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Accelerometer Time-Series Analysis | Python
Processed and analyzed accelerometer sensor data using Pandas and NumPy to extract motion features and visualize multi-axis acceleration patterns. Demonstrated time-series preprocessing and signal interpretation techniques.
https://github.com/bryan781/Accelerometer-Data-Analysis.git
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• Jul 10
Title:
Professional Data Entry Project (Microsoft Excel)
Description:
Built a professional Excel-based Data Entry Project for accurate data collection, organization, and management. The project includes structured spreadsheets, automated calculations, data validation, sorting, filtering, and clean formatting to improve efficiency and reduce errors. Designed for businesses to maintain organized records and streamline daily data management tasks.
Skills Used:
Microsoft Excel
Data Entry
Data Cleaning
Data Validation
Data Formatting
Sorting & Filtering
Formulas (SUM, IF, COUNTIF, VLOOKUP/XLOOKUP)
Spreadsheet Management
Data Organization
Clean structure and a really practical summary dashboard — the Paid/Pending breakdown is a nice touch for order tracking. Good example of turning raw entries into something genuinely usable.
Dataset scale: 1,200 rows analyzed, totaling 1.26M in total price, averaging 1.1K per row (median 823.62), with a max single value of 3.5K and a min of 11.39.
Anomaly detection: The audit panel flags 28 statistical outliers (beyond 2.5 standard deviations), with the most extreme case highlighted by row number, value, and Z-score a nice touch for surfacing data-quality issues rather than just describing the distribution.
Correlation and concentration: It reports a strong positive correlation (r = 0.72) between unit price and total price (expected, but good to confirm), and flags that coupon usage is concentrated FREESHIP accounts for 26.5% of coupon codes, with an HHI score suggesting a lack of diversity there.
Data quality flag: A missing-values warning notes CouponCode is 26% empty, which is a meaningful caveat any coupon-based segmentation should be read with that gap in mind.
Trend behavior: The time series shows a mild downward drift, confirmed quantitatively by a "temporal shift" note (7.2% contraction in average total price between the first and second half of the date range).
Category breakdown: Chair, Printer, and Laptop lead in total value contribution, with Phone trailing fairly even spread across the seven product types rather than one runaway category.
Forecasting module: The standout addition here is the "Advanced Multi-Model Projection Forecast" panel a linear regression trend (y = -0.11x + 1120.22) projecting values forward to a target of 987.17 by 03/07/2025, with a configurable model selector and forecast horizon (periods slider), plus a caveat about 95% confidence intervals and seasonality risk.
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