NYC Agency Social Media Engagement using DL

[abhisheks008]

Deep Learning Simplified Repository (Proposing new issue)

:red_circle: Project Title : NYC Agency Social Media Engagement using DL :red_circle: Aim : The aim of this project is to analyze the media engagement using deep learning methods. :red_circle: Dataset : https://www.kaggle.com/datasets/thedevastator/nyc-agency-social-media-engagement :red_circle: Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

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:red_circle::yellow_circle: Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

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:white_check_mark: To be Mentioned while taking the issue :

• Full name :
• GitHub Profile Link :
• Email ID :
• Participant ID (if applicable):
• Approach for this Project :
• What is your participant role? (Mention the Open Source program)

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[SaketGudimella]

Full name : Saketa Sri Ramacharyulu Gudimella GitHub Profile Link : https://github.com/SaketGudimella Email ID : [email protected]

Approach for this Project : The project focuses on analyzing the social media engagement dataset of NYC agencies during 2011-2012 using three deep learning algorithms: Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Gated Recurrent Units (GRUs). The RNN's is used to find capture sequential dependencies in social media engagement metrics over time, LSTMs is used to uncover long-term patterns within the dataset and GRUs is used to provide another additional prespective dataset's temporal dynamics.

What is your participant role? Contributor - SWOC'24

@abhisheks008 Please assign me this issue under SWOC'24 label

[abhisheks008]

Issue assigned to you @SaketGudimella

[vivekvardhan2810]

Full Name : Vivek Vardhan GitHub Profile Link : https://github.com/vivekvardhan2810 Email ID : [email protected] Participant ID (if applicable): Approach for this Project : This project aims to analyze media engagement of NYC agencies using deep learning methods. It begins with exploratory data analysis to understand dataset patterns and preprocess data by handling missing values and encoding features. Long Short-Term Memory Networks (LSTMs), Transformer-based models (e.g., BERT).

What is your participant role? (Mention the Open Source program): GSSOC 2024 Contributor

@abhisheks008 Please assign me this issue under GSSOC'24 label

[abhisheks008]

Hi @vivekvardhan2810 it will be better if you implement 2-3 deep learning methods/algorithms for this project. Can you put a brief with an updated approach?

[vivekvardhan2810]

here is my updated Approach @abhisheks008

Approach Of this project: This project endeavors to scrutinize social media engagement of NYC agencies employing deep learning techniques. It commences with thorough exploratory data analysis to discern dataset patterns and proceeds to preprocess the data, encompassing tasks such as handling missing values and encoding features. Subsequently, it employs a combination of Long Short-Term Memory Networks (LSTMs), Convolutional Neural Networks (CNNs), and Transformer-based models like BERT to glean insights from the data, enabling predictive analysis of engagement metrics such as likes, shares, and comments. These models are meticulously trained and evaluated to furnish actionable insights for refining the social media strategies of NYC agencies, including optimal content creation, timing, and audience targeting, thus enhancing their digital presence and effectiveness.

[abhisheks008]

Cool. Go ahead and start working on this issue. Assigned to you @vivekvardhan2810

[vivekvardhan2810]

Thank You @abhisheks008 👍