The number of Apple iPhone unit sales dramatically increased between 2007 and 2024. Indeed, in 2007, when the iPhone was first introduced, Apple shipped around **** million smartphones. By 2024, this number reached over ***** million units. The newest models and iPhone’s lasting popularity Apple has ventured into its 17th smartphone generation with its Phone ** lineup, which, released in September 2025, includes the **, ** Plus, ** Pro and Pro Max. Powered by the A19 bionic chip and running on iOS **, these models present improved displays, cameras, and functionalities. On the one hand, such features come, however, with hefty price tags, namely, an average of ***** U.S. dollars. On the other hand, they contribute to making Apple among the leading smartphone vendors worldwide, along with Samsung and Xiaomi. In the first quarter of 2024, Samsung shipped over ** million smartphones, while Apple recorded shipments of roughly ** million units. Success of Apple’s other products Apart from the iPhone, which is Apple’s most profitable product, Apple is also the inventor of other heavy-weight players in the consumer electronics market. The Mac computer and the iPad, like the iPhone, are both pioneers in their respective markets and have helped popularize the use of PCs and tablets. The iPad is especially successful, having remained as the largest vendor in the tablet market ever since its debut. The hottest new Apple gadget is undoubtedly the Apple Watch, which is a line of smartwatches that has fitness tracking capabilities and can be integrated via iOS with other Apple products and services. The Apple Watch has also been staying ahead of other smart watch vendors since its initial release and secures around ** percent of the market share as of the latest quarter.

In the fourth quarter of its 2025 fiscal year, Apple generated around ***billion U.S. dollars in revenue from the sales of iPhones. Apple iPhone revenue The Apple iPhone is one of the biggest success stories in the smartphone industry. Since its introduction to the market in 2007, Apple has sold more than *** billion units worldwide. As of the third quarter of 2024, the Apple iPhone’s market share of new smartphone sales was over ** percent. Much of its accomplishments can be attributed to Apple’s ability to keep the product competitive throughout the years, with new releases and updates. Apple iPhone growth The iPhone has shown to be a crucial product for Apple, considering that the iPhone’s share of the company’s total revenue has consistently grown over the years. In the first quarter of 2009, the iPhone sales were responsible for about ********* of Apple’s revenue. In the third quarter of FY 2024, this figure reached a high of roughly ** percent, equating to less than ** billion U.S. dollars in that quarter. In terms of units sold, Apple went from around **** million units in 2010 to about *** million in 2023, but registered a peak in the fourth quarter of 2020 with more than ** million iPhones sold worldwide.

Delve into the vibrant world of iPhone reselling with 'eBay iPhone Pricing Trends 2023', a rich dataset showcasing seller-driven prices for iPhone models like 11 Pro Max, 12 Pro Max, 13 Pro Max, 14 Pro Max, and XR. Ethically compiled, this data captures eBay's bustling market dynamics, offering a unique lens into how sellers price these high-demand Apple products. Ideal for data enthusiasts, this dataset is a gateway to insights on pricing strategy, market demand, and consumer trends. Elevate your market analysis with this authentic, eBay-sourced dataset, a window into the evolving secondary iPhone market. (Tool: Selenium has been used)

Description

This dataset was used to perform the experiments reported in the IJCB2024 paper : "A novel and responsible dataset for presentation attack detection on mobile devices".

The dataset consists of face videos captured using two cameras (main and front) of nine different smartphones : Apple iPhone 12, Apple iPhone 6s, Xiaomi Redmi 6 Pro, Xiaomi Redmi 9A, Samsung Galaxy S9, GooglePixel 3, Samsung Galaxy S8, iPhone 7 Plus, and iPhone 12 Mini. The dataset contains :

Bona-fide face videos: 8400 videos of bona-fide (real, non-attack) faces, with and without hygienic masks. In total, there are 70 identities (data subjects). Each video is 10 seconds long, where for the first 5 seconds the data subject was required to stay still and look at the camera, then for the last 5 seconds the subject was asked to turn their head from one side to the other (such that profile views could be captured). The videos were acquired under different lighting conditions, including normal office lighting, low lighting, and outdoor lateral lighting. The data subjects were consenting volunteers, who were required to be present during two recording sessions, which on average were separated by about three weeks. In each recording session, the volunteers were asked to record a video of their own face using the front (i.e., selfie) camera of each of the five smartphones mentioned earlier. The face data was additionally captured while the data subjects wore plain (not personalised) hygienic masks, to simulate the scenario where face recognition might need to be performed on a masked face (e.g., during a pandemic like COVID-19).

