Optimize Frontend Performance with Multi-User Analytics Dashboard

Optimize Frontend Performance with Multi-User Analytics DashboardOptimize Frontend Performance with Multi-User Analytics Dashboard

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Kunal Prabhakar

• Mar 31

A production-style multi-dashboard analytics platform monitoring frontend performance metrics (load time, API latency, error rates) for multi-user environments. (RBAC)

Completed work

Chart.js Next.js Vercel Frontend Development

Đình Phước Trần

• Nov 8

which option is better for homestay website?

Frontend Development AI Development React

3 voted

100%

0 voted

3 votes

Closed

Kapil Sharma

• Nov 10

'Solution 1' is more practical.

Mahammadali Mammadov

• Nov 7

End-to-end retail analytics pipeline that turns raw, messy CSV exports into business decisions, built in Python and SQL and runnable with one command.

What it does:

Cleans and validates 6 relational tables, fixes 585 data-quality issues and logs every action

Loads a SQLite warehouse with keys, constraints and window-function SQL

Customer analytics: RFM segmentation, cohort retention, CAC / LTV by channel, product Pareto, market-basket analysis

Churn prediction model validated on future data (ROC-AUC 0.71, no data leakage)

6-month revenue forecast, best of 3 models by backtest (4.2% MAPE)

Auto-generated Excel report (live formulas, native charts) and an interactive Streamlit dashboard

18 automated tests

Data Analysis Data Visualization Statistics Matplotlib pandas Python

Eros C.

• Nov 7

Logging every cleaning action is the part I like most: when a number in the report looks strange, you can follow it back to the fix that changed it. With 585 issues, how did you decide which ones the script fixes alone and which ones it only reports?