cogentdom

Acoustics_FTB

Algorithms

AnimationDemo

architecture-fullstack

This project scrapes data of stocks and feeds them down a pipeline that trains an ML model and displays a dashboard using Streamlit. This application is then containerized using docker and deployed to a virtual machine on AWS using EC2. This instance is then accessed from a custom domain name that is being hosted on a Cloudflare DNS server. The major problem within this project was the complex nature resulting from a high number of composition files. Due to the uniqueness of the deployment I found no insight from research and ultimately performed brute force through experimentation to reach a solution.

audio-signal-processing

bioinformatics-btree

This project will read the Human Genome by encoding the sequence. Sequenced using the 4 different organic chemicals, known as bases, the Human Genome is about 2.87 billion bases long. Utilizing the B-tree data structure as a solution for memory constraints we will also be able to search the sequence with a O(log n) capability.

Docker_VPS_App

This project scrapes data of stocks and feeds them down a pipeline that trains an ML model and displays it as a dashboard using Streamlit. This Streamlit app is then containerized using docker and deployed to a virtual machine on AWS using EC2. This EC2 instance is then accessed from a custom domain name that is being hosted on a Cloudflare DNS server.

forecast-product-demand

Harnessing the power of machine learning this project offers large retail stores a more efficient solution to their supply chain by identifying future sales of inventory. Allowing supply chain managers insight that can be used to measure lost sales from shortages along with a means of identifying products with the greatest upward trend; ultimately bolstering the decision-making process when faced with a limited shelf space. The major problem faced with this problem was designing a single model which understood a single product was listed at various indices where each record represents said product at an individual shop. I was able to solve this by slicing my data describing every product and then creating one of these slices for every store; this not only solved the problem but provided predictions on demand for every good at every location.

strategic-financial-insight

A project I completed for the Idaho Policy Institute where I delivered insightful models to bolster their decision-making process to form a strategy which best allocates government resources intended to reduce local crime. There were two main problems I encountered during this project; the first of which was imbalance of data across a twenty-year period. I was able to solve this problem when I identified a pattern showing consistent data collection amongst the variables every five years. The second problem was the quantity of variables within the data far exceeded my ability to manually analyze each individual value. I solved this by creating a pipeline which returned variables that surpassed a set threshold based upon their relevance to the set targets.

TensorAudio

cogentdom/forecast-product-demand

Harnessing the power of machine learning this project offers large retail stores a more efficient solution to their supply chain by identifying future sales of inventory. Allowing supply chain managers insight that can be used to measure lost sales from shortages along with a means of identifying products with the greatest upward trend; ultimately bolstering the decision-making process when faced with a limited shelf space. The major problem faced with this problem was designing a single model which understood a single product was listed at various indices where each record represents said product at an individual shop. I was able to solve this by slicing my data describing every product and then creating one of these slices for every store; this not only solved the problem but provided predictions on demand for every good at every location.

cogentdom/Docker_VPS_App

This project scrapes data of stocks and feeds them down a pipeline that trains an ML model and displays it as a dashboard using Streamlit. This Streamlit app is then containerized using docker and deployed to a virtual machine on AWS using EC2. This EC2 instance is then accessed from a custom domain name that is being hosted on a Cloudflare DNS server.

cogentdom/TensorAudio

cogentdom/Algorithms

cogentdom/AnimationDemo

cogentdom/architecture-fullstack

This project scrapes data of stocks and feeds them down a pipeline that trains an ML model and displays a dashboard using Streamlit. This application is then containerized using docker and deployed to a virtual machine on AWS using EC2. This instance is then accessed from a custom domain name that is being hosted on a Cloudflare DNS server. The major problem within this project was the complex nature resulting from a high number of composition files. Due to the uniqueness of the deployment I found no insight from research and ultimately performed brute force through experimentation to reach a solution.

cogentdom/audio-signal-processing

cogentdom/bioinformatics-btree

This project will read the Human Genome by encoding the sequence. Sequenced using the 4 different organic chemicals, known as bases, the Human Genome is about 2.87 billion bases long. Utilizing the B-tree data structure as a solution for memory constraints we will also be able to search the sequence with a O(log n) capability.

cogentdom/strategic-financial-insight

A project I completed for the Idaho Policy Institute where I delivered insightful models to bolster their decision-making process to form a strategy which best allocates government resources intended to reduce local crime. There were two main problems I encountered during this project; the first of which was imbalance of data across a twenty-year period. I was able to solve this problem when I identified a pattern showing consistent data collection amongst the variables every five years. The second problem was the quantity of variables within the data far exceeded my ability to manually analyze each individual value. I solved this by creating a pipeline which returned variables that surpassed a set threshold based upon their relevance to the set targets.

cogentdom/API-Dashboard

cogentdom/API-Data-Runner

cogentdom/AppModTraining

cogentdom/Bengali-Audio

cogentdom/Cloud_Build

cogentdom/cogentdom

cogentdom/Flask_REST

cogentdom/LocationDemo

cogentdom/mastering-supply-chain

cogentdom/MessageDemo

cogentdom/MusicManipulation

cogentdom/PickerViewDemo

cogentdom/portfolio

cogentdom/Prices_Indices

cogentdom/RR_Diner_Coffee

cogentdom/Sentiment_API

cogentdom/Streamlit

cogentdom/SurveyDataCollector

cogentdom/SwiftDemo2

cogentdom/tf2-for-deep-learning

cogentdom/TFTwalkthrough