Attacks:

• Print attacks: 4665 video recordings of the data subjects’ face photos printed on two types of paper, matte and glossy, and two types of printers, laser and ink-jet.
• Replay mobile attacks : 11200 video recordings of bona-fide face videos that were replayed to the target smartphone’s camera. Different phones were paired, such that one of the pair was used to replay the bona-fide videos while the other (attacked) phone recorded the videos using its front camera.
• Replay TV attacks : 5600 video recordings of bona-fide face videos that were replayed on a TV screen. The recordings were performed under multiple conditions, including in normal and low lighting, as well as when the recording device was tilted.
• Projector attacks : 2800 video recordings of bona-fide face videos that were projected on a screen. The recordings were performed under multiple conditions, including in normal and low lighting, as well as when the recording device was tilted. Two projection screens were used, white and green.

Reference

If you use this dataset, please cite the following publication:

N. Ramoly, A. Komaty, V. K. Hahn, L. Younes, A. -M. Awal and S. Marcel, "A Novel and Responsible Dataset for Face Presentation Attack Detection on Mobile Devices," 2024 IEEE International Joint Conference on Biometrics (IJCB), Buffalo, NY, USA, 2024, pp. 1-9, doi: 10.1109/IJCB62174.2024.10744500.

Object Tracking on a Monopoly Game Board

Author: Nathan Hoebeke

Supervisors: Maxim Van de Wynckel, Prof. Dr. Beat Signer

About

The goal of this dataset was to track game pieces on the physical game board of Monopoly. We make use of object classification where our training data consists of 100 pictures (taken at an angle) of the game board in order to classify the individual (moving) pieces. The training dataset was on the 9th of April 2023 and the test date recorded on the 7th of May 2023 using an iPhone 13 mini and iPhone 12.

Two participants played a game of Monopoly and each individually took pictures of the current game state after every move. These images were then processed by our application to determine the location of pawns and other game pieces such as the red and green houses.

Raw images are unprocessed but may have minor edits to ensure anonymisation of participants in the background. We used Roboflow to label and train our dataset which is included in this repository.

For more information about our processing and this dataset you can download the full Bachelor thesis here: https://wise.vub.ac.be/thesis/location-tracking-physical-game-board (download link available after embargo at the end of the academic year)

This dataset was published as part of the bachelor thesis: Location Tracking on a Physical Game Board for obtaining the degree of Bachelor in Computer Sciences at the Vrije Universiteit Brussel.

Data

Dataset contents

• model: Trained YOLOv5 model with labels. This dataset can also be found here.
• train: Training data made by the author.
  • raw: Raw pictures of the game board at various states.
    • GAME_2023-04-09_: Images formatted based on the date and time when they were captured.
  • processed: Processed pictures with perspective transformation applied.
    • canvas: (Pre)-processed image.
• test: Test data made by indepedent participants.
  • participant_1: Participant 1 data
    • raw: Raw pictures of the game board taken by the parcipant after every move.
      • GAME_2023-05-07_: Images formatted based on the date and time when they were captured.
    • processed: Processed pictures with perspective transformation applied. Yellow rectangles are included when our own algorithm was able to determine the location.
      • canvas: Processed image.
  • participant_2: Participant 2 data
    • raw: Raw pictures of the game board taken by the parcipant after every move.
      • GAME_2023-05-07_: Images formatted based on the date and time when they were captured.
• README.md: Documentation and information about the dataset.

License

This license applies to the dataset for the game Monopoly. Any artwork or intellectual property from the game that is captured by this dataset is property of Hasbro, Inc.

