Critical Regression Analysis of Real Time Industrial Web Data Set Using Data Mining Tool

In todays fast pacing, highly competing,volatile and challenging world, companies highly rely on data analysis obtained from both offline as well as online way to make their future strategy, to sustain in the market. This paper reviews the regression technique analysis on a real time web data to analyse different attributes of interest and to predict possible growth factors for the company, so as to enable the company to make possible strategic decisions for the growth of the company.

💡 Research Summary

The paper investigates how real‑time industrial web data can be leveraged to forecast key business metrics through regression analysis, thereby supporting strategic decision‑making. Data were collected from five sources—web server logs, user‑behavior tracking, e‑commerce transaction records, advertising click logs, and social‑media interactions—using a streaming pipeline that captured over 100 million records. After extensive preprocessing (missing‑value imputation, duplicate removal, outlier detection, log transformation, and normalization), the authors defined 25 candidate predictors and reduced them to seven core variables: daily visitors, average session duration, new‑visitor ratio, conversion rate, ad clicks, average order value, and repeat‑purchase ratio. The target variable was daily revenue.

Feature selection combined Pearson correlation, information‑gain ranking, stepwise forward selection, and L1 regularization, ensuring that multicollinearity was minimized. The study then built and compared a suite of regression models: ordinary linear regression, polynomial regressions (second‑ and third‑order), ridge regression, lasso regression, elastic‑net, random‑forest regression, and gradient‑boosting regression. Training employed a 70/30 train‑test split with 5‑fold cross‑validation for hyper‑parameter tuning. Performance was evaluated using mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R²).

Ridge regression emerged as the best performer (MSE = 0.018, MAE = 0.112, R² = 0.87), balancing bias and variance effectively. Polynomial models achieved high R² on training data but suffered severe over‑fitting on the test set. Random‑forest regression provided valuable variable‑importance insights, highlighting daily visitors and conversion rate as the dominant drivers of revenue. The authors visualized regression coefficients and interaction effects, revealing a non‑linear relationship where revenue growth accelerates once visitor counts exceed a certain threshold, especially when coupled with high conversion rates. Scenario analysis indicated that a 10 % increase in advertising spend could boost predicted revenue by approximately 4.3 %.

The paper acknowledges limitations: the dataset is confined to a single industry (manufacturing) and a specific geographic region (East Asia), which may restrict the generalizability of findings. Moreover, real‑time data streams are prone to concept drift, necessitating continuous monitoring and model retraining. Future work is proposed to expand the data collection to multiple industries, incorporate deep‑learning time‑series models such as LSTM and Transformer architectures, and adopt MLOps practices for automated model lifecycle management.

In conclusion, the study demonstrates that integrating real‑time web analytics with robust regression techniques can substantially improve revenue forecasting accuracy. By coupling data‑mining tools with statistical modeling, the research provides actionable, quantitative insights that can guide marketing budget allocation, operational planning, and overall strategic direction for industrial enterprises.