Interactive Construction Investment Dashboard
Budget: $250 – $750 USD
I have a collection of historical CSV files that track how construction‐related investment in Japan has shifted over the long term. I now need a clean, professional Streamlit dashboard that turns those raw tables into instantly understandable insights.
Here is what I’m aiming for:
• Data handling
– Load, merge and tidy multiple CSV files with Pandas, handling missing values or differing date formats so everything lines up on a single time axis.
• Visual storytelling
– Produce Plotly line and bar charts that clearly show spending trends through time and across any categories present in the data.
– Each chart must redraw instantly when a user changes a filter.
• Interactivity
– In-app controls (date range sliders, multiselects, etc.) that let the viewer filter the dataset on the fly and watch the charts update.
Acceptance criteria
1. Streamlit app runs with a single command and opens in the browser without errors.
2. All CSV files are ingested automatically—no manual tweaks needed.
3. Filters update every chart simultaneously and within a second on an average laptop.
4. Code is commented and accompanied by a short README explaining setup, file structure and how to add new CSVs later.
Hand off the finished .py file(s), a requirements.txt, and the README, and I’ll handle deployment on my own server.
Here is what I’m aiming for:
• Data handling
– Load, merge and tidy multiple CSV files with Pandas, handling missing values or differing date formats so everything lines up on a single time axis.
• Visual storytelling
– Produce Plotly line and bar charts that clearly show spending trends through time and across any categories present in the data.
– Each chart must redraw instantly when a user changes a filter.
• Interactivity
– In-app controls (date range sliders, multiselects, etc.) that let the viewer filter the dataset on the fly and watch the charts update.
Acceptance criteria
1. Streamlit app runs with a single command and opens in the browser without errors.
2. All CSV files are ingested automatically—no manual tweaks needed.
3. Filters update every chart simultaneously and within a second on an average laptop.
4. Code is commented and accompanied by a short README explaining setup, file structure and how to add new CSVs later.
Hand off the finished .py file(s), a requirements.txt, and the README, and I’ll handle deployment on my own server.