This analysis explores customer data from a mall dataset, focusing on the relationship between demographic factors (such as age, income, and gender) and spending behavior. Using exploratory data analysis and K-means clustering, the analysis segments customers into distinct groups, providing insights into potential opportunities for targeting high-spending demographics and engaging income-rich but low-spending shoppers.

This project analyzes Lending Club loan data from 2007 to 2010 to predict whether borrowers will fully repay their loans using a binomial regression model. The goal is to identify key factors influencing loan repayment outcomes.

This dataset was kindly made available by the Stanford Open Policing Project, it includes stop data from Rhode Island, covering the period from 2005 through 2015. I found the dataset on Kaggle and used the following inspirational questions to explore this data set. See the menu above to toggle between tabs.

The dataset is one of the historical sales of supermarket company which has recorded in 3 different branches for 3 months data.

I've collected approximately 20 years of monthly closing data to analyze the returns of $SPY.

Using credit card data, I will use a logistic regression model to predict whether cardholders in a test sample got approved or denied.

An exploratory analysis of data collected between the 2015-16 and the 2018-19 season.

I obtained California housing data from Kaggle to create an exploratory analysis and a predictive model.

I obtained the following dataset that contains 100+ attributes for over 18,000 players from FIFA 20. I’ve used this data to conduct an exploratory analysis of the data.

Exploring Height Differences Between Fathers and Sons"

I am analyzing my personal portfolio, exploring the stocks that performed well and the ones that didn’t. Using financial data from a calendar year, what is the optimal asset allocation percentages among the top stocks.

Investigating the relationship between a runner's weight and their mile run time.

This is a simple data set of US superstore from 2014-2018, obtained from Kaggle. The column names reveal what type of information is revealed in the data set. Using high level metrics we see how successful the company has been in generating sales and the total profit earned over the past four years. I will analyze specific attributes on a high level and won’t examine the attributes on an annual basis, with the objective to explore recomendations the company can implement to earn 15% gross profit over the next four years.

Analyzing twitter activity related to the Coachella Music Festival

Over the past year I’ve practiced my SQL skills and have created a series of SQL projects that answers specific questions, providing insight into the data that it is exploring.

Taking data uploaded by Sebastian Sauer and using it to create a model to predict shoe sizes.

As an auditor, this concept really connected to me and I thought I would conduct an analysis on an financial statement balance.