MINI PROJECT_2.pptx
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This document contains information about a web scraping project using Beautiful Soup. It lists the team members and guide and provides an overview of web scraping and how it can be used for data collection, competitive analysis, lead generation, content creation, and market research. It discusses that the idea already exists but the team plans to implement it in an ethical, user-friendly way using AI. It also lists some drawbacks of web scraping like limited performance and legality concerns. Finally, it provides information about another project to create a fire detection system using AI and machine learning by analyzing camera images and sensor data.
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This leads us to the question: If E-commerce and fashion companies need to be agile and consumer-friendly, then why are so many still using the same product catalog management methods that were devised years ago? The manual product classification and data attribution process are only leading to an increased risk of error and time delay affecting the brand reputation. Also, leading to lost sales opportunities due to incomplete or inaccurate product records that don’t really reflect the actual product.
In this webinar, we will discuss how to efficiently manage machine learning projects without tech headaches by plugging in your data and building your models instantly.
What you will learn
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- Know the secret sauce of automating time-intensive, repetitive steps to quickly build models
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Quelles que soient les applications, il ne s'agitpas de prévoir l'avenir mais de réduire l'incertitude en modélisant des probabilités et des scénarios d'évolution. Les technologies sont entrées dans une phase opérationnelle. Les avancées du Big Data dans la modélisation, le machine learning, ou l'algorithmique sémantique apportent désormais la puissance calculatoire qui faisait auparavant défaut pour fouiller les vastes ensembles de données non-structurées disponibles sur le web, les média sociaux et l'internet des objets.
Au-delà des défis en termes de R&D, l'enjeu aujourd'hui est de simplifier l'accès aux approches prédictives pour en démocratiser les usages dans les différents métiers. Des solutions innovantes sont développées pour faciliter la conception de modèles et simplifier le développement d'applications "Web Services" ou "BI Mobile" pour mieux toucher les décideurs. Les modes de distribution en cloud permettent de mutualiser les ressources. Des modèles économiques innovants sont également expérimentés par les fournisseurs de solutions pour réduire les coûts d'accès aux technologies et essaimer dans les entreprises.
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Source : http://forum.gfii.fr/forum/de-la-business-intelligence-au-predictif-grace-aux-big-data
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About the webinar
It’s no secret that a well-organized product catalog becomes extremely crucial as consumers look for a more rich and consistent online experience while E-shopping. Often, the task of digitizing the catalog of the fast-moving and large volume products becomes daunting due to insufficient, erroneous and fragmented data.
This leads us to the question: If E-commerce and fashion companies need to be agile and consumer-friendly, then why are so many still using the same product catalog management methods that were devised years ago? The manual product classification and data attribution process are only leading to an increased risk of error and time delay affecting the brand reputation. Also, leading to lost sales opportunities due to incomplete or inaccurate product records that don’t really reflect the actual product.
In this webinar, we will discuss how to efficiently manage machine learning projects without tech headaches by plugging in your data and building your models instantly.
What you'll learn
- How E-commerce companies are using AI to drive more sales and seamless customer experience
- Know the secret sauce of automating time-intensive, repetitive steps to quickly build models
- Demo: A deeper understanding of the end-to-end machine learning workflow for a fashion product catalog management using Skyl.ai
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Future of Ecommerce: How to Improve the Online Shopping Experience Using Mach...
About the webinar
It’s no secret that a well-organized product catalog becomes extremely crucial as consumers look for a more rich and consistent online experience while E-shopping. Often, the task of digitizing the catalog of the fast-moving and large volume products becomes daunting due to insufficient, erroneous, and fragmented data.
This leads us to the question: If E-commerce and fashion companies need to be agile and consumer-friendly, then why are so many still using the same product catalog management methods that were devised years ago? The manual product classification and data attribution process are only leading to an increased risk of error and time delay affecting the brand reputation. Also, leading to lost sales opportunities due to incomplete or inaccurate product records that don’t really reflect the actual product.
In this webinar, we will discuss how to efficiently manage machine learning projects without tech headaches by plugging in your data and building your models instantly.
