asaficontact

can_chess_with_hexagons_rl

Reinforcement Learning Exploration for "Can Chess, With Hexagons?"

FX_forecasting_model

Foreign Exchange Forecasting Model created for the paper "Can Interest Rate Factors Explain Rate Fluctuations?"

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.

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.

public-apis

A collective list of free APIs for use in software and web development.

RottenPotatoes

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

spelling_game

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.

Tennis_Shot_Prediction

Predictive Modeling of Tennis Player Poses and Ball Trajectory

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.

asaficontact/FX_forecasting_model

Foreign Exchange Forecasting Model created for the paper "Can Interest Rate Factors Explain Rate Fluctuations?"

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.

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.

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.

asaficontact/can_chess_with_hexagons_rl

Reinforcement Learning Exploration for "Can Chess, With Hexagons?"

asaficontact/public-apis

A collective list of free APIs for use in software and web development.

asaficontact/RottenPotatoes

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

asaficontact/spelling_game

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.

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.

asaficontact/Tennis_Shot_Prediction

Predictive Modeling of Tennis Player Poses and Ball Trajectory