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How to build a speech-to-query GPT Financial app using your own data - https://lnkd.in/gJTB-C3G
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everything you can do now in the app thanks to artificial intelligence
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Actively Seeking 2024 Full Time Position | Data Analyst | Embedded System | Power BI | Tableau | SQL | Python | R | MS in Computer Engineering - Graduate Student at UT Dallas.
Day 55 of #75HardDataAnalyticsChallenge: Google Play Store Data Set EDA I recently performed an exploratory data analysis on the Google Play Store dataset, examining various aspects of app categories, ratings, reviews, content ratings, installation counts, app types, and pricing. Here's a summary of my analysis: I loaded the dataset containing information about Google Play Store applications and conducted an initial data inspection, checking for missing values and data types. Throughout my exploratory data analysis, I discovered some valuable insights. For example, the dataset covers various app categories, with some categories having a higher number of apps than others. Additionally, most apps have high ratings, indicating overall satisfaction among users. I also identified the top 10 most reviewed apps based on the total number of reviews. These top-reviewed apps have a significant impact on user engagement and feedback. Content ratings suggest that apps target diverse age groups, catering to different audiences. Furthermore, free apps dominate the Google Play Store, with a smaller proportion of paid apps. In terms of installation counts, there is significant variability among apps, with some apps having significantly higher installations than others. I found that there seems to be a positive correlation between installation counts and ratings, with higher-rated apps generally having more installations. Finally, I examined the distribution of ratings for paid apps, providing insights into the quality of paid apps about their prices. Paid apps exhibit a wide range of ratings, indicating variability in user satisfaction levels among paid offerings. Check out the link below to learn more about my analysis. #DataAnalytics #GooglePlayStore #EDA #AppDevelopment #MobileApps #AppMarket #DataVisualization #DataInsights #DataAnalysis #DataScience #AppTrends #AppEcosystem #AppRatings #AppReviews #AppInstallations #MarketAnalysis #TechTrends #AppMonetization #UserEngagement #UserFeedback #AppCategories #AppPerformance #AppQuality #AppPricing https://www.k
Google Play Store Data Set EDA
kaggle.com
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New year, new ML solutions. Shed the time-consuming work of manually annotating Data in 5 simple steps with AmniSphere, coming this year Q1! 1. Catalog: Support for Multiple Catalogs via CSV 2. Collect: Collect your Items/Objects via our Mobile App 3. Amnitation: ML Automated Data Annotation, byproduct Synthetic Data 4. Datasets: Organize and optimize your datasets your way to build a Runtime Model for your use case 5. Build 98-99% accurate Models you can use anywhere. #AutomatedAnnotation #NoManualAnnotation #Amniscient
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VPS is the Future of GPS #gps #vps #vpsserver #gpstracking #gpstrackingsystem #gpstracker #gpsdevice #gpstrackingdevice
Google Visual Positioning System... ! #innovation •-•-•-•-• ▪︎ Learn to Build a Financial Analytics App using OpenAI & Langchain. Register for FREE👉 https://lnkd.in/e48-XxD6
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Curious about using Copilot to interact with your data in model-driven apps? Then this guide is for you. The feature is still in preview, but I recommend checking it out on a test environment. Let me know what you think about it
5 step guide to enabling, and using Copilot for your data, in your model-driven apps
http://powerappsviking.com
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AppSheet unlocks the untapped power of Google Workspace for businesses of all shapes sizes to build no-coding-required apps that provide additional business value. This video provides a digestible overview: https://lnkd.in/eBRfrvtz
How to build no-code AI powered apps for Google Workspace with AppSheet
https://www.youtube.com/
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Director, Editorial Data and Analytics at Foundry (formerly IDG Communications, still part of IDG). Award-winning data scientist & journalist.
The {jsmodule} #rstats 📦 features RStudio add-ins and Shiny apps to analyze data. It was designed for medical research - but its “basic statistics” option is also useful for general data exploration. By Kim Jinseob & others Docs: https://lnkd.in/egwwipPt Online Shiny app: https://lnkd.in/egz3b_NX #dataviz #EDA
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