belly-button-biodiversity-dashboard

Belly Button Biodiversity Dashboard is an open-source interactive dashboard that visualises the Belly Button Biodiversity dataset. Built with JavaScript, D3.js, Plotly.js, HTML, and CSS, the dashboard features include a dropdown menu, horizontal bar chart, bubble chart, demographic information display.

credit-risk-classification

The aim of this project is to use the logistic regression mode as a binary classifier to analyse credit card risk. The recommended model helps to predict the high-risk cases. The accuracy, precision, and recall metrics are used to evaluate this model performance.

crowdfunding_etl

The goal of this project is to build an ETL pipeline using Python, Pandas, Python dictionary methods to extract and transform the data. Four CSV files will be created and they will be used to create an ERD and a table schema. Finally, the CSV file data will be uploaded into a Postgres database.

cryptoclustering

This project applies K-means algorithm to group cryptocurrencies based on 24-hour and 7-day price changes. It also investigates the impact of dimensionality reduction using PCA on clustering outcomes.

datavisualizationrepository

Repository for course work for UWA Viz bootcamp

deep-learning-challenge

This project uses deep learning to solve a classification problem. The dataset was preprocessed and a neural network model was optimized to achieve the target performance. Various techniques were tried to improve the model, demonstrating the power of deep learning models for classification problems.

global-significant-earthquake-analysis

Performed comprehensive Exploratory Data Analysis (EDA) on Global Significant Earthquake data to uncover patterns, trends, and insights.

hawaii-climate-analysis-surfsup-with_sqlalchemy

The analysis in this project aims to provide insight into the climate patterns of Honolulu in Hawaii and inform decisions regarding the best time to visit and what activities to plan.

home_sales

This project analyses home sales data using PySpark SQL. It involves creating a temporary table, running queries, and performing caching and partitioning. The final step involves uncaching and verifying the temporary table.

legacy_employee_database_analysis

pandas-challenge

Module 4 challenge

perth-restaurant-explorer-mapping-insights-analysing-trends

In this project, we looked at Yelp data about restaurants and bars in Perth and performed exploratory data analysis to determine relationships between some different variables. An interactive map is also created giving user the chance to choose their desired criteria from a list.

predicting-aids-progression-with-data-insights

The purpose of this analysis is to create a binary classification model using different machine learning techniques to predict if an individual with HIV symptoms will be infected with AIDs after receiving a particular treatment after 20 days. The performances for all the five models in this project are compared at the end.

python-challenge

Module 3 challenge

shivab

shivabajelan

squamous_cell_carcinoma_treatment_analysis

Module 5 challenge

uk_food_hygiene_rating_analysis_using_mongodb

The goal is to help the editors of a food magazine, Eat Safe, Love, to evaluate the data and assist their journalists and food critics in deciding where to focus future articles. The project aims to provide insights into the ratings data to identify establishments that meet the magazine's criteria for featuring in their articles.

usgs_earthquake_visualisation

USGS Earthquake Visualisation is an open-source project that provides an interactive map to visualise earthquake data collected by the USGS, highlighting the relationship between tectonic plates and seismic activity. Built with JavaScript, Leaflet.js, D3.js, HTML, and CSS, the project is available on GitHub under the MIT License.

vba-challenge

Module 2 challenge, VBA scripting

weather-and-vacation-analysis

Module 6 challengeThis project involved using Python and an API to investigate weather trends near the equator by collecting and analyzing weather data. The analysis helped to draw conclusions and provide insights into the factors affecting weather trends in this region.

web_scraping_mars_new_and_weather_data_summary

I used BeautifulSoup and automated browsing to extract information about Mars from two different sources. In Part 1, I scraped titles and preview text from Mars news articles, while in Part 2, I scraped and analysed Mars weather data to gain insights into the planet's climate patterns.