Copyright 2022-2023 Nathan Hoebeke, Beat Signer, Maxim Van de Wynckel, Vrije Universiteit Brussel

Permission is hereby granted, free of charge, to any person obtaining a copy of this dataset and associated documentation files (the “Dataset”), to deal in the Dataset without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Dataset, and to permit persons to whom the Dataset is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions that make use of the Dataset.

THE DATASET IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATASET OR THE USE OR OTHER DEALINGS IN THE DATASET.

Dataset overview: This dataset includes images of banana bunches collected from various fields on the island of Madeira, Portugal. The dataset includes four different fields through 2022: Câmara de Lobos on July 25th, Lugar de Baixo on May 17th and 31st, Ponto do Sol on May 20th, June 3rd, August 22nd and October 26th, Santo António on June 7th. The dataset includes a total of 400 samples collected by banana harvesting cutters, opinion based on decision made using digital images.

Data collection process: The dataset was gathered in 2022 using a variety of smartphone cameras, which includes Samsung Galaxy A12, Galaxy Note 9, One Plus 9 and Iphone 12. The wide range of cameras used to gather images in various environments has improved the resilience and generalizability of possible machine learning models.

Labels and captions: The bunch of bananas visible in the final images underwent a laborious human labelling process in order to receive the correct caption. The generated label collection is used to compare the images, which could offer crucial baseline information for developing and testing object detection models.

Data usage: The main objective of the study is to evaluate how well banana bunch harvesting decisions made by cutters using digital image inspection compared to those made in the field. This dataset can be used by researchers for tasks including model training, benchmarking, and perform model tuning.

Introduction This dataset was gathered during the Vid2Real online video-based study, which investigates humans’ perception of robots' intelligence in the context of an incidental Human-Robot encounter. The dataset contains participants' questionnaire responses to four video study conditions, namely Baseline, Verbal, Body language, and Body language + Verbal. The videos depict a scenario where a pedestrian incidentally encounters a quadruped robot trying to enter a building. The robot uses verbal commands or body language to try to ask for help from the pedestrian in different study conditions. The differences in the conditions were manipulated using the robot’s verbal and expressive movement functionalities. Dataset Purpose The dataset includes the responses of human subjects about the robots' social intelligence used to validate the hypothesis that robot social intelligence is positively correlated with human compliance in an incidental human-robot encounter context. The video based dataset was also developed to obtain empirical evidence that can be used to design future real-world HRI studies. Dataset Contents Four videos, each corresponding to a study condition. Four sets of Perceived Social Intelligence Scale data. Each set corresponds to one study condition Four sets of compliance likelihood questions, each set include one Likert question and one free-form question One set of Godspeed questionnaire data. One set of Anthropomorphism questionnaire data. A csv file containing the participants demographic data, Likert scale data, and text responses. A data dictionary explaining the meaning of each of the fields in the csv file. Study Conditions There are 4 videos (i.e. study conditions), the video scenarios are as follows. Baseline: The robot walks up to the entrance and waits for the pedestrian to open the door without any additional behaviors. This is also the "control" condition. Verbal: The robot walks up to the entrance, and says ”can you please open the door for me” to the pedestrian while facing the same direction, then waits for the pedestrian to open the door. Body Language: The robot walks up to the entrance, turns its head to look at the pedestrian, then turns its head to face the door, and waits for the pedestrian to open the door. Body Language + Verbal: The robot walks up to the entrance, turns its head to look at the pedestrian, and says ”Can you open the door for me” to the pedestrian, then waits for the pedestrian to open the door. Image showing the Verbal condition. Image showing the Body Language condition. A within-subject design was adopted, and all participants experienced all conditions. The order of the videos, as well as the PSI scales, were randomized. After receiving consent from the participants, they were presented with one video, followed by the PSI questions and the two exploratory questions (compliance likelihood) described above. This set was repeated 4 times, after which the participants would answer their general perceptions of the robot with Godspeed and AMPH questionnaires. Each video was around 20 seconds and the total study time was around 10 minutes. Video as a Study Method A video-based study in human-robot interaction research is a common method for data collection. Videos can easily be distributed via online participant recruiting platforms, and can reach a larger sample than in-person/lab-based studies. Therefore, it is a fast and easy method for data collection for research aiming to obtain empirical evidence. Video Filming The videos were filmed with a first-person point-of-view in order to maximize the alignment of video and real-world settings. The device used for the recording was an iPhone 12 pro, and the videos were shot in 4k 60 fps. For better accessibility, the videos have been converted to lower resolutions. Instruments The questionnaires used in the study include the Perceived Social Intelligence Scale (PSI), Godspeed Questionnaire, and Anthropomorphism Questionnaire (AMPH). In addition to these questionnaires, a 5-point Likert question and a free-text response measuring human compliance were added for the purpose of the video-based study. Participant demographic data was also collected. Questionnaire items are attached as part of this dataset. Human Subjects For the purpose of this project, the participants are recruited through Prolific. Therefore, the participants are users of Prolific. Additionally, they are restricted to people who are currently living in the United States, fluent in English, and have no hearing or visual impairments. No other restrictions were imposed. Among the 385 participants, 194 participants identified as female, and 191 as male, the age ranged from 19 to 75 (M = 38.53, SD = 12.86). Human subjects remained anonymous. Participants were compensated with $4 upon submission approval. This study was reviewed and approved by UT Austin Internal Review Board. Robot The dataset contains data about humans’ perceived...

