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Projects in Awesome Lists by asaficontact

A curated list of projects in awesome lists by asaficontact .

https://github.com/asaficontact/learning_to_beat_the_random_walk

In this project, I explore various machine learning techniques including Principal Component Analysis (PCA), Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Sentiment Analysis in an effort to predict the directional changes in exchange rates for a list of developed and developing countries.

asset-pricing carry-trade cosine-similarity exchange-rates exchange-rates-forecasting financial-econometrics financial-economics forex forex-prediction latex neural-networks news-articles object-oriented-programming principal-component-analysis sentiment-analysis shinyapps support-vector-machines textblob-sentiment-analysis tf-idf vader-sentiment-analysis

Last synced: 27 Oct 2024

https://github.com/asaficontact/term_spread_combinations

In this project, I show how different combinations and components of term spread have varying shapes, which can be analyzed in order to understand movements in the economy. Calculating term spread dispersion can help us better price risk in the bond market. Term spread combinations have varying power in explaining future movements in macro variable. It shows that the spanning hypothesis of the term spread against a macro variable might hold true depending on the combination and component of term spread that we are taking into consideration. This project provides a mechanism through which we can identify the best combination of a term spread for creating an efficient􏰐 macro 􏰍finance model.

econometrics economic-analysis finance interest-rates latex macroeconomics plotly r recession recession-indicator unemployment yield-curve

Last synced: 27 Oct 2024

https://github.com/asaficontact/project_floodlight

Crisis incidents caused by rebel groups create a negative influence on the political and economic situation of a country. However, information about rebel group activities has always been limited. Sometimes these groups do not take responsibility for their actions, sometimes they falsely claim responsibility for other rebel group’s actions. This has made identifying the rebel group responsible for a crisis incident a significant challenge. Project Floodlight aims to utilize different machine learning techniques to understand and analyze activity patterns of 17 major rebel groups in Asia (including Taliban, Islamic State, and Al Qaeda). It uses classification algorithms such as Random Forest and XGBoost to predict the rebel group responsible for organizing a crisis event based on 14 different characteristics including number of fatalities, location, event type, and actor influenced. The dataset used comes from the Armed Conflict Location & Event Data Project (ACLED) which is a disaggregated data collection, analysis and crisis mapping project. The dataset contains information on more than 78000 incidents caused by rebel groups that took place in Asia from 2017 to 2019. Roughly 48000 of these observations were randomly selected and used to develop and train the model. The final model had an accuracy score of 84% and an F1 Score of 82% on testing dataset of about 30000 new observations that the algorithm had never seen. The project was programmed using Object Oriented Programming in Python in order to make it scalable. Project Floodlight can be further expended to understand other crisis events in Asia and Africa such as protests, riots, or violence against women.

acled asia classification-model crisis grid-search grid-search-hyperparameters matplotlib object-oriented-programming pandas python3 random-forest sklearn xgboost-model

Last synced: 27 Oct 2024

https://github.com/asaficontact/stack_classifier_project

We classified Stack Overflow Python questions from 2008-2016 with Natural Language Processing and Deep Learning. Using Regular Expressions, we removed HTML tags and punctuation. We also utilized spaCy to tokenize, lemmatize and remove stop words. Using Keras, we built a 4 layered artificial neural network with a 20% dropout rate using relu and softmax activation functions. We also utilized the adam optimizer and categorical cross-entropy loss function which classified 11 tags 88% successfully.

cross-entropy-loss deep-learning deep-neural-networks keras lemmatization neural-networks object-oriented-programming pandas python3 regular-expressions relu sklearn spacy spacy-nlp stackoverflow tfidf tokenization

Last synced: 05 Nov 2024

https://github.com/asaficontact/techassist.ai

TechAssist.ai is a Ruby on Rails web app for managing tech projects. Users can sign up, log in, and navigate a dashboard to add, preview, and start projects based on preferences like language and difficulty. The app supports Docker and Heroku for easy development and deployment. Ideal for beginners seeking guided tech project experiences.

Last synced: 05 Nov 2024

https://github.com/asaficontact/rottenpotatoes

This is a assignment project I did for my Engineering Software as a Service (SaaS) class.

Last synced: 05 Nov 2024