This project analyzes a real-world health insurance dataset to determine whether men are charged more than women. Using descriptive statistics, regression models, and visualizations, it examines how factors like age, BMI, smoking status, number of children, and region affect insurance costs and clarifies the role of gender in predicting charges.

Análisis exploratorio simple de un dataset tomado de Kaggle (https://www.kaggle.com/datasets/adilshamim8/social-media-addiction-vs-relationships/data). En ella se exploran múltiples variables cualitativas y cuantitativas de estudiantes de diversos países y grupos.

This is my first Data Analysis Project. I did it as part of Google Data Analytics course. There are three documents, Prepare and Process Data, Analysis, and Finding and Recommendation. This document contain key findings and recommendations only. Please see other two for data prepare, processing and complete analysis.

This is my first Data Analysis Project. I did it as part of Google Data Analytics course. There are three documents, Prepare and Process Data, Analysis, and Finding and Recommendation. This document contain data analysis and visualization only. Please see other two for data prepare, processing and recommendations.

This is my first Data Analysis Project. I did it as part of Google Data Analytics course. There are three documents, Prepare and Process Data, Analysis, and Finding and Recommendation. This document contain Prepare and Process Data only. Please see other two for complete analysis, visualization, and recommendations.

This project investigates the determinants of newborn birth weight using a series of linear regression models. Starting with simple linear predictors, we explored the inclusion of interaction terms and non-linear effects to improve model fit and predictive accuracy. Key predictors include maternal age, number of previous pregnancies, gestational age, newborn length, head circumference, and sex.