project analyzes NYC shooting data to identify seasonal patterns and correlate them with biopsychosocial factors (recession, job loss, housing instability. Removed years 2020-2023 as they were deemed confounding variables

in this analysis I decomposed the time series components and found that there was plenty of seasonality and little to no trend I then identified as well as removed outliers from there selected the 250 best-selling stores and ran a 60-week forecast followed by a 60-week forecast of the 250 worst selling stores

Maven Toys Mexico City September 2023 performance dashboard

here i did a statistical analysis using Poisson Regression to look for some connection between Covid-19 death and possible comorbidities

this is a dashboard focusing on the overall business of Addidas sales as well as reports focused on Addidas sales from each individual retailer

My first Dashboard using bikeshare data between 2023 and 2024

I used the customer_churn_tbl data set. This is a data set that looks at Churn, subscription tenure, monthly and total charges, in addition to dependents, gender, SeniorCitizen, Partner, Dependents, PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies, Contract, PaperlessBilling and PaymentMethod To analyze the outlook of churn: I used Kaplan Meier and Cox Regression.