Sales Data Visualization and Future Predictions Using Power BI and Machine Learning
Bibliographic record
Abstract
In this era of digitalization, data are produced at every step of our lives. These data are important in any business to analyse and make future predictions to grow and expand. Several tools are available in the market to analyse, visualize, and predict based on data. In this research, we use Power BI to visualize sales data and several Machine Learning models to make future sales predictions. By merging Power BI's exceptional visualization capabilities with Machine Learning models, it gives a comprehensive method for exploring historical sales trends and predicting future paths. These capabilities enable companies to pull useful insights from historical datasets to help inform decisions and drive maximum sales performance. From Power BI, any manager or entrepreneur can comfortably visualize historic sales data. Employing different Machine Learning models, namely, Random Forest, XGBoost, ARIMA, helps us find the best model that can be used for sales prediction in the future. This increases business intelligence and helps increase the revenue of any business in the digital landscape.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".