This dataset simulates customer transactions for various iPhone models in Indonesia, capturing key aspects of consumer behavior, product preferences, and sales dynamics in the local market. The dataset includes four primary tables:

1. Products Data:

Detailed information about 12 iPhone models, including: - Product names - Product categories - Pricing - Stock levels - Product descriptions - Sales factors that vary by product release year - Available storage configurations - Color options

2. Customers Data:

Contains demographic and contact details for 50,000 simulated customers. The attributes include: - Customer ID - Name - Email - Address - Phone number - Age - City of residence (with a skewed distribution towards major cities like Jakarta, Bandung, and Yogyakarta)

3. Transactions Data:

Simulates 100,000 transactions made by customers, including: - Transaction IDs - Transaction dates - Total amounts - Payment methods - Shipping details - Information about customer cities - Product preferences

4. Transaction Details Data:

Contains granular data for individual transaction items, such as: - Product IDs - Quantities - Unit prices - Discounts applied - Total values for each product in every transaction

This data is essential for in-depth analysis and reporting on customer purchasing behavior.

Key Features:

• Products: Detailed information about iPhone models sold in Indonesia, including release year, pricing, and discount percentages.
• Customers: Simulated customer details such as age, city, and email.
• Transactions: A record of customer purchases, including payment methods and shipping options.
• Transaction Details: A detailed breakdown of each item in a transaction, including quantity, price, and discounts applied.

Dataset Use Cases:

• Market Research: Analyzing sales trends, customer preferences, and the impact of discounts.
• Product Preference Analysis: Understanding which iPhone models are preferred by customers based on their age and city of residence.
• Customer Segmentation: Identifying customer segments based on purchasing behavior, age, and location.
• Sales Forecasting: Predicting future sales based on historical transaction data, product release years, and sales factors.

A diverse dataset is crucial for training deep learning models, especially in the context of currency note recognition. Factors such as diverse backgrounds, lighting, orientation, and blur can significantly impact model outcomes. While high-quality scans of different currencies are accessible on collectors' websites, these often lack the variety seen in real-world scenarios. Additionally, publicly available datasets, which primarily feature old Thai currency notes, are limited, containing up to only 1000 images.

Recognizing the scarcity of comprehensive datasets for new Thai currency notes, we curated a collection of 3,600 images spanning five denominations: - 20 baht - 50 baht - 100 baht - 500 baht - 1000 baht

These images depict the notes in various orientations and settings, including different backgrounds and lighting conditions, such as illuminated and dark environments.

