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Case study · Personal project

Data Trend Dashboard

Analysis & visualization · personal project

An interactive Streamlit dashboard that ingests any CSV, cleans it, and surfaces trends, rolling averages and IQR outliers — with exportable reports for non-technical readers.

Type
Personal project
Role
Solo
Focus
Data cleaning, statistics, reporting

streamlit · data trend dashboard

Mock · synthetic data
Datasample.csv60 rows · 4 columnsRolling window7Outlier methodIQR × 1.5ExportExcelPDFPower BITrend analysismean116.6rows60outliers3.3%raw7-pt rolling averageIQR outlier
Illustrative mock-up with synthetic data.

The problem

Raw CSV exports are messy, and most people who need the findings won’t open a notebook. The goal was a tool that goes from a raw file to a shareable report.

What I built

  • Ingest and clean

    Upload any CSV; date columns are detected automatically and the data is cleaned and preprocessed with Pandas.

  • Trends and outliers

    Line charts with configurable rolling averages, IQR outlier detection with visual flagging, a correlation heatmap and categorical breakdowns.

  • Summary statistics

    Automated mean, standard deviation, min, max, skew, kurtosis and outlier percentage.

  • Reports people can open

    Exports to CSV, a three-sheet Excel workbook, a Power BI-ready package with DAX measures, or a PDF report with charts embedded.

Stack

Analysis
PythonPandasNumPy
UI & charts
StreamlitPlotly
Export
OpenPyXLReportLabKaleido

Result

  • Findings can be shared as Excel, PDF or a Power BI package with people who will never open the dashboard.
  • Summary statistics and outlier flags generated automatically for any uploaded dataset.