Data Treasure Hunt The Best Facebook Group Scraper.pdf
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Facebook Group Member Scraper is a specialized tool designed to extract data from the membership pools of Facebook groups. They empower users, marketers, researchers, and businesses to delve deeper into these groups, accessing crucial information about their members, demographics, engagement patterns, and more.
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222111Organization N.docx
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1
1
1
Organization Name: Insta-Buy
Insta-Buy is an E-Commerce Multinational American company. It was founded in 2010 and is based in Atlanta, Georgia. It mainly operates with grocery delivery and pick up and it offers services through web application and mobile application to various states in United States. It is one of the major online marketplaces for grocery delivery. The company is valued at $1 billion worth and has partnership with over 150 retailers. It is known for its fresh produce and timely delivery and pickup.
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1. Improve customer engagement and increase revenue
1. Launch promotions that target specific customer group
1. Optimizing prices to generate maximum profits
1. Keep proper inventory and reduce over stalking
1. Minimizing fraud happenings and protecting privacy
1. Provide batter customer service at low cost
1. Analyze data and make decision in real time
TOPICS:
Student: Ahmed
Topic: Bayesian Networks (Predicting Sales In E-commerce Using Bayesian Network Model)
Student: Meet
Topic: Predictive Analysis
Student: Peter
Topic: Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation
Student: Nayeem
Topic: Ensemble Modeling
Student: Shek
Topic: L.Jack & Y.D. Tsai, Using Text Mining of Amazon Reviews to Explore User-Defined Product Highlights and Issues.
Student: Suma
Topic: Deep Neural Networks
REFERENCES:
Olufunke Rebecca Vincent, A. S. (2017). A Cognitive Buying Decision-Making Process in B2B E-Commerce Using Analytic-MLP. Elsevier.
https://www.researchgate.net/publication/319278239_A_Cognitive_Buying_Decision-Making_Process_in_B2B_E-Commerce_Using_Analytic-MLP
Wan, C. C. (2017). Forcasting E-commerce Key Performance Indicators
https://beta.vu.nl/nl/Images/stageverslag-wan_tcm235-867619.pdf
Fienberg, S. (2006). Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation. Statistical Science, .
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relations and interactions among users. Hence, association rules mining techniques are one of
the techniques which can be applied in order to extract more implicit data as input to the
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Analyzing social media data has become increasingly crucial for gaining insights into user behavior, market trends, and overall public sentiment. Advanced techniques in social media data analysis involve utilizing sophisticated tools and methodologies to extract meaningful information.
Lessons in Social Media Marketing 2024
Conquer the Algorithm and Feed Your Brand in 2024:
Tired of social media feeling like a bottomless pit for your content?
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Build communities that connect: ❤️ Cultivate a loyal following who are hungry for your brand's unique flavor.
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It's time to ditch the bland social media blandness and serve up a feast of success! In this ebook, you'll find:
Actionable strategies for every platform: From TikTok's trendy bites to Instagram's photogenic plates, we've got the recipe for every taste.
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222111Organization N.docx
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Organization Name: Insta-Buy
Insta-Buy is an E-Commerce Multinational American company. It was founded in 2010 and is based in Atlanta, Georgia. It mainly operates with grocery delivery and pick up and it offers services through web application and mobile application to various states in United States. It is one of the major online marketplaces for grocery delivery. The company is valued at $1 billion worth and has partnership with over 150 retailers. It is known for its fresh produce and timely delivery and pickup.
Predictive Analysis at Insta-Buy:
The predictive analytics is termed as what is likely to happen in the future. The predictive analytics is based on statistical and data mining technique. The aim of this technique is to predict the future of the project such as what would be the customer reaction on project, financial need, etc. In developing predictive analytical application, a number of techniques are used such as classification algorithms. The classification techniques are logistic regression, decision tree models and neural network. Clustering algorithms are used to segment customers in different groups which helps to target specific promotions to them. To estimate the relationship between different purchasing behavior, association mining technique is used (Mehra, 2014). As an example, for any product on Amazon.com results in the retailer also suggesting similar products that a customer might be interested in. Predictive analytics can be used in E-commerce to solve the following problems
1. Improve customer engagement and increase revenue
1. Launch promotions that target specific customer group
1. Optimizing prices to generate maximum profits
1. Keep proper inventory and reduce over stalking
1. Minimizing fraud happenings and protecting privacy
1. Provide batter customer service at low cost
1. Analyze data and make decision in real time
TOPICS:
Student: Ahmed
Topic: Bayesian Networks (Predicting Sales In E-commerce Using Bayesian Network Model)
Student: Meet
Topic: Predictive Analysis
Student: Peter
Topic: Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation
Student: Nayeem
Topic: Ensemble Modeling
Student: Shek
Topic: L.Jack & Y.D. Tsai, Using Text Mining of Amazon Reviews to Explore User-Defined Product Highlights and Issues.
Student: Suma
Topic: Deep Neural Networks
REFERENCES:
Olufunke Rebecca Vincent, A. S. (2017). A Cognitive Buying Decision-Making Process in B2B E-Commerce Using Analytic-MLP. Elsevier.
https://www.researchgate.net/publication/319278239_A_Cognitive_Buying_Decision-Making_Process_in_B2B_E-Commerce_Using_Analytic-MLP
Wan, C. C. (2017). Forcasting E-commerce Key Performance Indicators
https://beta.vu.nl/nl/Images/stageverslag-wan_tcm235-867619.pdf
Fienberg, S. (2006). Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation. Statistical Science, .
Presentation big data and social media final_video
The document discusses the challenges and opportunities of analyzing big data from social media. It notes that social media generates the largest record of human activity but making sense of the unstructured data is a challenge. It provides examples of how companies use social media data for applications like credit risk assessment and personalized recommendations. The document also discusses privacy and ethical issues with social media data mining, and best practices for social media marketers to leverage big data insights.
How to Safely Scrape Data from Social Media Platforms and News Websites.pptx
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This document provides an overview of recommender systems. It discusses how recommender systems aim to help users find items online that match their interests. It describes two main approaches for recommender systems - collaborative filtering and content-based filtering. Collaborative filtering looks at users' past behaviors and items to find similarities between users and make recommendations. Content-based filtering uses item attributes and properties to recommend similar items to users. The document also discusses challenges with existing recommender systems and how different techniques can be combined in hybrid systems.
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