AI-POWERED CRYPTO DASHBOARD

AI-powered Crypto Analysis Dashboard

COMPANY

ShauryaGupta

ROLE

Data Scientist

EXPERTISE

Data Science & Mahine Learning

YEAR

2025

Project Description

An interactive Streamlit dashboard that provides real-time cryptocurrency trend analysis and predictive forecasts using CoinGecko API data.

Timeline

February 2025 (4-week sprint: data exploration, model development, UI prototyping, and deployment).

Background

With the rapid growth and volatility of cryptocurrency markets, non-technical users lacked an accessible tool to visualize trends and make data-driven decisions.

Process

Starting with user needs, I sketched low-fidelity wireframes to map out data-exploration flows and conducted informal surveys to validate feature priorities. I then iterated on mid- and high-fidelity prototypes in Figma, gathering peer feedback to refine chart types and interaction patterns. This iterative planning ensured that each visualization and control element aligned with real-world decision-making tasks.

Research & Planning

• Assessed common user needs through informal surveys and explored available open APIs (CoinGecko) for data reliability.
• Defined key metrics (price history, volume, market cap) and sketched low-fidelity wireframes to outline data visualization flows.

Design & Prototyping

I crafted the dashboard’s UI in Streamlit to balance clarity and interactivity, organizing key metrics (price history, volume, market cap) into distinct, scrollable sections and embedding Matplotlib charts with adjustable date-range sliders. The layout prioritizes rapid insight discovery, using clean typography and intuitive control placement to guide users through trend analysis without overwhelming them.

Development & Implementation

Leveraging Python’s data stack, I ingested over 5,000 data points from the CoinGecko API and performed cleaning and transformation with pandas and NumPy . I explored multiple regression algorithms in scikit-learn, ultimately training a model that delivered ~90% accuracy in short-term price forecasts. Finally, I integrated these components into a responsive Streamlit app and deployed it on Streamlit Cloud, enabling accessible, no-setup access for stakeholders.

Solution

Built an automated pipeline processing 5,000+ daily crypto data, trained ML models with ~90% forecast accuracy, and integrated everything into an interactive Streamlit dashboard deployed on Streamlit Cloud.

Data Pipeline & Management

Automated daily ingestion, cleaning, and normalization of 5,000+ cryptocurrency data points using pandas and NumPy—ensuring accurate, up-to-date metrics for price, volume, and market cap.

Predictive Modeling & Analytics

Engineered features (rolling averages, momentum indicators) and trained scikit-learn regression models achieving ~90% short-term forecast accuracy, surfacing actionable insights directly in the dashboard.

Interactive Dashboard & Deployment

Developed a Streamlit app with Matplotlib visualizations and dynamic date-range controls, then deployed on Streamlit Cloud for seamless, no-setup access by stakeholders.

Results

Here, the outcomes and achievements of the project are highlighted, including user feedback, adoption rates, and industry recognition.

Accessible Insights

Enabled non-technical users to explore real-time cryptocurrency trends and forecasts via an intuitive Streamlit interface.

Accurate Forecasts

Delivered short-term price predictions with ~90% validation accuracy, empowering users to make data-driven decisions.

Seamless Adoption

Deployed on Streamlit Cloud for instant, no-install access—facilitating broad stakeholder use and rapid feedback loops.