Predictive Fashion Trend Analytics Tool
Budget: $250 – $750 USD
I need an end-to-end predictive analytics tool that will help me see tomorrow’s fashion trends before they hit the streets. The engine must ingest historical sales figures, live social-media chatter, and runway show data, then merge those findings with computer-vision analysis of colors, textures, and patterns pulled from images and video.
Core experience I’m counting on you to bring:
• Fashion-specific predictive modeling and time-series forecasting
• Data-visualization skills that turn raw numbers into clear, bite-size insights designers can act on
• Deployment of scalable, cloud-hosted web apps with a companion mobile interface (iOS/Android) so teams can check the dashboard anywhere
Scope of work
1. Data pipeline – build flexible connectors (API, CSV upload or similar) so I can keep adding new data sets without code changes.
2. Feature engineering & model training – combine quantitative signals with visual cues to flag emerging color, texture and design directions.
3. Interactive dashboard – intuitive drill-downs, heatmaps, and timeline projections; highlight confidence intervals for each forecast.
4. Mobile view – streamlined version of the same insights, optimized for quick swipes and taps.
5. Documentation & hand-off – clear setup notes plus a short Loom or screen-record walkthrough.
Acceptance criteria
• All three data categories (historical sales, social media, runway shows) successfully ingested and updated on demand.
• Visual analysis accurately tags at least the top 10 dominant colors, textures and patterns per season.
• Forecast accuracy benchmarked against a held-out test set, with results and methodology included in the final report.
• Web dashboard loads in under 3 seconds; mobile app passes both iOS and Android store review guidelines.
Tech stack is open, but I regularly work with Python, TensorFlow, scikit-learn, and React, so staying in that ecosystem will make future maintenance easier.
Send over a brief outline of how you’d tackle data ingestion, model selection, and UI design, plus links to any similar fashion or retail analytics projects you’ve delivered. I’m ready to move quickly once I see the right fit.
Core experience I’m counting on you to bring:
• Fashion-specific predictive modeling and time-series forecasting
• Data-visualization skills that turn raw numbers into clear, bite-size insights designers can act on
• Deployment of scalable, cloud-hosted web apps with a companion mobile interface (iOS/Android) so teams can check the dashboard anywhere
Scope of work
1. Data pipeline – build flexible connectors (API, CSV upload or similar) so I can keep adding new data sets without code changes.
2. Feature engineering & model training – combine quantitative signals with visual cues to flag emerging color, texture and design directions.
3. Interactive dashboard – intuitive drill-downs, heatmaps, and timeline projections; highlight confidence intervals for each forecast.
4. Mobile view – streamlined version of the same insights, optimized for quick swipes and taps.
5. Documentation & hand-off – clear setup notes plus a short Loom or screen-record walkthrough.
Acceptance criteria
• All three data categories (historical sales, social media, runway shows) successfully ingested and updated on demand.
• Visual analysis accurately tags at least the top 10 dominant colors, textures and patterns per season.
• Forecast accuracy benchmarked against a held-out test set, with results and methodology included in the final report.
• Web dashboard loads in under 3 seconds; mobile app passes both iOS and Android store review guidelines.
Tech stack is open, but I regularly work with Python, TensorFlow, scikit-learn, and React, so staying in that ecosystem will make future maintenance easier.
Send over a brief outline of how you’d tackle data ingestion, model selection, and UI design, plus links to any similar fashion or retail analytics projects you’ve delivered. I’m ready to move quickly once I see the right fit.