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