have similar target audience as yours that help your company reach out to them quickly & effectively using direct marketing techniques such as emails & SMS etc.,

Research Ideas

• Creating targeted email campaigns to increase Saree sales: The dataset can be used to create targeted email campaigns that can reach the 10,000 Saree Retailers in India. This will allow businesses to increase sales by directing their message about promotions and discounts directly to potential customers.
• Customizing online product recommendations for each retailer: The dataset can be used to identify the specific products that each individual retailer is interested in selling, so product recommendations on an e-commerce website could be tailored accordingly. This would optimize customer experience giving them more accurate and relevant results when searching for a particular item they are looking for while shopping online.
• Using GPS technology to generate location-based marketing campaigns: By creating geo-fenced areas around each store using the pin code database, it would be possible to send out marketing messages based on people's physical location instead of just sending them out in certain neighborhoods or cities without regard for store locations within those areas. This could help reach specific customers with relevant messages about products or promotions that may interested them more effectively than a standard marketing campaign with no location targeting involved

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: 301-Saree-Garment-Retailer-Database-Sample.csv

Acknowledgements

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

Problem Statement

Business problem overview In the telecom industry, customers are able to choose from multiple service providers and actively switch from one operator to another. In this highly competitive market, the telecommunications industry experiences an average of 15-25% annual churn rate. Given the fact that it costs 5-10 times more to acquire a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition.

For many incumbent operators, retaining high profitable customers is the number one business goal.

To reduce customer churn, telecom companies need to predict which customers are at high risk of churn.

In this project, you will analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.

Understanding and defining churn There are two main models of payment in the telecom industry - postpaid (customers pay a monthly/annual bill after using the services) and prepaid (customers pay/recharge with a certain amount in advance and then use the services).

In the postpaid model, when customers want to switch to another operator, they usually inform the existing operator to terminate the services, and you directly know that this is an instance of churn.

However, in the prepaid model, customers who want to switch to another network can simply stop using the services without any notice, and it is hard to know whether someone has actually churned or is simply not using the services temporarily (e.g. someone may be on a trip abroad for a month or two and then intend to resume using the services again).

Thus, churn prediction is usually more critical (and non-trivial) for prepaid customers, and the term ‘churn’ should be defined carefully. Also, prepaid is the most common model in India and Southeast Asia, while postpaid is more common in Europe in North America.

This project is based on the Indian and Southeast Asian market.

Definitions of churn There are various ways to define churn, such as:

Revenue-based churn: Customers who have not utilised any revenue-generating facilities such as mobile internet, outgoing calls, SMS etc. over a given period of time. One could also use aggregate metrics such as ‘customers who have generated less than INR 4 per month in total/average/median revenue’.

The main shortcoming of this definition is that there are customers who only receive calls/SMSes from their wage-earning counterparts, i.e. they don’t generate revenue but use the services. For example, many users in rural areas only receive calls from their wage-earning siblings in urban areas.

**Usage-based churn: **Customers who have not done any usage, either incoming or outgoing - in terms of calls, internet etc. over a period of time.

A potential shortcoming of this definition is that when the customer has stopped using the services for a while, it may be too late to take any corrective actions to retain them. For e.g., if you define churn based on a ‘two-months zero usage’ period, predicting churn could be useless since by that time the customer would have already switched to another operator.

In this project, you will use the usage-based definition to define churn.

High-value churn In the Indian and the Southeast Asian market, approximately 80% of revenue comes from the top 20% customers (called high-value customers). Thus, if we can reduce churn of the high-value customers, we will be able to reduce significant revenue leakage.

In this project, you will define high-value customers based on a certain metric (mentioned later below) and predict churn only on high-value customers.

Understanding the business objective and the data The dataset contains customer-level information for a span of four consecutive months - June, July, August and September. The months are encoded as 6, 7, 8 and 9, respectively.

The business objective is to predict the churn in the last (i.e. the ninth) month using the data (features) from the first three months. To do this task well, understanding the typical customer behaviour during churn will be helpful.

Understanding customer behaviour during churn Customers usually do not decide to switch to another competitor instantly, but rather over a period of time (this is especially applicable to high-value customers). In churn prediction, we assume that there are three phases of customer lifecycle :

The ‘good’ phase: In this phase, the customer is happy with the service and behaves as usual.

The ‘action’ phase: The customer experience starts to sore in this phase, for e.g. he/she gets a compelling offer from a competitor, faces unjust charges, becomes unhappy with service quality etc. In this phase, the customer usually shows dif...