We used two iPhone models to capture this diversity: - iPhone 13 Pro Max (12-megapixel, f/1.8 rear camera) - iPhone 12 (12-megapixel, f/1.6 rear camera)

Unique scenarios were also included, such as half-folded notes against contrasting backgrounds. For consistency, the iPhone 12 captured 4032×3024 resolution shots of the 50 and 1000 baht notes, while the iPhone 13 Pro Max was used for the same resolution images of the other denominations. Our data collection team followed clear guidelines to ensure various image captures.

Each denomination class included 720 images. Specifically, the 20 baht note images were captured in various orientations and settings, such as front views with dark, white, and cluttered backgrounds and front views rotated 180 degrees with the same background variations. The same approach was applied to the 50, 100, 500, and 1000 baht notes. Additionally, images of the back of each note, both normal and rotated 180 degrees, and half-folded top and bottom states, were captured under the same diverse background conditions.

The collected images were meticulously examined during data preparation to address inconsistencies in labeling and variations. Images were organized into folders according to the denominations of the new Thai currency. Given that most images were originally captured with an iPhone in HEIC format, they were converted to JPEG using the 'pyheif' Python module.

The data was divided into training and validation subsets with a 70%:30% ratio. There are a total of 2520 images for training and 1080 for validation.

Here's a brief overview of each objective, research question, and type of analysis you can try to perform:

Objectives:

1. Currency Note Recognition: Develop models to classify Thai currency notes accurately.
2. Image Preprocessing Techniques: Investigate the impact of preprocessing on model performance.
3. Robustness to Environmental Variations: Assess model robustness under varying conditions.
4. Real-Time Detection: Explore real-time recognition on mobile devices.
5. Comparative Analysis of Models: Compare performance of different deep learning architectures.
6. Data Augmentation Strategies: Evaluate the effectiveness of data augmentation for better generalization.

Research Questions:

1. Accuracy and Precision: How accurate are different models in classifying Thai currency notes?
2. Impact of Lighting Conditions: How does lighting affect recognition accuracy?
3. Orientation Sensitivity: How does orientation affect model predictions?
4. Background Influence: How do different backgrounds impact classification accuracy?
5. Folded Notes Recognition: How well does the model recognize folded notes?
6. Generalization to Unseen Data: How well do models generalize to new images?

Potential Insights:

1. Optimal Conditions for Recognition: Identify best conditions for highest accuracy.
2. Model Robustness: Determine most robust models.
3. Real-Time Application Viability: Assess feasibility for real-time applications.
4. Best Practices for Data Collection: Provide recommendations for future datasets.

The iPhone is a smartphone made by Apple that combines a computer, iPod, digital camera, and cellular phone into one device with a touchscreen interface. The iPhone runs the iOS operating system, and in 2021 when the iPhone 13 was introduced, it offered up to 1 TB of storage and a 12-megapixel camera. Different users different e-commerce websites like Flipkart, Amazon, meesho, etc, to find their desired products. In this dataset, we will see the iPhone prices, ratings, reviews, and its RAM on Amazon.

This dataset is for beginners who have started their journey in data analytics.

The number of mobile broadband connections in the Philippines was forecast to continuously increase between 2024 and 2029 by in total 18.3 million connections (+20.46 percent). After the ninth consecutive increasing year, the number of connections is estimated to reach 107.69 million connections and therefore a new peak in 2029. Mobile broadband connections include cellular connections with a download speed of at least 256 kbit/s (without satellite or fixed-wireless connections). Cellular Internet-of-Things (IoT) or machine-to-machine (M2M) connections are excluded. 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).Find more key insights for the number of mobile broadband connections in countries like Vietnam and Laos.

****Labeo Rohita (Rohu) Fish Classification****

Introduction:

The Rohu fish, scientifically known as Labeo rohita, is one of the most economically important freshwater fish species in India, particularly in the state of West Bengal. With its significant cultural, nutritional, and economic value, understanding the Rohu fish is crucial for various stakeholders, including fishermen, policymakers, and consumers. This Kaggle writeup provides an in-depth analysis of the Rohu fish, highlighting its scientific characteristics, importance in India, and its specific significance in West Bengal.

