{"id":24584487,"url":"https://github.com/harsh-git98/brain-dead-hackathon","last_synced_at":"2025-03-17T17:27:09.489Z","repository":{"id":231913458,"uuid":"783019660","full_name":"Harsh-git98/Brain-dead-hackathon","owner":"Harsh-git98","description":null,"archived":false,"fork":false,"pushed_at":"2024-06-08T20:29:37.000Z","size":254,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-24T04:56:12.749Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Harsh-git98.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2024-04-06T18:01:36.000Z","updated_at":"2024-06-08T20:29:40.000Z","dependencies_parsed_at":"2024-04-06T19:28:23.586Z","dependency_job_id":"05663937-ba75-4715-a01c-dd65afcee0a0","html_url":"https://github.com/Harsh-git98/Brain-dead-hackathon","commit_stats":null,"previous_names":["harsh-git98/brain-dead-hackathon"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Harsh-git98%2FBrain-dead-hackathon","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Harsh-git98%2FBrain-dead-hackathon/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Harsh-git98%2FBrain-dead-hackathon/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Harsh-git98%2FBrain-dead-hackathon/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Harsh-git98","download_url":"https://codeload.github.com/Harsh-git98/Brain-dead-hackathon/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244076757,"owners_count":20394169,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-01-24T04:56:14.329Z","updated_at":"2025-03-17T17:27:09.463Z","avatar_url":"https://github.com/Harsh-git98.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Brain Dead Hackathon Submission Report\n\n## Author:\n- Harsh Ranjan\n- Vanshika Kothari\n- Sayan Roy\n- Aaratrika Sarkar\n- Harsh Raj Gupta\n\n## Proposal: Analysis and Prediction of Rice Production using ARIMA and LSTM Models\n\n### Problem Statement\nIn this challenge, participants are tasked with predicting the production of rice on a state-wise or union territory-wise basis. The dataset provided spans from the agricultural sessions of 2004-2005 to 2022-2023, detailing the quantity of rice produced annually.\n\n### Proposal Overview\nThe prediction of rice production on a state-wise or union territory-wise basis is vital for agricultural planning and policymaking. In this proposal, we outline our approach to analyze the dataset spanning from 2004-2005 to 2022-2023 and predict rice production using two different models: ARIMA (AutoRegressive Integrated Moving Average) and LSTM (Long Short-Term Memory). Our goal is to derive meaningful insights from the data and provide accurate predictions for the next five years.\n\n### Dataset Analysis\n#### Overview\nThe dataset comprises three distinct tables, each offering insightful perspectives on rice production spanning from 2004-2005 to 2022-2023. Within Table 1 and Table 2, comprehensive state-wise data delineates the intricate nuances of rice production dynamics over the years.\n\n#### Analysis Goals\nOur analysis delves deeper to determine the rate of production growth or decline for each state/union territory. By scrutinizing the annual production trends against historical data, we can discern not only the magnitude but also the trajectory of change in rice production across different regions.\n\n#### Data Visualization\nWe utilize bar charts, pie charts, and line plots to comprehensively analyze state-wise production variations over the years.\n\n### ARIMA Model\n#### Preprocessing\nWe ensure the reliability of our analysis by prioritizing the attainment of stationarity within the time series data.\n\n#### Model Selection\nWe employ the ARIMA model and conduct hyperparameter tuning to achieve the best possible fit to the data.\n\n#### Training and Evaluation\nWe train the ARIMA model using historical data and subject it to rigorous evaluation using metrics such as MAE and RMSE.\n\n#### Prediction\nWe make predictions for the next five years using the trained ARIMA model.\n\n### LSTM Model\n#### Data Preparation\nWe transform the dataset into sequences of fixed length to prepare it for training the LSTM model.\n\n#### Model Architecture\nWe carefully design an LSTM architecture considering factors such as the number of layers, units, and dropout rates.\n\n#### Training and Validation\nWe train the LSTM model using historical data and validate its performance on a separate test set.\n\n#### Prediction\nWe make predictions for the next five years using the trained LSTM model.\n\n## Comparative Analysis\nWe conduct a comprehensive comparative analysis of the ARIMA and LSTM models for forecasting state-wise rice production.\n\n## Conclusion\nOur proposed approach aims to provide accurate forecasts for agricultural planning and decision-making, contributing to improving rice production strategies and addressing challenges in agricultural sustainability.\n\n---\n\n# Developing a Multimodal Model for Detecting Harmful Internet Memes using CLIP + CNET Architecture\n\n## Introduction\nThe proliferation of internet memes, often combining images and text, has become a significant cultural phenomenon. In this proposal, we outline our approach to develop a novel multimodal machine learning model for classifying harmful internet memes using the CLIP (Contrastive Language-Image Pre-training) model in conjunction with a custom convolutional neural network (CNet) architecture.\n\n## Dataset Description\nWe utilize the \"BrainDead Multimodal Data for Hateful Meme\" dataset provided for the competition, consisting of 10,000 data points, each representing an internet meme.\n\n## Proposed Approach\nWe outline our approach, including preprocessing, CLIP model integration, CNet architecture design, fusion, and classification.\n\n## Comparative Analysis\nWe compare the performance of our proposed model with similar datasets A and B, utilizing performance metrics and insights gained from the comparison.\n\n## Conclusion\nOur proposed multimodal model aims to achieve superior performance compared to existing benchmark models while minimizing computation power requirements, contributing to the advancement of meme moderation systems.\n\n---\n\n## Credits\n- Vanshika Kothari\n- Sayan Roy\n- Harsh Raj Gupta\n- Harsh Ranjan\n- Aaratrika Sarkar\n\nWe extend our sincere gratitude to all team members for their exceptional contributions to our ML model report. Each team member's dedication, expertise, and collaborative spirit were truly commendable.\n\n## References\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fharsh-git98%2Fbrain-dead-hackathon","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fharsh-git98%2Fbrain-dead-hackathon","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fharsh-git98%2Fbrain-dead-hackathon/lists"}