Suman Thapa's repositories
dotnetcorecms
A Simple Content Management System built on .NET Core 2.1
5-day-weather-forecast
Python application which calls the OpenWeatherMap API to get a 5 day weather forecast
Algorithms
A collection of algorithms and data structures
bokeh-stock-market
A live visualization of stock prices and indicators using bokeh
coding-interview-university
A complete computer science study plan to become a software engineer.
examples
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
http-step
Rundeck HTTP Workflow Step Plugin
ignite-essentials-developer-training
[Developer Training] Apache Ignite Essentials - Key Design Principles for Building Data-Intensive Applications
ignite-spring
Spring (not Boot) Apache Ignite client with "mains".
ignite-streaming-monitoring-demo
Stream data to Apache Ignite and monitor the state of the cluster with GridGain Control Center
java-algorithms-implementation
Algorithms and Data Structures implemented in Java
jetson_nano
This repository is a collection of scripts/programs I use to set up the software development environment on my Jetson Nano, TX2, and Xavier NX.
kafka_stock
A financial data processing and visualization platform using Apache Kafka, Apache Cassandra, and Bokeh.
MetarParser
A java program decoding metar and taf
mongodb-currency-analysis
MongoDB 5.0 Time Series - Crypto Currency Analysis
mongodb-grafana
MongoDB plugin for Grafana
requests_pkcs12
Add PKCS#12 support to the Python requests library in a clean way, without monkey patching or temporary files
spring-data-training
Template project for Apache Ignite with Spring Boot and Spring Data Training
spring-kafka-ignite-zeppelin-example
Example how to integrate different data sources like Kafka topics, Csv files into Ignite DB and do analysis with Apache Zeppelin
time-series-forecasting
In this project, we analysed the timeseries for the forecasting of Coal prices in South Africa. We did extensive 'Exploratory Data Analysis' with the help of which we performed data cleaning, processing along with feature selection and extraction. We checked the timeseries for stationarity and seasonality using both graphical and mathematical models. We handled outliers in some years. We believe the reason for the outliers is the economic crisis in 2008. There was a sharp slowdown in demand, and with mining output remaining stubbornly high. Therefore, coal benchmarks fell down. We trained Classical timeseries forecasting method: ARIMA (Auto Regressive Integrated Moving Average), and a Deep Learning method using LSTM along with extensive hyperparameter search using TALOS library. We calculate the metric R^2 (coefficient of determination) regression score function. The R^2 score obtained using ARIMA is 0.96 in comparison to 0.61 which is achieved by LSTM model. For this, not very complex, timeseries ARIMA outperforms the LSTM model as we are trying an overly complex Deep Learning method to fit on a simple timeseries data.
TimeSeriesConvNet
Convolutional neural network for analysis of time series converted to images.
ViMusic
An Android application for streaming music from YouTube Music.