The potential of using Chat GPT and AI to revolutionize the way we interact with computers, specifically in the field of medical diagnostics. Chat GPT can make conversations between doctors and patients more natural, while AI can analyze vast amounts of patient data to identify trends and estimate a patient’s health. Patients can use Chat GPT to better understand their medical conditions, and both Chat GPT and AI can be used to automate tasks such as scheduling appointments and processing test results. However, there are limitations to using AI, including data bias, complex results, and analysis errors. To reduce errors, it is important to validate findings using various techniques and ensure that data is accurate and up-to-date. Chat GPT also employs security measures to protect patient data privacy and confidentiality.

Monthly dataset tracking topic frequency, keyword volume, and conversation patterns across ChatGPT discussions. Data is normalized on a 0 to 100 scale for easy comparison. Aggregates millions of AI interactions to reveal emerging trends, user interests, and discussion momentum across technology, finance, health, education, and business categories.

This dataset contains a CSV file related to ChatGPT including keywords(chatgpt, chat gpt) #hashtags and @mentions about ChatGPT. OpenAI's conversational AI model. The file includes information on 500,000 tweets. The dataset aims to help understand public opinion, trends, and potential applications of ChatGPT by analyzing tweet volume, sentiment, user engagement, and the influence of key AI events. The dataset offers valuable insights for companies, researchers, and policymakers, allowing them to make informed decisions and shape the future of AI-powered conversational technologies.

Check out my Comprehensive Analysis on this dataset: Medium article "Cracking the ChatGPT Code: A Deep Dive into 500,000 Tweets using Advanced NLP Techniques"

Learn about the collection process in Medium article "Effortlessly Scraping Massive Twitter Data"

This dataset contains all available conversations from chatlogs.net between users and ChatGPT. Version 1 contains all conversations available up to the cutoff date of April 4, 2023. Version 1 contains all conversations available up to the cutoff date of April 20, 2023.

This if the data we used for our analysis

This study aims to explore students' associations with Artificial Intelligence (AI) and how these perceptions have evolved following the release of Chat GPT. A free word association test was conducted with 836 German high school students aged 10–20. Associations were collected before and after the release of Chat GPT, processed, cleaned, and inductively categorized into nine groups: technical association, assistance system, future, human, negative, positive, artificial, others, and no association. In total, 355 distinct terms were mentioned, with “robot” emerging as the most frequently cited, followed by “computer” and “Chat GPT,” indicating a strong connection between AI and technological applications. The release of Chat GPT had a significant impact on students' associations, with a marked increase in mentions of Chat GPT and related assistance systems, such as Siri and Snapchat AI. The results reveal a shift in students' perception of AI-from abstract, futuristic concepts to more immediate, application-based associations. Network analysis further demonstrated how terms were semantically clustered, emphasizing the prominence of assistance systems in students' conceptions. The findings underscore the importance of integrating AI education that fosters both critical reflection and practical understanding of AI, encouraging responsible engagement with the technology. These insights are crucial for shaping the future of AI literacy in schools and universities.

DeepSeek vs. ChatGPT: AI Performance & User Behavior (July 2023 - Feb 2025)

This synthetically generated dataset provides a realistic AI performance comparison between ChatGPT (GPT-4-turbo) and DeepSeek (DeepSeek-Chat 1.5) over a 1.5-year period. With 10,000+ rows, it captures key user interaction metrics, platform performance indicators, and AI response characteristics to analyze trends in accuracy, engagement, and adoption.

Key Features:

• Time-Series Ready – Granular date and time columns for trend and seasonality analysis.
• Comparative AI Analysis – Compare user engagement, retention rates, and response quality.
• User Behavior Insights – Analyze session durations, input text complexity, and user ratings.
• Technical Performance Metrics – Evaluate AI response accuracy and processing speed.
• Data Cleaning Practice – Includes intentionally introduced null values for preprocessing exercises.

Ideal For:

• AI benchmarking and platform performance studies
• Time-series forecasting and trend analysis
• Data preprocessing and feature engineering
• Power BI, SQL, and Python-based analytical dashboards

📜 License: MIT – Free for research, projects, and analysis.

Comparison of Tokens used to run all evaluations in the Artificial Analysis Intelligence Index by Model

Quardo/Turkish-Chat_GPT-4O

  Description

This is a simple dataset generated by OpenAI's GPT-4O (gpt-4o-2024-08-06). The dataset includes various entries created and evaluated by the AI model, providing a unique collection of Turkish chat data for analysis and research.

  Warning

Please note that this dataset may contain errors or inconsistencies as it is fully generated by an AI model. It is highly recommended to check and edit the data before usage, as AI can… See the full description on the dataset page: https://huggingface.co/datasets/Quardo/Turkish-Chat_GPT-4O.

