Simple Linear Regression and basic EDA

Exploratory Data Analysis (EDA): Bank Customer Churn Objective The goal of this analysis is to understand the key patterns and drivers behind customer churn in a banking context. Churn — when a customer leaves the bank — can be extremely costly, so uncovering early warning signs is crucial. This EDA serves as the foundation before building any predictive machine learning models. Key Focus Areas Framing the Business Problem: Reducing churn = retaining valuable customers = higher lifetime value. Data Distributions: Analysis of numerical variables (e.g., balance, credit score, age, tenure) to identify skewness, outliers, and transformation needs. Churn Trends by Geography and Demographics: Deep dive into country-level churn (Germany vs France vs Spain), and patterns by gender, age, and tenure. Feature Relationships: Scatter plots, correlation matrix, and churn ratios to explore how variables interact and contribute to churn risk. Handling Imbalanced Data: Churn is typically a minority class — analysis accounts for the skew and explores preprocessing ideas for modeling. Tools Used R & R Markdown tidyverse, ggplot2, DataExplorer, caret Interactive visualizations and summary statistics Key Insights Tenure = 1 year is the most volatile group with the highest churn rate. German customers show significantly higher churn than other countries. Balance and credit score distributions reveal potential high-risk segments. Certain variables are uniformly distributed, offering low predictive power. Class imbalance confirmed — churners are a minority class (~20%), requiring resampling or class-weighted models in future steps. Next Steps Data cleaning and preprocessing (e.g., scaling, encoding) Feature engineering Classification modeling using logistic regression, tree-based methods, and ensemble models This analysis is the first step in building a robust churn prediction model. Proper EDA ensures smarter feature selection, better model accuracy, and ultimately smarter business decisions.

This project analyzes the famous Titanic dataset to investigate how survival rates were affected by passenger class (socioeconomic status) and gender. Using R programming and statistical methods, we clean and explore the data, visualize survival patterns, and perform hypothesis tests to determine if these factors significantly influenced a passenger's chance of survival.

Este estudio realiza un análisis estadístico y espacial de los cultivos sustitutos establecidos en Colombia entre 2007 y 2018 como parte de las estrategias de sustitución voluntaria de hoja de coca. A partir de información agrícola, socioeconómica y geográfica, se examinan las tendencias temporales de siembra y producción, así como la distribución espacial de los cultivos alternativos implementados en los territorios con presencia histórica de economías ilícitas. El análisis incorpora técnicas de estadística descriptiva, modelos de dependencia espacial, medidas de autocorrelación global y local, y visualizaciones geográficas para identificar patrones de concentración, expansión o rezago en la adopción de cultivos sustitutos. Estos resultados permiten caracterizar las dinámicas territoriales asociadas a la sustitución, evaluar la efectividad espacialmente diferenciada de las iniciativas y aportar evidencia para orientar la toma de decisiones en políticas públicas rurales y programas de desarrollo alternativo.

.

For an international sample of firm-year observations around the mandatory adoption of IFRS in 2005, we examine the pre- and post-IFRS average stock illiquidity of firms in countries that are subject to IFRS adoption versus those that are not. We also tackle issues surrounding parallel trends.