Analisis Pengaruh Performa Pengiriman terhadap Rating Pelanggan Menggunakan Algoritma Regresi Linear pada Dataset E-Commerce
DOI:
https://doi.org/10.57255/diaml.v1i1.1863Keywords:
Linear Regression, Customer Rating, Delivery Performance, e-Commerce, Machine LearningAbstract
Customer satisfaction is a critical indicator of e-commerce platform success, and customer ratings are often associated with delivery performance. This study aims to analyze the influence of delivery performance, represented by the delivery_days variable, along with other transaction attributes on customer_rating using the Linear Regression algorithm. The dataset used is a synthetic e-commerce dataset comprising 5000 transactions with 12 attributes, obtained from Kaggle, containing no missing values or duplicates. The research stages include data wrangling, exploratory data analysis (EDA), feature engineering (extracting order_month and is_weekend from order_date), categorical encoding using one-hot encoding, train-test splitting (80:20), and modeling with Linear Regression through a ColumnTransformer pipeline. Model evaluation produced an MAE of 1.0276, MSE of 1.3840, RMSE of 1.1764, and R² of -0.0007. Correlation analysis revealed a very weak relationship between delivery_days and customer_rating (r=-0.006), and the regression coefficient for delivery_days was -0.0061, indicating that delivery performance does not have a meaningful effect on customer rating in this dataset. The negative R² value indicates that the linear regression model fails to adequately explain the variability of customer ratings, suggesting that ratings in this dataset are influenced by factors beyond the available transactional attributes
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