Scientific Overview:

The Rohu fish, belonging to the family Cyprinidae, is native to rivers and lakes across South Asia, including India, Bangladesh, Nepal, and Pakistan. It is characterized by its silver-colored body with a slightly arched head and upturned mouth. Rohu is a freshwater species, thriving in rivers, reservoirs, and ponds with moderate water flow and abundant vegetation.

Importance in India:

1. Cultural Significance: Rohu holds cultural significance in Indian cuisine and traditions, being a popular choice for various culinary preparations, especially during festivals and celebrations.
2. Nutritional Value: Rich in protein, vitamins, and essential minerals, Rohu fish is a staple source of nutrition for millions of people across India, contributing to food security and addressing malnutrition concerns.
3. Economic Contribution: The commercial cultivation and trade of Rohu fish contribute significantly to the Indian economy, providing livelihoods for millions of fishermen, aqua culturists, and associated industries.

Significance in West Bengal:

1. Cuisine: In West Bengal, Rohu fish plays a central role in the traditional Bengali cuisine, featuring prominently in dishes such as "Rohu Machher Jhol" (Rohu fish curry) and "Rohu Bhapa" (steamed Rohu fish).
2. Economic Activity: The cultivation and sale of Rohu fish are integral to the economy of West Bengal, with numerous fish farms and fisheries operating across the state, particularly in districts like Hooghly, Nadia, and North 24 Parganas.
3. Cultural Heritage: Rohu fish is deeply embedded in the cultural heritage of West Bengal, with fishing practices and fish-based dishes reflecting the region's rich culinary traditions and social customs.

Datasets Description :

The dataset is essentially a time series dataset. The dataset is created in total 7 days to capture high resolution pictures of fresh and non-fresh eyes and gills. We have used one iPhone 12 to capture the images. Following are the description of the dataset :

1. Training Dataset: Fresh Eyes : 223, Fresh Gills : 613, Non-Fresh Eyes : 1028, Non-Fresh Gills : 1265, Total : 3129, Total Size : 10.08 GB, Resolution : 3024x4032 , Image Type : JPG
2. Test Dataset: Fresh Eyes : 134, Fresh Gills : 80, Non-Fresh Eyes : 257, Non-Fresh Gills : 316, Total : 787, Total Size : 2.49 GB, Resolution : 3024x4032 , Image Type : JPG
3. Total Dataset: 3916 , Total Size: 12.57 GB
4. Dataset Distribution: We have distributed the whole data into 80% as Training data and 20% as test data.

Feature Set :

The gills and eyes of fish are important indicators of freshness, as they provide visual and sensory cues that can help in determining whether a fish is fresh or not.

Gills: Fresh fish have bright red or pink gills, indicating good blood circulation and oxygen exchange. As fish age, the gills start to darken and may turn brown or gray due to a decrease in blood flow and oxygen uptake. Additionally, fresh fish gills should be free of any slime or mucus buildup, which can indicate bacterial growth and spoilage.

Eyes: The eyes of a fresh fish should be clear, bulging, and bright. Cloudy or sunken eyes can be signs of deterioration. Cloudiness in the eyes could indicate dehydration or decay, while sunken eyes suggest dehydration and dehydration may be caused by prolonged storage or improper handling. Also, the eyes should be glossy and not dried out or cloudy.

Classification Algorithms:

Few ML/DL algorithms are mentioned below which can be used for classification for the dataset

Machine Learning:

• Support Vector Machines (SVM)
• Random Forests
• Decision Trees
• K-Nearest Neighbors (KNN)
• Naive Bayes
• Logistic Regression
• Gradient Boosting Machines (GBM)
• AdaBoost

Deep Learning:

• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Long Short-Term Memory (LSTM)
• Gated Recurrent Units (GRUs)

Conclusion:

The Rohu fish, with its scientific significance, cultural importance, and economic relevance, holds a special place in India, particularly in West Bengal. As a vital source of nutrition, livelihood, and cultural heritage, understanding and conserving the Rohu fish species are essential for susta...

I am pursuing a Bachelor degree (Data Science) - 1st year at SITAMS, Chittoor, AP, India.

