Pinned Repositories
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
The Crowdfunding-Analysis with Excel project analyses 1,000 crowdfunding projects and concludes that the US, theater, film and video, and music industries have the most campaigns, with a success rate of 50-60%. The dataset lacks some key data points such as gender and age of backers and distribution of campaigns across different US states.
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.
Flight_Insights_Delays_and_Cancelations
Flight Insights is a Power BI project analysing 2,000,000 commercial flights from major US airports to uncover patterns in flight delays and cancellations. The project leverages a structured business intelligence workflow, including data transformation, modelling, and visualisation, to create an interactive dashboard to bring our data to life.
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.
ShivaB
Shivabajelan's Repositories
Shivabajelan/ShivaB
Shivabajelan/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.
Shivabajelan/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.
Shivabajelan/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.
Shivabajelan/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.
Shivabajelan/DataVisualizationRepository
The Crowdfunding-Analysis with Excel project analyses 1,000 crowdfunding projects and concludes that the US, theater, film and video, and music industries have the most campaigns, with a success rate of 50-60%. The dataset lacks some key data points such as gender and age of backers and distribution of campaigns across different US states.
Shivabajelan/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.
Shivabajelan/Global-Significant-Earthquake-Analysis
Performed comprehensive Exploratory Data Analysis (EDA) on Global Significant Earthquake data to uncover patterns, trends, and insights.
Shivabajelan/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.
Shivabajelan/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.
Shivabajelan/Legacy_Employee_Database_Analysis
The SQL project involved designing tables to hold data from six CSV files, creating a table schema for each file, importing the data into SQL tables, and performing data analysis. The analysis involved answering various questions about the data, such as listing employee information and department managers and ...
Shivabajelan/pandas-challenge
Module 4 challenge
Shivabajelan/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.
Shivabajelan/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.
Shivabajelan/Python-Challenge
Module 3 challenge
Shivabajelan/Shivabajelan
Shivabajelan/Squamous_Cell_Carcinoma_Treatment_Analysis
The study involved treating 249 mice with SCC tumors using a range of drug regimens, including Pymaceuticals' drug of interest, Capomulin. Over 45 days, tumor development was observed and measured to compare the performance of Capomulin against other treatments. My task was to generate tables and figures for the technical report of the study.
Shivabajelan/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.
Shivabajelan/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.
Shivabajelan/VBA-challenge
Module 2 challenge, VBA scripting
Shivabajelan/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.
Shivabajelan/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.