Assignment for the Coursera Data Analyst Path (R Markdown Page=

This report analyzes the Boston Housing dataset to identify key drivers of residential property prices using multiple regression techniques in R. Four models are built and compared: - Full Linear Model (R² = 0.733) - Reduced Linear Model (insignificant variables removed) - Quadratic Model (R² = 0.832) - Log-Quadratic Model (best diagnostic behavior) Key findings: - Room count (rm) is the strongest positive predictor - Poverty rate (lstat) is the strongest negative predictor - Air quality (nox) significantly depresses home values - Quadratic model achieves lowest RMSE of 4.49 Tools used: R, ggplot2, broom, knitr, Quarto

Laporan ini menyajikan analisis visual terhadap dataset diamonds untuk mengeksplorasi hubungan antara karakteristik fisik dan kualitas berlian dengan harga. Analisis dilakukan menggunakan pendekatan exploratory data analysis (EDA) melalui visualisasi bivariat, yaitu scatter plot, jitter plot, dan boxplot.

Segmenting Supermarket Customers using Hierarchical Clustering and Segmenting Consumers Recycling Perception & Habits using K-means

This report forecasts US GDP growth rate using 64 years of quarterly Federal Reserve data (1960-2023). Two models are built and compared: - ARIMA — captures autocorrelation structure - ETS — adapts to structural shifts post-COVID Key findings: - GDP growth is mean-reverting (~2.5% long run average) - ARIMA performs better in stable economic periods - ETS adapts faster during volatile periods - Both models significantly outperform naive forecasts Tools used: R, forecast, tseries, Quarto