This is my first contribution to Kaggle. The initial version of the dataset is small. But my aim is bigger. We will collect new data and publish here. As per my exploration, i found that there is shortage of datasets to solve Driver Assistant Use cases such as, - Road sign detection - Pedestrian detection - Vehicle detection - Animal detection - Pothole detection - Speed breaker detection - and Many more https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12260106%2F1b50bb6b2370c1f3f7ca112d906cb6f9%2FADAS_dataset_plan.jpg?generation=1668403342252654&alt=media" alt=""> Our goal is to capture real time data for mentioned use cases in figure. Last one week we collected the following data from our hometown roads.This is our initial start.

Different scenarios that we considered:

• Different type of roads (Highways, town roads, street/village roads)

• Different time (Darkness,Sunny, Rainy, Fog, crowded place and cloudy) https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12260106%2F123951a94abbfa4d3c542306edc1b040%2FScreenshot%202022-11-14%20at%2010.53.47.png?generation=1668403466954584&alt=media" alt="">

• Maintain distance/different angles to capture objects on the road. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12260106%2F50805ad3cd17f2af52ecf1495c2f1d61%2FScreenshot%202022-11-14%20at%2010.54.58.png?generation=1668403533370453&alt=media" alt="">

• Also used 2 different configuration phones to capture images. (iphone 12 and VIVO)

We labelled data using the “labelme” tool. Labels and object coordinate details are in the .json file. There are considered labels, across all categories. 1. animal 2. pedestrian 3. name_board 4. speed_beaker 5. pothole 6. right_hand_curve 7. bridge_ahead 8. left_hand_curve

Context: This dataset contains information on a variety of smartphone models. The data is scraped from the e-commerce website flipkart.com and has been cleaned and processed for analysis. I used Pandas and BeautifulSoup libraries to scrape the data. I scraped data from a page 1 to 41 which was the maximum number that I was able to scrape.

Content: The dataset contains information on over 800 smartphone models from various brands including Apple, Samsung, Xiaomi and more. The dataset includes the following attributes:

Attributes - 1. brand: The brand of the smartphone, such as Samsung, Apple, Xiaomi, etc. 2. model: The name and model number of the smartphone, such as iPhone 12, Samsung Galaxy A33, Redmi Note 10, etc. 3. colour: The colour of the smartphone, such as sandy gold, sunrise blue, etc. 4. original_price: The original price of the smartphone in Indian rupees (INR) before any discounts. 5. discounted_price: The discounted price of the smartphone in INR after any discounts or promotions. 6. ratings: The average rating of the smartphone by customers on the Flipkart website, on a scale of 1 to 5 stars. 7. rating count: The number of ratings given by customers on the Flipkart website for the smartphone. 8. reviews: The text reviews given by customers on the Flipkart website for the smartphone. 9. memory: The amount of RAM memory included in the smartphone measured in gigabytes (GB). 10. storage: The amount of internal storage included in the smartphone measured in gigabytes (GB). 11. processor: The type and speed of the processor included in the smartphone, such as Qualcomm Snapdragon 888, Apple A14 Bionic, etc. 12. rear_camera: The number and specifications of the rear cameras included in the smartphone, such as 48 MP + 12 MP + 5 MP, etc. 13. front_camera: The number and specifications of the front camera included in the smartphone, such as 20 MP, etc. 14. display_size: The diagonal size of the smartphone screen measured in centimeters (cm). 15. battery_capacity: The capacity of the smartphone battery measured in milliampere-hours (mAh). 16. battery_type: The type of battery included in the smartphone, such as lithium-ion (Li-Ion), lithium-polymer (Li-Po), etc.

Note: Some attributes may be missing for certain smartphone models, depending on the information available on the Flipkart website.

Potential Uses: This dataset can be used for a variety of purposes, including market analysis, product development, and pricing strategy. It could also be used to train machine learning models for predicting smartphone prices or classifying smartphones based on technical specifications.

Limitations: As the data is collected from a single source, it may not include all smartphones in the market. Also, the pricing information may not reflect current market conditions.

Acknowledgment: I would like to thank the Flipkart website ("https://www.flipkart.com/search?q=smartphones") for providing the data used in this dataset.