Conflict in Ethiopia using DL

[abhisheks008]

Deep Learning Simplified Repository (Proposing new issue)

:red_circle: Project Title : Conflict in Ethiopia using DL :red_circle: Aim : The aim of this project is to analyze the dataset using deep learning methods. :red_circle: Dataset : https://www.kaggle.com/datasets/e1iasm/conflict-in-ethiopia-acled-dataset :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. 😎

[whitewolf2000ani]

Full name: Aniket Kumar GitHub Profile Link: https://github.com/whitewolf2000ani Email ID: [email protected] Participant ID (if applicable): NA Approach for this Project: This project aims to provide a comprehensive analysis of political violence in Ethiopia by leveraging the ACLED dataset. The dataset contains detailed information on conflict events, including actors, event types, locations, and fatalities. Through exploratory data analysis (EDA), we will uncover insights into the dynamics of political violence, patterns of actor interactions, and geographical trends within Ethiopia. By examining this data, we seek to better understand the nature and context of political conflicts in the region. My approach Data Preprocessing: Clean and preprocess your data. This involves tasks like handling missing values, encoding categorical variables, scaling numerical features, and splitting the data into training, validation, and test sets. Choose Deep Learning Techniques: Select the deep learning methods. This could include techniques like: Convolutional Neural Networks (CNNs) for analyzing satellite imagery or photos related to conflicts. Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks for analyzing temporal sequences of events, such as conflict occurrences over time. Transformer models for processing textual data like news articles or social media posts related to conflicts. Model Development: Develop and train your deep learning models on the prepared dataset. Experiment with different architectures, hyperparameters, and training strategies to optimize model performance. Evaluation: Evaluate your models using appropriate metrics. For instance, if you're predicting conflict events, metrics like accuracy, precision, recall, and F1-score might be relevant. If you're analyzing text, you might consider metrics like perplexity or BLEU score. Interpret Results: Interpret the results of analysis. What insights can you gain from our models? How well do they perform in predicting or analyzing conflict events? Are there any interesting patterns or trends in the data? Iterate and Improve: Iterate on your models and analysis based on your findings. Refine your approaches, experiment with different data sources or features, and incorporate feedback to improve the accuracy and relevance of our results. Documentation and Reporting: Document our methodology, findings, and conclusions thoroughly. Prepare a report or presentation summarizing your project, including details about your dataset, model architectures, evaluation results, and insights gained. This project will answer the following questions:

1. What are the fatality rates?
2. What kinds of events are the frequent reasons for conflict?
3. Who are the major actors?
4. Which places are most prone to conflict?
5. Which media(s) has been the main source(s) of the conflict news
6. Is there a correlation between specific actor interactions and the level of violence in conflict events?
7. What is the impact of civilian targeting in conflict events, and are there any trends related to this?
8. How do different administrative divisions (ADMIN1, ADMIN2, ADMIN3) relate to the frequency and nature of conflict events? I am registered as a contributor for GSSOC24 and would like to contribute to the problem statement.

[abhisheks008]

Too good! Issue assigned to you @whitewolf2000ani