This analytical report delves into the factors influencing graduation rates across higher education institutions in 2023, utilizing a comprehensive dataset from the Department of Education comprising 6,352 rows and 78 columns. The dataset encompasses variables such as tuition fees, geographical location, SAT averages, admission rates, and financial aid statistics. The focus of the analysis is to apply Ridge and LASSO regression techniques to build linear and logistic models for predicting completion rates. By implementing regularization techniques, the report aims to prevent overfitting and enhance model performance. Key aspects include identifying and removing outliers using Mahalanobis Distance and Local Outlier Factor methods, estimating optimal lambda values, and comparing the performance of the Ridge and LASSO models. This exploration provides valuable insights into the effectiveness of each method and contributes to a deeper understanding of the dynamics affecting graduation rates in higher education.

This report details the creation of a robust Customer Relationship Management (CRM) system designed to enhance customer engagement and retention for Cafecito, a thriving chain of boutique coffee shops in New England. Through an exploratory analysis of a dataset comprising 25,000 customer records, the report outlines the rationale behind the CRM data structure, which includes three primary tables: Customer Table, Transaction Table, and Coupon & Offers Table. By merging these tables into a consolidated dataset, Cafecito aims to gain a deeper understanding of its customer base, analyzing purchasing behaviors and preferences to inform targeted marketing strategies. This framework will ultimately empower Cafecito to deliver personalized experiences that resonate with its diverse clientele, enriching the community and coffee culture.

This report analyzes the Redfin listing dataset for Maine and New Hampshire, exploring the connections between various numerical and categorical variables and home prices. With insights from 3,015 recent home sales, the analysis investigates factors such as property type, location, and home characteristics, including size and age. Through descriptive analysis and regression modeling, the report addresses key questions about significant differences in home prices between selected locations and the relationships between home prices and property features. The findings aim to inform stakeholders and contribute to a better understanding of real estate trends and community well-being.

This report provides a comprehensive analysis of health-related data sourced from the Behavioral Risk Factor Surveillance System (BRFSS). By leveraging statistical techniques and visualizations, we explore key health dynamics and trends across various demographics. The analysis highlights patterns related to health behaviors, chronic conditions, and access to healthcare services, offering valuable insights into public health challenges. Interactive graphs and visualizations enhance the understanding of complex relationships within the data, making this report a useful resource for researchers, policymakers, and anyone interested in health analytics. Dive into the findings to uncover critical health insights that can inform decision-making and health initiatives.