In this project, I utilized the 2019 Survey of Consumer Finances dataset to explore small business households using clustering & PCA. Revealed unique subgroups, financial insights, and interactive data visualization. Valuable for policymakers & researchers. Uncover the potential of small businesses! You can read about the project here.
This project involves developing a robust data-driven model leveraging machine learning techniques to predict the likelihood of bankruptcy for companies. By analyzing various financial indicators and utilizing advanced algorithms, the project offers insights into identifying early warning signs and assessing bankruptcy risks. This project contributes to proactive decision-making, risk mitigation, and financial stability for stakeholders in the business and financial sectors. You can read about the project here.
This project involved building a classification model to predict severe damage in buildings during the 2015 Nepal earthquake. The project utilized Python, SQL, and machine learning algorithms such as logistic regression and decision trees. The aim of the project was to provide insights for disaster management and mitigation.You can read about the project here.
In this project, I work with air quality data from openAfrica, one of Africa's largest open data platforms. I upload the data to a MongoDB database and query using Python. Then I use Pandas and other Python libraries to build an autoregressive time series model to predict PM 2.5 readings throughout the day in 2018. You can read about the project here.
In this project, I use Selenium to scrape real estate data from realtor.ca. Then I use Pandas and other Python libraries to process, analyze and build a machine learning model to predict house prices in Toronto. You can read about the project here.
In this project, I use Google sheets to clean, analyse and create an interactive sales dashboard for the House of Electronics store. In addition, I used insights from my analysis to provide three (3) recommendations that would drive growth for the store. You can read about the project here.
In this project, I use R in Rstudio to analyze Airbnb listings in Gauteng, South Africa as a proxy for listings in lagos, Nigeria to inform decision making. You can read about the project here.
In this project, I use SQL to clean and explore a fitbit dataset from Kaggle to gain insight into how non-bellabeat customers use smart devices. You can read about the project here.
In this project, I use R in Rstudio to analyze the last 12 months of Cyclistic trip data to glean insights that could drive company growth. You can read about the project here.
This project involves examing and transforming raw Nashville housing data using SQL Server to make it more usable for exploration.
Tableau Dashboards for projects on COVID 19, Fitbit user insights, Airbnb case study, and New Year's eve resolutions.
In this project we take raw global COVID-19 data and explore it in SQL Server to get insights for visualization.