Open abhisheks008 opened 5 months ago
Full name: Aniket Kumar GitHub Profile Link: https://github.com/whitewolf2000ani Email ID: kaniket123456789@gmail.com 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:
Too good! Issue assigned to you @whitewolf2000ani
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.
π Follow the Guidelines to Contribute in the Project :
requirements.txt
- This file will contain the required packages/libraries to run the project in other machines.Model
folder, theREADME.md
file must be filled up properly, with proper visualizations and conclusions.:red_circle::yellow_circle: Points to Note :
:white_check_mark: To be Mentioned while taking the issue :
Happy Contributing π
All the best. Enjoy your open source journey ahead. π