What you will learn
- How E-commerce companies are using AI to drive more sales and seamless customer experience
- Know the secret sauce of automating time-intensive, repetitive steps to quickly build models
- Demo: A deeper understanding of the end-to-end machine learning workflow for a fashion product catalog management using Skyl.ai
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How Startups can leverage big data?
Data is being generated at a feverish pace and forward thinking companies are integrating big data and analytics as part of their core strategy from day one. However, it is often hard to sift through the hype around big data and many companies start with only a small subset of data. Can smaller companies benefit from big data efforts? We will discuss several use cases and examples of how startups are using data to optimize their operations, connect with their users, and expand their market.
Modern Data Architectures for Business Insights at Scale
Using AWS to design and build your data architecture has never been easier to gain insights and uncover new opportunities to scale and grow your business. Join this workshop to learn how you can gain insights at scale with the right big data applications.
Azure Machine Learning
A presentation covers how data science is connected to build effective machine learning solutions. How to build end to end solutions in Azure ML. How to build, model, and evaluate algorithms in Azure ML.
Mdd
The document discusses using deep learning for object recognition. It proposes building an app using the ImageNet dataset that can recognize objects with 80% accuracy without internet or heavy processing. The app would continue learning to improve over time and detect multiple objects in an image, providing probability scores if uncertain. The ImageNet dataset is a large visual dataset used for object classification and detection challenges involving millions of images across hundreds of categories.
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MINI PROJECT_2.pptx
- 1. TEAM MEMBERS Stanley Saji George (63) Sajith Sajeev (54) K Karthikeyan (10) Ken Jose (14) GUIDE’S NAME Er. Lini Ickappan TEAM NO: 7
- 3. WEBSCRAPING USING BEAUTIFUL SOUP A Process of extracting data from websites using automated tools or software Data collection: extract large amounts of data from multiple websites quickly and easily. Competitive analysis: help businesses gather information about their competitors, such as pricing, product offerings, and marketing strategies.
- 4. WEBSCRAPING USING BEAUTIFUL SOUP Lead generation: help businesses collect contact information from potential customers, such as email addresses or phone numbers. Content creation: used to gather information for creating content, such as news articles, blog posts, or social media updates. Market research: used to gather data on consumer behavior, such as product preferences, shopping habits, and social media activity.
- 5. WEBSCRAPING IDEA ALREADY EXISTS ? YES DOES CHANGES WE PLAN TO IMPLEMENT • Respect the websites ToS • Use ethical Webscraping techniques • A User-Friendly GUI • AI to enhance the webscraping process • Using a distributed webscraping system
- 6. WEBSCRAPING DRAWBACKS Limited browser support Limited performance Legality concerns HTML inconsistencies Data accuracy
- 8. WEBSCRAPING USING BEAUTIFUL SOUP REQUIREMENTS SOFTWARE • Beautiful soup • PyCharm • Visual Studio Code • Sublime Text • Requests library • Pandas for data manipulation or • Selenium for web automation • A code editor or development environment
- 9. FIRE DETECTION SYSTEM USING AI & ML
- 10. FIRE DETECTION SYSTEM USING AI & ML To detect the presence of fire A combination of image processing and machine learning techniques The system uses a camera to capture real-time images and applies computer vision algorithms to detect fire- related objects and patterns. The system is trained using a dataset of images to identify potential fire hazards accurately. The proposed system can provide an early warning
- 11. FIRE DETECTION SYSTEM IDEA ALREADY EXIST ? DOES YES Existing systems use AI algorithms to analyze various types of data Images from cameras, temperature readings from sensors, and smoke particle measurements, to detect fires in their early stages.
- 12. FIRE DETECTION SYSTEM DRAWBACKS False alarms Limited coverage Delayed response Maintenance issues Our AI-based fire detection systems use more sophisticated algorithms and data sources to detect fires quickly and accurately.
- 13. FIRE DETECTION SYSTEM SOFTWARE REQUIREMENTS HARDWARE OS ML Libraries DBMS Real-time DPS Sensors Cameras Networking Equipments Storage
- 14. THANK YOU