This is a mock dataset from Chat GPT

You are a data analyst hired by GreenFuture Inc., an environmental consultancy firm. GreenFuture Inc. specializes in assessing the environmental impact of businesses in various sectors, focusing on carbon footprint, waste management, and energy efficiency. They've collected data from several companies and want to analyze this data to identify trends, potential areas for improvement, and industry benchmarks.

Suggested Analysis Tasks:

Emission Trends Analysis: Calculate total emissions (CO2, CH4, N2O) per year for all companies. Identify the top 5 companies with the highest CO2 emissions in the latest year available. Sector-wise Energy Use Analysis

Aggregate energy use (Electricity, Fossil Fuels, Renewables) by sector and year: Determine the sector with the highest reliance on fossil fuels versus renewables.

Waste Management Efficiency: Calculate the percentage of waste recycled for each company and find the top 3 companies with the best recycling rates. Analyze trends in waste management practices over the years.

Correlation Analysis: Explore the correlation between energy use types (Electricity, Fossil Fuels, Renewables) and emissions (CO2) to identify patterns.

Actionable Insights: Based on the analysis, suggest actionable insights for companies to reduce their environmental impact. This might involve recommendations for sectors or companies lagging in renewable energy use or those with inefficient waste management practices.

Discover the booming AI Skincare Advisor market! Learn about its projected $2.5 billion valuation by 2033, key growth drivers, leading companies like Reveive and Bioderma, and the challenges ahead. Get insights into personalized skincare, AI technology, and market trends.

Comprehensive comparison of Artificial Analysis Intelligence Index vs. Context Window (Tokens) by Model

The AutoScout Data dataset is a comprehensive collection of information on various vehicles, capturing a wide array of attributes that can be used for analysis in automotive research, market analysis, and machine learning projects related to car sales and valuations. Here is an overview of the dataset columns:

make_model: This column contains the make and model of the vehicle. It is a categorical variable that identifies the brand and specific model of the car.

body_type: This column describes the body type of the vehicle (e.g., sedan, SUV, hatchback). It is a categorical variable representing the shape and style of the car.

price: This column lists the price of the vehicle in the respective currency. It is a numerical variable crucial for market valuation studies.

vat: This column indicates whether the price includes VAT (Value Added Tax). It is a categorical variable, typically showing "Yes" or "No."

km: This column shows the mileage of the vehicle in kilometers. It is a numerical variable that indicates the usage level of the car.

Type: This column describes the type of car, such as "new," "used," or "demonstration." It is a categorical variable.

Fuel: This column specifies the type of fuel the vehicle uses, such as petrol, diesel, electric, etc. It is a categorical variable.

Gears: This column indicates the number of gears the vehicle has. It is a numerical variable.

Comfort_Convenience: This column lists comfort and convenience features of the vehicle, such as air conditioning, heated seats, etc. It is a categorical variable with multiple entries.

Entertainment_Media: This column describes the entertainment and media features available in the car, like Bluetooth, radio, CD player, etc. It is a categorical variable with multiple entries.

Extras: This column details any extra features of the car, which might include things like alloy wheels, roof rails, etc. It is a categorical variable with multiple entries.

Safety_Security: This column includes information on safety and security features of the vehicle, such as airbags, ABS, etc. It is a categorical variable with multiple entries.

age: This column indicates the age of the vehicle in years. It is a numerical variable.

Previous_Owners: This column shows the number of previous owners of the vehicle. It is a numerical variable.

hp_kW: This column lists the horsepower of the vehicle in kilowatts. It is a numerical variable indicative of the car’s power.

Inspection_new: This column indicates whether the vehicle has a new inspection. It is a categorical variable, typically showing "Yes" or "No."

Paint_Type: This column specifies the type of paint of the vehicle, such as metallic, matte, etc. It is a categorical variable.

Upholstery_type: This column describes the type of upholstery in the vehicle, such as leather, fabric, etc. It is a categorical variable.

Gearing_Type: This column indicates the type of gearing system, such as manual, automatic, etc. It is a categorical variable.

Displacement_cc: This column lists the engine displacement in cubic centimeters (cc). It is a numerical variable.

Weight_kg: This column shows the weight of the vehicle in kilograms. It is a numerical variable.

Drive_chain: This column describes the drive chain of the vehicle, such as front-wheel drive, rear-wheel drive, etc. It is a categorical variable.

cons_comb: This column indicates the combined fuel consumption of the vehicle. It is a numerical variable representing fuel efficiency.

Usage and Potential Analyses This dataset can be used for various analyses, including:

• Price Prediction: Building models to predict vehicle prices based on various attributes.
• Market Analysis: Understanding trends in vehicle types, features, and pricing.
• Customer Preferences: Analyzing which features are most common or popular among different types of vehicles.
• Vehicle Performance: Studying the relationship between engine displacement, horsepower, and fuel consumption.

The dataset's rich set of features allows for detailed exploration and insights into the automotive market, providing valuable information for consumers, dealers, and manufacturers alike.

Text generated with Chat-GPT

Acknowlegement

Foto von Alev Takil auf Unsplash